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Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix
Magnetic Resonance in Medicine
Abstract
Relative cerebral blood flow (CBF) and tissue mean transit time (MTT) estimates from bolus-tracking MR perfusion-weighted imaging (PWI) have been shown to be sensitive to delay and dispersion when using singular value decomposition (SVD) with a single measured arterial input function. This study proposes a technique that is made time-shift insensitive by the use of a block-circulant matrix for deconvolution with (oSVD) and without (cSVD) minimization of oscillation of the derived residue function. The performances of these methods are compared with standard SVD (sSVD) in both numerical simulations and in clinically acquired data. An additional index of disturbed hemodynamics (oDelay) is proposed that represents the tracer arrival time difference between the AIF and tissue signal. Results show that PWI estimates from sSVD are weighted by tracer arrival time differences, while those from oSVD and cSVD are not. oSVD also provides estimates that are less sensitive to blood volume compared to cSVD. Using PWI data that can be routinely collected clinically, oSVD shows promise in providing tracer arrival timing-insensitive flow estimates and hence a more specific indicator of ischemic injury. Shift maps can continue to provide a sensitive reflection of disturbed hemodynamics.
... Computed tomography (CT) or magnetic resonance (MR) perfusion with bolus injection of contrast agent, a more frequently used imaging modality, can acquire information on arterial blood arrival time in addition to the traditional circulation parameters, CBF, CBV, and mean transit time (MTT), calculated as the ratio of CBV to CBF [4,5]. In the current clinical setting, arrival time is usually calculated as Tmax, the peak time of the residue function estimated by deconvolution analysis [6][7][8][9]. This Tmax parameter helps estimate the spatial extent of delayed arrival regions, especially in patients with acute stroke [10,11]. ...
... Arterial voxels were automatically extracted from all the voxels. Subsequently, AIFs were used for delay-insensitive deconvolution analysis [6], providing a set of perfusion parametric maps: CBF, CBV, MTT, and Tmax. The parameter of interest in this study is Tmax, the peak time of a residue function derived from deconvolution analysis, which provides information on the tracer arrival timing [6][7][8][9]. ...
... Subsequently, AIFs were used for delay-insensitive deconvolution analysis [6], providing a set of perfusion parametric maps: CBF, CBV, MTT, and Tmax. The parameter of interest in this study is Tmax, the peak time of a residue function derived from deconvolution analysis, which provides information on the tracer arrival timing [6][7][8][9]. ...
Background
Positron emission tomography (PET) with ¹⁵O-gas for quantifying cerebral blood flow (CBF) and oxygen metabolism is the gold standard for assessing hemodynamics in ischemic cerebrovascular disease. However, conventional ¹⁵O-gas PET methods do not provide information on regional arrival timing, a hemodynamic parameter typically measured using computed tomography (CT) perfusion with contrast media. This study demonstrated that ¹⁵O-gas PET with a state-of-the-art clinical PET scanner and optimized analysis can generate arrival time maps. In this retrospective study of ten patients with unilateral stenosis or occlusion of the major arteries, we compared PET-derived arrival time maps with CT perfusion Tmax maps.
Results
In PET with short inhalation of [¹⁵O]-CO2 gases, dynamic images were reconstructed with 2-sec temporal resolution, followed by weighted least-squares fitting of one-tissue compartment models, with or without the contributions from vascular components. PET arrival time maps were visually comparable to CT perfusion Tmax maps regarding the spatial extent of delayed brain regions, with less noise and higher image quality when using the model without the vascular components. Region-of-interest analyses showed good correlations between the two modalities: correlation coefficients of 0.834 for absolute values and 0.718 for ipsilateral-to-contralateral differences, respectively, indicating that ¹⁵O-gas PET can quantitatively measure the arrival time with reasonable accuracy.
Conclusions
The present method generates arrival-time maps with ¹⁵O-gas PET by applying optimized kinetic analysis to dynamic [¹⁵O]-CO2 images acquired using a state-of-the-art, high-sensitivity clinical PET scanner. Additional arrival time information for conventional PET parameters of CBF and oxygen metabolism may facilitate a more comprehensive understanding of the hemodynamic status in cerebrovascular steno-occlusive diseases.
... Circulant SVD (cSVD) and Volterra SVD (vSVD), widely used these days, rely on discretization methods for AIF and dynamic regularization methods that are adapted to DSC-MRI data. 6,[10][11][12][13] However, these methods are still noise-dependent and computationally expensive. ...
... 6,12,13 In case of cSVD, the rank matrix is truncated so that the resulting tissue response function attains an oscillation index of less than 10% while still having the highest number of elements after truncation. 10 In case of vSVD, the rank matrix is modified so that the resulting tissue response function is regularized based on the standard-form Tikhonov L-curve criterion. 11 Both cSVD and vSVD require computationally expensive calculations for each C a and C t pair, which significantly prolongs the processing time. ...
... The tissue response function (TRF) is formally defined in Eqs. (10) and (11). ...
Purpose
To propose the simulation‐based physics‐informed neural network for deconvolution of dynamic susceptibility contrast (DSC) MRI (SPINNED) as an alternative for more robust and accurate deconvolution compared to existing methods.
Methods
The SPINNED method was developed by generating synthetic tissue residue functions and arterial input functions through mathematical simulations and by using them to create synthetic DSC MRI time series. The SPINNED model was trained using these simulated data to learn the underlying physical relation (deconvolution) between the DSC‐MRI time series and the arterial input functions. The accuracy and robustness of the proposed SPINNED method were assessed by comparing it with two common deconvolution methods in DSC MRI data analysis, circulant singular value decomposition, and Volterra singular value decomposition, using both simulation data and real patient data.
Results
The proposed SPINNED method was more accurate than the conventional methods across all SNR levels and showed better robustness against noise in both simulation and real patient data. The SPINNED method also showed much faster processing speed than the conventional methods.
Conclusion
These results support that the proposed SPINNED method can be a good alternative to the existing methods for resolving the deconvolution problem in DSC MRI. The proposed method does not require any separate ground‐truth measurement for training and offers additional benefits of quick processing time and coverage of diverse clinical scenarios. Consequently, it will contribute to more reliable, accurate, and rapid diagnoses in clinical applications compared with the previous methods including those based on supervised learning.
... In numerical implementation, Q(t) and C a (t) are linearly interpolated to a virtual sampling interval of 1 s and zero-padded to twice their length to avoid time aliasing 19 . This formulation is equivalent to circulant convolution in the time domain and therefore considered insensitive to delay in contrast arrival between the artery and the tissue (T0) 19,23 . The flow-scaled IRF can be estimated by the inverse Fourier Transform of the quotient between tissue and arterial TDC frequency spectrums. ...
... Specifically, all combinations of T0 ∈ [0, 1, 2, . . . , 23,24] seconds and MTT ∈ [2, 3, . . . , 23,24] seconds were searched, leading to a total of 575 grid search combinations per tissue TDC. ...
... , 23,24] seconds and MTT ∈ [2, 3, . . . , 23,24] seconds were searched, leading to a total of 575 grid search combinations per tissue TDC. A non-negative linear least squares algorithm 24 solved for physiologicallyconstrained positive values of CBF for each T0 and MTT pair. ...
CTP is an important diagnostic tool in managing patients with acute ischemic stroke, but challenges persist in the agreement of stroke lesion volumes and ischemic core-penumbra mismatch profiles determined with different CTP post-processing software. We investigated a systematic method of calibrating CTP stroke lesion thresholds between deconvolution algorithms using a digital perfusion phantom to improve inter-software agreement of mismatch profiles. Deconvolution-estimated cerebral blood flow (CBF) and Tmax was compared to the phantom ground truth via linear regression for one model-independent and two model-based deconvolution algorithms. Using the clinical standard of model-independent CBF < 30% and Tmax > 6 s as reference thresholds for ischemic core and penumbra, respectively, we determined that model-based CBF < 15% and Tmax > 6 s were the corresponding calibrated thresholds after accounting for quantitative differences revealed at linear regression. Calibrated thresholds were then validated in 63 patients with large vessel stroke by evaluating agreement (concordance and Cohen’s kappa, κ) between the two model-based and model-independent deconvolution methods in determining mismatch profiles used for clinical decision-making. Both model-based deconvolution methods achieved 95% concordance with model-independent assessment and Cohen’s kappa was excellent (κ = 0.87; 95% confidence interval [CI] 0.72–1.00 and κ = 0.86; 95% CI 0.70–1.00). Our systematic method of calibrating CTP stroke lesion thresholds may help harmonize mismatch profiles determined by different software.
... where C t (t) denotes the tissue concentration curve of the ROI located in gray matter, C a (t) is the AIF either corrected using one of the three rescaling criteria described above or without rescaling, ⊗ symbol represents the convolution operator, and R(t) represents the residue impulse response function. Deconvolution of Equation 5.6 to estimate CBF was carried out using the block circulant singular value decomposition method (cSVD) [5,8,93]. The block circulant decomposition method has an advantage of being less sensitive to tracer arrival timing differences. ...
... Tmax is the argument, i.e., t of the maximum of R(t). The deconvolution utilizes a matrix method called the circulant singular deconvolution, which is sensitive to the peak amplitudes of AIF (cSVD) [93]. The changed AIF amplitude used in the cSVD algorithm shifts the maximum of R(t) to a higher time points t which accounts for higher Tmax values. ...
... The present study has shown that the absence of multi-voxel AIF scaling results in inaccurate and untrue CBF values. 93 ...
This thesis describes the research findings of a four-year Dual PhD degree programme between National Tsing Hua University (NTHU), Taiwan and University of Liverpool (UoL), United Kingdom. Under the joint supervision of Prof. Fan-Pei Gloria Yang (NTHU) and Prof. Ke Chen (UoL), research was carried out for two years at the Center of Cognition Sciences, NTHU, and two years at the University of Liverpool in alternate years.
Conventional Computed Tomography (CT) and Magnetic Resonance (MR) imaging are not sufficiently sensitive to evaluate acute stroke [2]. Perfusion-weighted imaging (PWI) is a noninvasive MR/CT technique that infers how blood traverses the brain’s vasculature by assessing various hemodynamic parameters such as cerebral blood volume, cerebral blood flow, mean transit time, and time to peak [3]. In stroke patients, these parameters are used to locate the penumbra and core [4, 5, 2, 3, 6]. Penumbra refers to brain regions that are on the verge of infarction but are still salvageable if reperfused [7, 3, 8]. The infarct core is the tissue that has already infarcted or will infarct regardless of reperfusion [8]. Risk of errors in core and penumbra volume estimations leads to misleading conclusions. Part of the risk stems from using established and more standard software, which employs simple mathematical models that are incapable of delivering the required accuracy and sensitivity [2].
... Although only the default block circulant singular value deconvolution with oscillation index (oSVD) was used in this study, the program offers three more deconvolutional methods, namely singular value decomposition (sSVD, the original method from Ostergaard (26, 27)), block circulant singular value deconvolution with a fixed cutoff (cSVD) (28), and a Tikhonov regularized Fourier deconvolution (29,30). ...
... Restricted perfusion lesions (shown as Regions of Interest, ROI) are delineated on Tmax maps. Tmax is calculated using a regularized deconvolution method, namely oSVD (28), with an automatically detected global Arterial Input Function (AIF). The AIF is the average of all AIF candidates from the slice with the most suitable AIF candidates. ...
Purpose
This study aimed to evaluate the perfomance of Siemens Healthineers’ StrokeSegApp performance in automatically segmenting diffusion and perfusion lesions in patients with acute ischemic stroke and to assess its clinical utility in guiding mechanical thrombectomy decisions.
Methods
This retrospective study used MRI data of acute ischemic stroke patients from the prospective observational single-center 1000Plus study, acquired between September 2008 and June 2013 (clinicaltrials.org; NCT00715533) and manually segmented by radiologists as the ground truth. The performance of the StrokeSegApp was compared against this ground truth using the dice similarity coefficient (DSC) and Bland–Altman plots. The study also evaluated the application’s ability to recommend mechanical thrombectomy based on DEFUSE 2 and 3 trial criteria.
Results
The StrokeSegApp demonstrated a mean DSC of 0.60 (95% CI: 0.57–0.63; n = 241) for diffusion deficit segmentation and 0.80 (95% CI: 0.76–0.85; n = 56) for perfusion deficit segmentation. The mean volume deviation was 0.49 mL for diffusion lesions and −7.69 mL for perfusion lesions. Out of 56 subjects meeting DEFUSE 2/3 criteria in the cohort, it correctly identified mechanical thrombectomy candidates with a sensitivity of 82.1% (95% CI: 63.1–93.9%) and a specificity of 96.4% (95% CI: 81.7–99.9%).
Conclusion
The Siemens Healthineers’ StrokeSegApp provides accurate automated segmentation of ischemic stroke lesions, comparable to human experts as well as similar commercial software, and shows potential as a reliable tool in clinical decision-making for stroke treatment.
... Cerebral blood flow (CBF) maps were calculated by circular deconvolution of tissue concentration curves using a contralesional arterial reference curve. 15 Cerebral blood volume (CBV) was calculated by numeric integration of the tissue concentration curve. Mean transit time (MTT) was the quotient of CBV and CBF. ...
Futile recanalization hampers prognoses for ischemic stroke patients despite successful recanalization therapy. Allegedly, hypertension and reperfusion deficits contribute, but a better understanding is needed of how they interact and mediate disease outcome. We reassessed data from spontaneously hypertensive and normotensive Wistar-Kyoto rats (male, n = 6–7/group) that were subjected to two-hour embolic middle cerebral artery occlusion and thrombolysis in preclinical trials. Serial MRI allowed lesion monitoring and parcellation of regions-of-interest that represented infarcted (core) or recovered (perilesional) tissue. Imaging markers of hemodynamics and blood-brain barrier (BBB) status were related to tissue fate and neurological outcome. Despite comparable ischemic severity during occlusion between groups, hypertensive rats temporarily developed larger lesions after recanalization, with permanently aggravated vasogenic edema and BBB permeability. One day post-stroke, cerebral blood flow (CBF) was variably restored, but blood transit times were consistently prolonged in hypertensives. Compared to the core, perilesional CBF was normo-to-hyperperfused in both groups, yet this pattern reversed after seven days. Volumes of hypo- and hyperperfusion developed irrespective of strain, differentially associating with final infarct volume and behavioral outcome. Incomplete reperfusion and cerebral injury after thrombolysis were augmented in hypertensive rats. One day after thrombolysis, fractional volumes of hypoperfusion associated with worsened outcomes, while fractional volumes of hyperperfusion appeared beneficial or benign.
... AUCIRF which is calculated by integrating the IRF with respect to AIF. and MTT is calculated by dividing the AUCIRF by PHIRF. Additionally, in oSVD we evaluate a metric called oscillation index (OI), which gives the variability in the IRF [12][13][14]. ...
In intracranial aneurysm (IA) treatment, digital subtraction angiography (DSA) monitors device-induced hemodynamic changes. Quantitative angiography (QA) provides more precise assessments but is limited by hand-injection variability. This study evaluates correction methods using in vitro phantoms that mimic diverse aneurysm morphologies and locations, addressing the 2D and temporal limitations of DSA. We used a patient-specific phantom to replicate three distinct IA morphologies at various Circle of Willis points: the middle cerebral artery (MCA), anterior communicating artery (ACA), and the internal carotid artery (ICA), each varying in size and shape. The diameters of the IA at MCA, ACA and ICA are 10.1, 10 and 7 millimeters, respectively. QA parameters for both non-stenosed and stenosed conditions were measured with 5ml and 10ml boluses over various injection durations to generate time density curves (TDCs). To address the variability in injection, several singular value decomposition (SVD) variants, standard SVD (sSVD) with Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD) were applied. These methods enabled the extraction of IA impulse response function (IRF), peak height (PHIRF), area under the curve (AUCIRF), and mean transit time (MTT). We evaluated the robustness of bias-reducing methods by observing the invariance of these parameters with respect to the injection conditions, and the location and size of the aneurysm. The application of SVD variants, sSVD, bSVD, and oSVD, significantly reduced QA parameter variability due to injection techniques.
... The variants explored include standard SVD with classical Tikhonov regularization (sSVD) 17 , block-circulant SVD (bSVD) 18 , and oscillation index SVD (oSVD). 19 These methods aim to optimize the balance between fidelity to the measured data and regularization to mitigate the effects of noise and ill-conditioning and will be briefly described in the context of 2D-QA. ...
Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD-based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations. The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using Computational Fluid Dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time-density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block-circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment. Implementing SVD-based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.
... The absolute Cerebral Blood Volume, or CBV, (ml/100 g) was calculated on a voxelwise basis from the ratio of the area under the tissue concentration curve and the AIF (Fig. 1C). The quantification of CBF (ml/100 g/min) was performed by a deconvolution of the AIF and C(t) using a singular-value decomposition with a block-circulant matrix (Fig. 1C) (Wu et al., 2003). The Mean Transit Time (MTT), which represents the average time that the contrast agent needs to travel through the entire circulation for an ideal bolus, was determined using the central volume theorem as the ratio of CBV and CBF (MTT = CBV/CBF) (Fig. 1C). ...
... Local AIFs were employed to deconvolve concentration-time curves using truncated singular value decomposition. CBF and CBV maps were computed with an emphasis on the standardized procedures to ensure data reliability [16][17][18]. CBF maps (with global truncation thresholds set at 10%) were produced utilizing an inhouse Matlab (The MathWorks Inc, Natick, MA) code. ...
Objectives. This study investigates the association between cerebral blood flow (CBF) and overall survival (OS) in glioblastoma multiforme (GBM) patients receiving chemoradiation. Identifying CBF biomarkers could help predict patient response to this treatment, facilitating the development of personalized therapeutic strategies. Materials and Methods. This retrospective study analyzed CBF data from dynamic susceptibility contrast (DSC) MRI in 30 newly diagnosed GBM patients (WHO grade IV). Radiomics features were extracted from CBF maps, tested for robustness, and correlated with OS. Kaplan-Meier analysis was used to assess the predictive value of radiomic features significantly associated with OS, aiming to stratify patients into groups with distinct post-treatment survival outcomes. Results. While mean relative CBF and CBV failed to serve as independent prognostic markers for OS, the prognostic potential of radiomic features extracted from CBF maps was explored. Ten out of forty-three radiomic features with highest intraclass correlation coefficients (ICC > 0.9), were selected for characterization. While Correlation and Zone Size Variance (ZSV) features showed significant OS correlations, indicating prognostic potential, Kaplan-Meier analysis did not significantly stratify patients based on these features. Visual analysis of the graphs revealed a predominant association between the identified radiomic features and OS under two years. Focusing on this subgroup, Correlation, ZSV, and Gray-Level Nonuniformity (GLN) emerged as significant, suggesting that a lack of heterogeneity in perfusion patterns may be indicative of a poorer outcome. Kaplan-Meier analysis effectively stratified this cohort based on the features mentioned above. Receiver operating characteristic (ROC) analysis further validated their prognostic value, with ZSV demonstrating the highest sensitivity and specificity (0.75 and 0.85, respectively). Conclusion. Our findings underscored radiomics features sensitive to CBF heterogeneity as pivotal predictors for patient stratification. Our results suggest that these markers may have the potential to identify patients who are unlikely to benefit from standard chemoradiation therapy.
... The variants explored include standard SVD with classical Tikhonov regularization (sSVD), 17 block-circulant SVD (bSVD), 18 and oscillation index SVD (oSVD). 19 These methods aim to optimize the balance between fidelity to the measured data and regularization to mitigate the effects of noise and ill-conditioning and will be briefly described in the context of 2D-QA. ...
Background
Intraoperative 2D quantitative angiography (QA) for intracranial aneurysms (IAs) has accuracy challenges due to the variability of hand injections. Despite the success of singular value decomposition (SVD) algorithms in reducing biases in computed tomography perfusion (CTP), their application in 2D QA has not been extensively explored. This study seeks to bridge this gap by investigating the potential of SVD‐based deconvolution methods in 2D QA, particularly in addressing the variability of injection durations.
Purpose
Building on the identified limitations in QA, the study aims to adapt SVD‐based deconvolution techniques from CTP to QA for IAs. This adaptation seeks to capitalize on the high temporal resolution of QA, despite its two‐dimensional nature, to enhance the consistency and accuracy of hemodynamic parameter assessment. The goal is to develop a method that can reliably assess hemodynamic conditions in IAs, independent of injection variables, for improved neurovascular diagnostics.
Materials and methods
The study included three internal carotid aneurysm (ICA) cases. Virtual angiograms were generated using computational fluid dynamics (CFD) for three physiologically relevant inlet velocities to simulate contrast media injection durations. Time‐density curves (TDCs) were produced for both the inlet and aneurysm dome. Various SVD variants, including standard SVD (sSVD) with and without classical Tikhonov regularization, block‐circulant SVD (bSVD), and oscillation index SVD (oSVD), were applied to virtual angiograms. The method was applied on virtual angiograms to recover the aneurysmal dome impulse response function (IRF) and extract flow related parameters such as Peak Height PHIRF, Area Under the Curve AUCIRF, and Mean transit time MTT. Next, correlations between QA parameters, injection duration, and inlet velocity were assessed for unconvolved and deconvolved data for all SVD methods. Additionally, we performed an in vitro study, to complement our in silico investigation. We generated a 2D DSA using a flow circuit design for a patient‐specific internal carotid artery phantom. The DSA showcases factors like x‐ray artifacts, noise, and patient motion. We evaluated QA parameters for the in vitro phantoms using different SVD variants and established correlations between QA parameters, injection duration, and velocity for unconvolved and deconvolved data.
Results
The different SVD algorithm variants showed strong correlations between flow and deconvolution‐adjusted QA parameters. Furthermore, we found that SVD can effectively reduce QA parameter variability across various injection durations, enhancing the potential of QA analysis parameters in neurovascular disease diagnosis and treatment.
Conclusion
Implementing SVD‐based deconvolution techniques in QA analysis can enhance the precision and reliability of neurovascular diagnostics by effectively reducing the impact of injection duration on hemodynamic parameters.
... Cerebral blood flow (CBF) maps were calculated by circular deconvolution of tissue concentration curves using a contralesional arterial reference curve. 13 Cerebral blood volume (CBV) was calculated by numeric integration of the tissue concentration curve. Mean transit time (MTT) was the quotient of CBV and CBF. ...
Futile recanalization hampers prognoses for ischemic stroke patients despite successful recanalization therapy. Allegedly, hypertension and reperfusion deficits contribute, but a better understanding is needed of how they interact and mediate disease outcome. We assessed data from spontaneously hypertensive and normotensive Wistar-Kyoto rats (male, n=6-7/group) that were subjected to two-hour embolic middle cerebral artery occlusion and thrombolysis in preclinical trials. Serial MRI allowed lesion monitoring and parcellation of regions-of-interest that represented infarcted (core) or recovered (perilesional) tissue. Imaging markers of hemodynamics and blood-brain barrier (BBB) status were related to tissue fate and neurological outcome. Despite comparable ischemic severity during occlusion between groups, hypertensive rats temporarily developed larger lesions after recanalization, with permanently aggravated vasogenic edema and BBB permeability. One day post-stroke, cerebral blood flow (CBF) was variably restored, but blood transit times were consistently prolonged in hypertensives. Compared to the core, perilesional CBF was normo-to- hyperperfused in both groups, yet this pattern reversed after seven days. Volumes of hypo- and hyperperfusion developed irrespective of strain, differentially associating with final infarct volume and behavioral outcome. Incomplete reperfusion and cerebral injury after thrombolysis were augmented in hypertensive rats. One day after thrombolysis, hypoperfusion associated with worsened outcomes, while regional hyperperfusion appeared beneficial or benign.
... The tissue concentration response is proportional to the amount of blood (delivering arterial tracer concentration, AIF) passing through the tissue per unit time (CBF) and can be defined as the convolution of the tissue response function [CBF × residue function R(t) and AIF as C t (t) = CBF · AIF⨂R(t)] (83,84). The tissue impulse response function was obtained by deconvolution using the traditional singular value decomposition approach with a fixed threshold (20% cutoff) (85)(86)(87). At R(t) = 0, CBF was determined as the initial height of the tissue impulse response function ( fig. ...
Cerebral perfusion is critical for the early detection of neurological diseases and for effectively monitoring disease progression and treatment responses. Mouse models are widely used in brain research, often under anesthesia, which can affect vascular physiology. However, the impact of anesthesia on regional cerebral blood volume and flow in mice has not been thoroughly investigated. In this study, we have developed a whole-brain perfusion MRI approach by using a 5-second nitrogen gas stimulus under inhalational anesthetics to induce transient BOLD dynamic susceptibility contrast (DSC). This method proved to be highly sensitive, repeatable within each imaging session, and across four weekly sessions. Relative cerebral blood volumes measured by BOLD DSC agree well with those by contrast agents. Quantitative cerebral blood volume and flow metrics were successfully measured in mice under dexmedetomidine and various isoflurane doses using both total vasculature-sensitive gradient-echo and microvasculature-sensitive spin-echo BOLD MRI. Dexmedetomidine reduces cerebral perfusion, while isoflurane increases cerebral perfusion in a dose-dependent manner.
... Quantitative T 1 and T 2 maps were calculated by derivative-based full least-squares fitting of complex-valued data (21). Maps of cerebral blood flow (CBF) were calculated by circular deconvolution of tissue concentration curves using an arterial reference curve obtained from the contralateral hemisphere (22). CBV was calculated by numeric Frontiers in Neurology 04 frontiersin.org ...
General anesthesia is routinely used in endovascular thrombectomy procedures, for which volatile gas and/or intravenous propofol are recommended. Emerging evidence suggests propofol may have superior effects on disability and/or mortality rates, but a mode-of-action underlying these class-specific effects remains unknown. Here, a moderate isoflurane or propofol dosage on experimental stroke outcomes was retrospectively compared using serial multiparametric MRI and behavioral testing. Adult male rats (N = 26) were subjected to 90-min filament-induced transient middle cerebral artery occlusion. Diffusion-, T2- and perfusion-weighted MRI was performed during occlusion, 0.5 h after recanalization, and four days into the subacute phase. Sequels of ischemic damage—blood–brain barrier integrity, cerebrovascular reactivity and sensorimotor functioning—were assessed after four days. While size and severity of ischemia was comparable between groups during occlusion, isoflurane anesthesia was associated with larger lesion sizes and worsened sensorimotor functioning at follow-up. MRI markers indicated that cytotoxic edema persisted locally in the isoflurane group early after recanalization, coinciding with burgeoning vasogenic edema. At follow-up, sequels of ischemia were further aggravated in the post-ischemic lesion, manifesting as increased blood–brain barrier leakage, cerebrovascular paralysis and cerebral hyperperfusion. These findings shed new light on how isoflurane, and possibly similar volatile agents, associate with persisting injurious processes after recanalization that contribute to suboptimal treatment outcome.
... Thereafter, the CBV was obtained according to Equation (2), using Simpson's rule for integration. Moreover, the CBF was obtained according to Equation (3), using a blockcirculant SVD singular value decomposition method [28]. All analyses were performed in a voxel-wise matter. ...
Perfusion measures of the total vasculature are commonly derived with gradient-echo (GE) dynamic susceptibility contrast (DSC) MR images, which are acquired during the early passes of a contrast agent. Alternatively, spin-echo (SE) DSC can be used to achieve specific sensitivity to the capillary signal. For an improved contrast-to-noise ratio, ultra-high-field MRI makes this technique more appealing to study cerebral microvascular physiology. Therefore, this study assessed the applicability of SE-DSC MRI at 7 T. Forty-one elderly adults underwent 7 T MRI using a multi-slice SE-EPI DSC sequence. The cerebral blood volume (CBV) and cerebral blood flow (CBF) were determined in the cortical grey matter (CGM) and white matter (WM) and compared to values from the literature. The relation of CBV and CBF with age and sex was investigated. Higher CBV and CBF values were found in CGM compared to WM, whereby the CGM-to-WM ratios depended on the amount of largest vessels excluded from the analysis. CBF was negatively associated with age in the CGM, while no significant association was found with CBV. Both CBV and CBF were higher in women compared to men in both CGM and WM. The current study verifies the possibility of quantifying cerebral microvascular perfusion with SE-DSC MRI at 7 T.
... 32 Maps of cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) were calculated by circular deconvolution of tissue concentration curves using an arterial reference curve obtained from the contralateral hemisphere. 33 Oscillation index regularization was set to 0.17. CBV was calculated by numeric integration of the tissue concentration curve, truncated at the 400 th image to minimize contrast agent recirculation effects. ...
Futile recanalization hampers prognoses of ischemic stroke after successful mechanical thrombectomy, hypothetically through post-recanalization perfusion deficits, onset-to-groin delays and sex effects. Clinically, acute multiparametric imaging studies remain challenging. We assessed possible relationships between these factors and disease outcome after experimental cerebral ischemia-reperfusion, using translational MRI, behavioral testing and multi-model inference analyses. Male and female rats (N = 60) were subjected to 45-/90-min filament-induced transient middle cerebral artery occlusion. Diffusion, T 2 - and perfusion-weighted MRI at occlusion, 0.5 h and four days after recanalization, enabled tracking of tissue fate, and relative regional cerebral blood flow (rrCBF) and -volume (rrCBV). Lesion areas were parcellated into core, salvageable tissue and delayed injury, verified by histology. Recanalization resulted in acute-to-subacute lesion volume reductions, most apparently in females (n = 19). Hyperacute normo-to-hyperperfusion in the post-ischemic lesion augmented towards day four, particularly in males (n = 23). Tissue suffering delayed injury contained higher ratios of hypoperfused voxels early after recanalization. Regressed against acute-to-subacute lesion volume change, increased rrCBF associated with lesion growth, but increased rrCBV with lesion reduction. Similar relationships were detected for behavioral outcome. Post-ischemic hyperperfusion may develop differentially in males and females, and can be beneficial or detrimental to disease outcome, depending on which perfusion parameter is used as explanatory variable.
... The CBV maps were calculated by dividing the area of the concentration time curve in each voxel with the area of the arterial input function (AIF). The CBF and MTT maps were calculated using the central volume theorem (CBF = CBV/MTT) and Zierler's area-to-height relationship after deconvolution of tissue concentration curves with the AIF using a singular value decomposition (SVD) deconvolution algorithm (Wu et al., 2003). The AIF was defined by taking the average of semiautomatically selected voxels within the branches of the middle cerebral artery (MCA) around the Sylvian fissure. ...
Objective:
Dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) has previously shown alterations in cerebral perfusion in patients with systemic lupus erythematosus (SLE). However, the results have been inconsistent, in particular regarding neuropsychiatric (NP) SLE. Thus, we investigated perfusion-based measures in different brain regions in SLE patients with and without NP involvement, and additionally, in white matter hyperintensities (WMHs), the most common MRI pathology in SLE patients.
Materials and methods:
We included 3 T MRI images (conventional and DSC) from 64 female SLE patients and 19 healthy controls (HC). Three different NPSLE attribution models were used: the Systemic Lupus International Collaborating Clinics (SLICC) A model (13 patients), the SLICC B model (19 patients), and the American College of Rheumatology (ACR) case definitions for NPSLE (38 patients). Normalized cerebral blood flow (CBF), cerebral blood volume (CBV) and mean transit time (MTT) were calculated in 26 manually drawn regions of interest and compared between SLE patients and HC, and between NPSLE and non-NPSLE patients. Additionally, normalized CBF, CBV and MTT, as well as absolute values of the blood-brain barrier leakage parameter (K2) were investigated in WMHs compared to normal appearing white matter (NAWM) in the SLE patients.
Results:
After correction for multiple comparisons, the most prevalent finding was a bilateral significant decrease in MTT in SLE patients compared to HC in the hypothalamus, putamen, right posterior thalamus and right anterior insula. Significant decreases in SLE compared to HC were also found for CBF in the pons, and for CBV in the bilateral putamen and posterior thalamus. Significant increases were found for CBF in the posterior corpus callosum and for CBV in the anterior corpus callosum. Similar patterns were found for both NPSLE and non-NPSLE patients for all attributional models compared to HC. However, no significant perfusion differences were revealed between NPSLE and non-NPSLE patients regardless of attribution model. The WMHs in SLE patients showed a significant increase in all perfusion-based metrics (CBF, CBV, MTT and K2) compared to NAWM.
Conclusion:
Our study revealed perfusion differences in several brain regions in SLE patients compared to HC, independently of NP involvement. Furthermore, increased K2 in WMHs compared to NAWM may indicate blood-brain barrier dysfunction in SLE patients. We conclude that our results show a robust cerebral perfusion, independent from the different NP attribution models, and provide insight into potential BBB dysfunction and altered vascular properties of WMHs in female SLE patients. Despite SLE being most prevalent in females, a generalization of our conclusions should be avoided, and future studies including all sexes are needed.
... Кількісні фізіологічні параметрами гемодинаміки в переважній більшості випадків розраховують, використовуючи метод деконволюції [5][6][7]. У цьому методі час-концентрація криві розглядають як результат згортки функції-відповіді з функцією артеріального притоку (ФАП). Отже, визначення ФАП є важливим кроком для отримання коректних значень фізіологічних параметрів гемодинаміки [8]. ...
Вірне визначення функції артеріального притоку є вирішальним кроком при обчисленні гемодинамічних параметрів перфузії за даними динамічно-сприятливої контрастної перфузійної магнітно-резонансної томографії. Хибне визначення функції артеріального притоку призводить до неточного кількісного оцінювання перфузійних параметрів під час використання методу деконволюції, що є наразі найуживанішим для визначення даних кровотоку, об’єму крові та середнього часу проходження. Дане дослідження спрямоване на розробку повністю автоматичного методу визначення місця для розрахунку функції артеріального притоку шляхом максимізації функції якості, яка розраховується за зведеними перфузійними параметрами. Для побудови функції якості у даному досліджені використовуються такі зведені перфузійних характеристик, як максимальне підсилення, площа під кривою, час до моменту максимального підсилення та повна ширини на рівні половинної амплітуди. Запропонований метод характеризується повною відтворюваністю результатів пошуку кандидатів у функцію артеріального притоку та підходить для обробки T2*-зважених перфузійних зображень магнітно-резонансної томографії із патологічною анатомією мозку людини. Для аналізу запропонованого методу у цьому дослідженні використовуються зображення від 32 різних пацієнтів із колекції TCGA мультиформної гліобластоми відкритої бази даних. Експертна оцінка дозволяє говорити про отримання у 84 % проаналізованих випадків більш кращих результатів у порівнянні з існуючим аналогом щодо розташування знайдених кандидатів у функцію артеріального притоку, форм розрахованих за знайденими кандидатами функцій артеріального притоку, а також перфузійних карт, розрахованих методом деконволюції з використанням знайдених кандидатів для визначення функції артеріального притоку.
... Simulated data were processed using a Matlab implementation of model-free deconvolution 47 , assuming regularization of 0.1. All simulations were run 1000 times per perturbation (perturbations described below). ...
Hyperpolarized carbon-13 magnetic resonance imaging is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel perfusion formalism and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T1, transmit B1, and kPL. A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100 g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100 g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2%, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02/s, p = 0.03, respectively) and a decrease in mean transit time (31 ± 8 vs 60 ± 2 s, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.
... However, a crucial prerequisite for accurate estimation of the shape of the tissue residue function is a reliable deconvolution algorithm. The true residue function is, by definition, characterized by non-negative values and strict decrease, while the most commonly used deconvolution algorithms, based on singular value decomposition [9,13], tend to return residue function estimates that suffer from severe oscillations [10]. Hence, improved deconvolution algorithms, with the ability to return physiologically plausible residue functions in DSC-MRI, have attracted recent attention [e.g. ...
Background
Estimation of the oxygen extraction fraction (OEF) by quantitative susceptibility mapping (QSM) magnetic resonance imaging (MRI) is promising but requires systematic evaluation. Extraction of OEF-related information from the tissue residue function in dynamic susceptibility contrast MRI (DSC-MRI) has also been proposed. In this study, whole-brain OEF repeatability was investigated, as well as the relationships between QSM-based OEF and DSC-MRI-based parameters, i.e., mean transit time (MTT) and an oxygen extraction index, referred to as apparent OEF (AOEF).
Method
Test-retest data were obtained from 20 healthy volunteers at 3 T. QSM maps were reconstructed from 3D gradient-echo MRI phase data, using morphology-enabled dipole inversion. DSC-MRI was accomplished using gradient-echo MRI at a temporal resolution of 1.24 s.
Results
The whole-brain QSM-based OEF was (40.4±4.8) % and, in combination with a previously published CBF estimate, this corresponds to CMRO2 = 3.36 ml O2/min/100 g. The intra-class correlation coefficient [ICC(2,1)] for OEF test-retest data was 0.73. The MTT-versus-OEF and AOEF-versus-OEF relationships showed correlation coefficients of 0.61 (p=0.004) and 0.52 (p=0.019), respectively.
Discussion
QSM-based OEF showed a convincing absolute level and good test-retest results in terms of the ICC. Moderate to good correlations between QSM-based OEF and DSC-MRI-based parameters were observed. The present results constitute an indicator of the level of robustness that can be achieved without applying extraordinary resources in terms of MRI equipment, imaging protocol, QSM reconstruction, and OEF analysis.
... Atwi et al. (2019) used a non-parametric singular-value decomposition (SVD) approach to model dCVR in both task and resting-state fMRI. In the SVD method, which is well established for response-function modeling in dynamic susceptibility contrast MRI ( Chen et al., 2005 ;Ostergaard et al., 1996 ;Wu et al., 2003 ), noise contributions are controlled by thresholding the eigenvalues of the diagonal matrix. This effectively reduces the rank of the decomposition, which is by definition analogous to truncating the frequency spectrum of the signal based on spectral power, and leads to oscillations in the resulting dCVR estimate. ...
Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work is motivated by the current need to evaluate the feasibility of dCVR modeling in various experimental conditions. In this work, we present a comparison of several recently published/utilized model-based deconvolution (response estimation) approaches for estimating the CO2 response function h(t), including maximum a posteriori likelihood (MAP), inverse logit (IL), canonical correlation analysis (CCA), and basis expansion (using Gamma and Laguerre basis sets). To aid the comparison, we devised a novel simulation framework that incorporates a wide range of SNRs, ranging from 10 to -7 dB, representative of both task and resting-state CO2 changes. In addition, we built ground-truth h(t) into our simulation framework, overcoming the conventional limitation that the true h(t) is unknown. Moreover, to best represent realistic noise found in fMRI scans, we extracted noise from in-vivo resting-state scans. Furthermore, we introduce a simple optimization of the CCA method (CCAopt) and compare its performance to these existing methods. Our findings suggest that model-based methods can accurately estimate dCVR even amidst high noise (i.e. resting-state), and in a manner that is largely independent of the underlying model assumptions for each method. We also provide a quantitative basis for making methodological choices, based on the desired dCVR parameters, the estimation accuracy and computation time. The BEL method provided the highest accuracy and robustness, followed by the CCAopt and IL methods. Of the three, the CCAopt method has the lowest computational requirements. These findings lay the foundation for wider adoption of dCVR estimation in CVR mapping.
... 19 This formulation is equivalent to circulant convolution in the time domain and therefore considered insensitive to delay in contrast arrival between the artery and the tissue (T0). 19,23 The flow-scaled IRF can be estimated by the inverse Fourier Transform of the quotient between tissue and arterial TDC frequency spectrums. However, due to noise and other artifacts, the raw solution often results in flow-scaled IRFs with spurious oscillations that are not physiologically plausible. ...
Objective: CTP is an important diagnostic tool in managing patients with acute ischemic stroke, but challenges persist in the reliability of stroke lesion volumes determined with different software. We investigated a systematic method to calibrate CTP lesion thresholds between deconvolution algorithms using a digital perfusion phantom.
Approach: The accuracy of one model-independent and two model-based deconvolution algorithms in estimating ground truth cerebral blood flow (CBF) and Tmax in the phantom was quantified. Reference thresholds for ischemic core and penumbra were model-independent CBF<30% and Tmax>6 s, respectively, which is the current clinical standard. The equivalent model-based CBF and Tmax thresholds were determined by comparing linear regressions of phantom ground truth and deconvolution-estimated perfusion between algorithms. Calibrated thresholds were then validated in 63 patients with large vessel stroke by comparing admission CTP ischemic core and <3-hour diffusion-weighted imaging (DWI) lesion volume by Bland-Altman analysis. Agreement in target mismatch (core < 70 ml, penumbra ≥ 15 ml, mismatch ratio ≥ 1.8) determined by the three methods was assessed by Cohen's kappa (κ) and concordance.
Main Results: The calibrated thresholds were CBF<15% and Tmax>6 s for both model-based methods. DWI minus CTP lesion mean volume differences (95% limits of agreement) were +16.2 (-30.9 to 63.3) ml, +10.9 (-32.9 to 54.7) ml, and +13.8 (-48.1 to 75.7) ml for model-independent and the two calibrated model-based approaches, respectively. Agreement in mismatch profiles with the two model-based deconvolution methods versus model-independent assessment was κ = 0.87 (95% confidence interval [CI]: 0.72 to 1.00) and κ = 0.86 (95% CI: 0.70 to 1.00), and both achieved 95% concordance.
Significance: We reported a systematic method of calibrating perfusion thresholds between deconvolution algorithms based on their quantitative accuracy. This may harmonize ischemic lesion volumes determined by different CTP software.
Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is the most common MRI method in clinical environments for assessment of perfusion-related parameters. In this study, special emphasis was placed on the shape of the tissue residue function under different physiological conditions. DSC-MRI-based parameters assumed to reflect arterial delay and cerebral oxygen extraction were obtained by deconvolution of tissue and arterial contrast-agent concentration time curves. The established mean transit time (MTT) estimate was supplemented by biophysical modelling for extraction of the oxygen extraction capacity, quantified in terms of an apparent oxygen extraction fraction (AOEF) index. Eight healthy volunteers were examined during normal breathing and spontaneous hyperventilation. Whole-brain MTT and AOEF increased during hyperventilation in all volunteers (average increase 33 % and 30 %, respectively). The arterial delay, reflecting the inverse of arterial flow rate, was also prolonged in all volunteers, and the mean arterial delay was 63 % longer during hyperventilation. The corresponding whole-brain MTT estimates were 3.8 ± 0.7 s during normal breathing and 5.0 ± 1.3 s during hyperventilation (mean ± SD, n = 8). The applied Bézier curve deconvolution algorithm returned tissue residue functions of plausible shapes, i.e., without oscillations and negative values, and some indications that curve shape is relevant for improved assessment of oxygen extraction properties were demonstrated.
Estimating progression of acute ischemic brain lesions – or biological lesion age - holds huge practical importance for hyperacute stroke management. The current best method for determining lesion age from non-contrast computerised tomography (NCCT), measures Relative Intensity (RI), termed Net Water Uptake (NWU). We optimised lesion age estimation from NCCT using a convolutional neural network – radiomics (CNN-R) model trained upon chronometric lesion age (Onset Time to Scan: OTS), while validating against chronometric and biological lesion age in external datasets ( N = 1945). Coefficients of determination (R ² ) for OTS prediction, using CNN-R, and RI models were 0.58 and 0.32 respectively; while CNN-R estimated OTS showed stronger associations with ischemic core:penumbra ratio, than RI and chronometric, OTS (ρ ² = 0.37, 0.19, 0.11); and with early lesion expansion (regression coefficients >2x for CNN-R versus others) (all comparisons: p < 0.05). Concluding, deep-learning analytics of NCCT lesions is approximately twice as accurate as NWU for estimating chronometric and biological lesion ages.
Dynamic computed tomography (CT)-based brain perfusion imaging is a non-invasive technique that can provide quantitative measurements of cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT). However, due to high radiation dose, dynamic CT scan with a low tube voltage and current protocol is commonly used. Because of this reason, the increased noise degrades the quality and reliability of perfusion maps. In this study, we aim to propose and investigate the feasibility of utilizing a convolutional neural network and a bi-directional long short-term memory model with an attention mechanism to self-supervisedly yield the impulse residue function (IRF) from dynamic CT images. Then, the predicted IRF can be used to compute the perfusion parameters. We evaluated the performance of the proposed method using both simulated and real brain perfusion data and compared the results with those obtained from two existing methods: singular value decomposition and tensor total-variation. The simulation results showed that the overall performance of parameter estimation obtained from the proposed method was superior to that obtained from the other two methods. The experimental results showed that the perfusion maps calculated from the three studied methods were visually similar, but small and significant differences in perfusion parameters between the proposed method and the other two methods were found. We also observed that there were several low-CBF and low-CBV lesions (i.e., suspected infarct core) found by all comparing methods, but only the proposed method revealed longer MTT. The proposed method has the potential to self-supervisedly yield reliable perfusion maps from dynamic CT images.
This study aims to mitigate these biases and enhance QA analysis by applying a path-length correction (PLC) correction, followed by singular value decomposition (SVD)-based deconvolution, to angiograms obtained through both in-silico and in-vitro methods. We utilized DSA data from in-silico and in-vitro patient-specific intracranial aneurysm models. To remove projection bias, PLC for various views were developed by co-registering the pre-existing 3D vascular geometry mask with the DSA projections, followed by ray tracing to determine paths across 3D vessel structures. These maps were used to normalize the logarithmic angiographic images, correcting for projection-induced foreshortening across different angles. Subsequently, we focused on eliminating injection bias by analyzing the corrected angiograms under varied projection views, injection rates, and flow conditions. Regions of interest at the aneurysm dome and inlet were placed to extract Time Density Curves for the lesion and the arterial input function, respectively. Using three standard SVD methodologies, we extracted the aneurysm Impulse Response function (IRF) and its associated parameters Peak Height (PHIRF), Area Under the Curve (AUCIRF), and Mean Transit Time (MTT). Our methodology employing PLC and SVD-based deconvolution ensures reliable quantitative angiographic measurements across varying conditions, supporting consistent assessments of disease severity and treatment efficacy. This approach significantly enhances intrapatient and intraprocedural reliability in neurovascular diagnostics.
Objective:
Hemifacial spasm (HFS) is treated by a surgical procedure called microvascular decompression (MVD). However, HFS re-appearing phenomenon after surgery, presenting as early recurrence, is experienced by some patients after MVD. Dynamic susceptibility contrast (DSC) perfusion MRI and two analytical methods: receiver operating characteristic (ROC) curve and machine learning, were used to predict early recurrence in this study.
Methods:
This study enrolled sixty patients who underwent MVD for HFS. They were divided into two groups: Group A consisted of 32 patients who had early recurrence, and Group B consisted of 28 patients who had no early recurrence of HFS. DSC perfusion MRI was undergone by all patients before the surgery to obtain the several parameters. ROC curve and machine learning methods were used to predict early recurrence using these parameters.
Results:
Group A had significantly lower relative cerebral blood flow (rCBF) than Group B in most of the selected brain regions, as shown by the region-of-interest (ROI)-based analysis. By combining three extraction fraction (EF) values at middle temporal gyrus, posterior cingulate, and brainstem, with age, using naive Bayes machine learning method, the best prediction model for early recurrence was obtained. This model had an area under the curve (AUC) value of 0.845.
Conclusion:
By combining EF values with age or sex using machine learning methods, DSC perfusion MRI can be used to predict early recurrence before MVD surgery. This may help neurosurgeons to identify patients who are at risk of HFS recurrence and provide appropriate postoperative care.
Dynamic cerebral perfusion CT (PCT) is an effective imaging technique for the clinical diagnosis and therapy guidance of many kinds of cerebrovascular diseases (CVDs), but the large radiation dose imposed on a patient during repeated CT scans greatly limits its clinical applications. Achieving low-dose PCT imaging with the help of advanced and satisfactory imaging methods is needed. A kinetic-induced voxel-clustering filter (KVCF) is proposed in this work to help process noisy and distorted PCT images acquired from low-dose CT scan protocols. In this new method, the intrinsic kinetic information of objective PCT images is extracted and effectively utilized to construct an image filter for every PCT frame. The new method is validated using both simulated and clinical low-dose PCT data, and the peak signal-to-noise ratio (PSNR) and feature similarity (FSIM) are applied for quantitative evaluations of both the dynamic images and the calculated hemodynamic parametric maps. Compared to several existing methods, the proposed KVCF method produces the best qualitative and quantitative imaging effects. With satisfactory performance and high interpretability, KVCF is proven to be effective and implementable in clinical low-dose PCT imaging tasks.
Introduction
To compare the perfusion volumes assessed by a new automated CT perfusion (CTP) software iStroke with the circular singular value decomposition software RAPID and determine its predictive value for functional outcome in patients with acute ischaemic stroke (AIS) who underwent endovascular treatment (EVT).
Methods
Data on patients with AIS were collected from four hospitals in China. All patients received CTP followed by EVT with complete recanalisation within 24 hours of symptom onset. We evaluated the agreement of CTP measures between the two softwares by Spearman’s rank correlation tests and kappa tests. Bland-Altman plots were used to evaluate the agreement of infarct core volume (ICV) on CTP and ground truth on diffusion-weighted imaging (DWI). Logistic regression models were used to test the association between ICV on these two softwares and functional outcomes.
Results
Among 326 patients, 228 had DWI examinations and 40 of them had infarct volume >70 mL. In all patients, the infarct core and hypoperfusion volumes on iStroke had a strong correlation with those on RAPID (ρ=0.68 and 0.66, respectively). The agreement of large infarct core (volume >70 mL) was substantial (kappa=0.73, p<0.001) between these two softwares. The ICV measured by iStroke and RAPID was significantly correlated with independent functional outcome at 90 days (p=0.009 and p<0.001, respectively). In patients with DWI examinations and those with an ICV >70 mL, the ICV of iStroke and RAPID was comparable on individual agreement with ground truth.
Conclusion
The automatic CTP software iStroke is a reliable tool for assessing infarct core and mismatch volumes, making it clinically useful for selecting patients with AIS for acute reperfusion therapy in the extended time window.
Background
To evaluate the feasibility and performance of velocity selective (VS) ASL based cerebral blood volume (CBV) mapping among glioma patients in clinical practice, comparing with the VSASL based cerebral blood flow (CBF) mapping and dynamic susceptibility contrast-enhanced perfusion-weighted imaging (DSC-PWI).
Methods
This study included patients with histologically proven brain glioma who underwent preoperative MRI including VSASL based CBV, CBF and DSC-PWI between 2017 and 2021. Visual inspection was performed to evaluate the lesion conspicuity on VSASL derived CBV maps based on 1–3 criteria by comparing to the surrounding parenchyma. The relative values of maximum tumor blood volume (rTBV) and tumor blood flow (rTBF), derived from VSASL or DSC-PWI were compared between low-grade and high-grade glioma. Linear regression and Bland–Altman analyses were constructed to evaluate the correlation and agreement of rTBV measurements between VSASL method and DSC-PWI. The diagnosis ability for discrimination between low- and high-grade glioma were evaluated using ROC curves.
Results
Forty-eight participants (mean age, 45 ± 13 years; 25 men; 23 high-grade gliomas) were evaluated. The lesion conspicuity of VSASL based CBV maps was good on visual inspection (averaged scores: 2.26 ± 0.76, weighted kappa of 0.8 between readers). Moreover, VSASL provided highly correlated quantifications of rTBV (R² = 0.83, p < 0.001) compared to DSC-PWI, and further improved diagnostic performance than VSASL derived rTBF measurements (ROC AUC = 0.94 vs. 0.89).
Conclusions
VSASL served as a promising and accurate means in quantification of TBV for glioma stratification in clinical patients, indicating its potential as a viable non-contrast alternative to DSC-PWI for brain tumor applications.
Pharmacokinetic (PK) parameters, revealing changes in the tumor microenvironment, are related to the pathological information of breast cancer. Tracer kinetic models (e.g., Tofts-Kety model) with a nonlinear least square solver are commonly used to estimate PK parameters. However, the method is sensitive to noise in images. To relieve the effects of noise, a deconvolution (DEC) method, which was validated on synthetic concentration–time series, was proposed to accurately calculate PK parameters from breast dynamic contrast-enhanced magnetic resonance imaging. A time-to-peak-based tumor partitioning method was used to divide the whole tumor into three tumor subregions with different kinetic patterns. Radiomic features were calculated from the tumor subregion and whole tumor-based PK parameter maps. The optimal features determined by the fivefold cross-validation method were used to build random forest classifiers to predict molecular subtypes, Ki-67, and tumor grade. The diagnostic performance evaluated by the area under the receiver operating characteristic curve (AUC) was compared between the subregion and whole tumor-based PK parameters. The results showed that the DEC method obtained more accurate PK parameters than the Tofts method. Moreover, the results showed that the subregion-based Ktrans (best AUCs = 0.8319, 0.7032, 0.7132, 0.7490, 0.8074, and 0.6950) achieved a better diagnostic performance than the whole tumor-based Ktrans (AUCs = 0.8222, 0.6970, 0.6511, 0.7109, 0.7620, and 0.5894) for molecular subtypes, Ki-67, and tumor grade. These findings indicate that DEC-based Ktrans in the subregion has the potential to accurately predict molecular subtypes, Ki-67, and tumor grade.
Background:
While the association between diffusion and perfusion MRI and survival in glioblastoma is established, prognostic models for patients are lacking. This study employed clustering of functional imaging to identify distinct functional phenotypes in untreated glioblastomas, assessing their prognostic significance for overall survival.
Methods:
A total of 289 patients with glioblastoma who underwent preoperative multimodal MR imaging were included. Mean values of apparent diffusion coefficient (ADC) normalized relative cerebral blood volume (nrCBV), and relative cerebral blood flow (rCBF) were calculated for different tumor compartments and the entire tumor. Distinct imaging patterns were identified using Partition Around Medoids (PAM) clustering on the training dataset, and their ability to predict overall survival was assessed. Additionally, tree-based machine-learning models were trained to ascertain the significance of features pertaining to cluster membership.
Results:
Using the training dataset (231/289) we identified two stable imaging phenotypes through PAM clustering with significantly different overall survival (OS). Validation in an independent test set revealed a high-risk group with a median OS of 10.2 months and a low-risk group with a median OS of 26.6 months (p=0.012). Patients in the low-risk cluster had high diffusion and low perfusion values throughout, while the high-risk cluster displayed the reverse pattern. Including cluster membership in all multivariate Cox regression analyses improved performance (p≤ 0.004 each).
Conclusions:
Our research demonstrates that data-driven clustering can identify clinically relevant, distinct imaging phenotypes, highlighting the potential role of diffusion and perfusion MRI in predicting survival rates of glioblastoma patients.
Tomographic perfusion imaging techniques are integral to translational stroke research paradigms that advance our understanding of the disease. Functional ultrasound (fUS) is an emerging technique that informs on cerebral blood volume (CBV) through ultrasensitive Doppler and flow velocity (CBFv) through ultrafast localization microscopy. It is not known how experimental results compare with a classical CBV-probing technique such as dynamic susceptibility contrast-enhanced perfusion MRI (DSC-MRI). To that end, we assessed hemodynamics based on uUS (n = 6) or DSC-MRI (n = 7) before, during and up to three hours after 90-minute filament-induced middle cerebral artery occlusion (MCAO) in rats. Recanalization was followed by a brief hyperperfusion response, after which CBV and CBFv temporarily normalized but progressively declined after one hour in the lesion territory. DSC-MRI data corroborated the incomplete restoration of CBV after recanalization, which may have been caused by the free-breathing anesthetic regimen. During occlusion, MCAO-induced hypoperfusion was more discrepant between either technique, likely attributable to artefactual signal mechanisms related to slow flow, and processing algorithms employed for either technique. In vivo uUS- and DSC-MRI-derived measures of CBV enable serial whole-brain assessment of post-stroke hemodynamics, but readouts from both techniques need to be interpreted cautiously in situations of very low blood flow.
CT perfusion (CTP)-derived quantitative maps of hemodynamic parameters have found important clinical applications in stroke, cancer, and cardiovascular disease. Blood flow, blood volume, transit time, and other perfusion parameters are sensitive markers of pathophysiology with impaired perfusion. This review summarizes the basic principles of CTP including image acquisition, tracer kinetic modeling, deconvolution algorithms, and diagnostic interpretation. The focus is on practical and theoretical considerations for accurate quantitative parametric imaging. Recommended CTP scan parameters to maintain CT number accuracy and optimize radiation dose versus image noise are first reviewed. Tracer kinetic models, which describe how injected contrast material is distributed between blood and the tissue microenvironment by perfusion and bidirectional passive exchange, are then derived. Deconvolution algorithms to solve for hemodynamic parameters of kinetic models are discussed and their quantitative accuracy benchmarked. The applications and diagnostic interpretation of CTP in stroke, cancer, and cardiovascular disease are summarized. Finally, we conclude with a discussion of future directions for CTP research, including radiation dose reduction, new opportunities with novel CT hardware, and emerging diagnostic applications.
The arterial input function (AIF) plays a crucial role in estimating quantitative perfusion properties from dynamic susceptibility contrast (DSC) MRI. An important issue, however, is that measuring the AIF in absolute contrast‐agent concentrations is challenging, due to uncertainty in relation to the measured ‐weighted signal, signal depletion at high concentration, and partial‐volume effects. A potential solution could be to derive the AIF from separately acquired dynamic contrast enhanced (DCE) MRI data. We aim to compare the AIF determined from DCE MRI with the AIF from DSC MRI, and estimated perfusion coefficients derived from DSC data using a DCE‐driven AIF with perfusion coefficients determined using a DSC‐based AIF. AIFs were manually selected in branches of the middle cerebral artery (MCA) in both DCE and DSC data in each patient. In addition, a semi‐automatic AIF‐selection algorithm was applied to the DSC data. The amplitude and full width at half‐maximum of the AIFs were compared statistically using the Wilcoxon rank‐sum test, applying a 0.05 significance level. Cerebral blood flow (CBF) was derived with different AIF approaches and compared further. The results showed that the AIFs extracted from DSC scans yielded highly variable peaks across arteries within the same patient. The semi‐automatic DSC–AIF had significantly narrower width compared with the manual AIFs, and a significantly larger peak than the manual DSC–AIF. Additionally, the DCE‐based AIF provided a more stable measurement of relative CBF and absolute CBF values estimated with DCE–AIFs that were compatible with previously reported values. In conclusion, DCE‐based AIFs were reproduced significantly better across vessels, showed more realistic profiles, and delivered more stable and reasonable CBF measurements. The DCE–AIF can, therefore, be considered as an alternative AIF source for quantitative perfusion estimations in DSC MRI.
Introduction
Dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion studies in magnetic resonance imaging (MRI) provide valuable data for studying vascular cerebral pathophysiology in different rodent models of brain diseases (stroke, tumor grading, and neurodegenerative models). The extraction of these hemodynamic parameters via DSC-MRI is based on tracer kinetic modeling, which can be solved using deconvolution-based methods, among others. Most of the post-processing software used in preclinical studies is home-built and custom-designed. Its use being, in most cases, limited to the institution responsible for the development. In this study, we designed a tool that performs the hemodynamic quantification process quickly and in a reliable way for research purposes.
Methods
The DSC-MRI quantification tool, developed as a Python project, performs the basic mathematical steps to generate the parametric maps: cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), signal recovery (SR), and percentage signal recovery (PSR). For the validation process, a data set composed of MRI rat brain scans was evaluated: i) healthy animals, ii) temporal blood–brain barrier (BBB) dysfunction, iii) cerebral chronic hypoperfusion (CCH), iv) ischemic stroke, and v) glioblastoma multiforme (GBM) models. The resulting perfusion parameters were then compared with data retrieved from the literature.
Results
A total of 30 animals were evaluated with our DSC-MRI quantification tool. In all the models, the hemodynamic parameters reported from the literature are reproduced and they are in the same range as our results. The Bland–Altman plot used to describe the agreement between our perfusion quantitative analyses and literature data regarding healthy rats, stroke, and GBM models, determined that the agreement for CBV and MTT is higher than for CBF.
Conclusion
An open-source, Python-based DSC post-processing software package that performs key quantitative perfusion parameters has been developed. Regarding the different animal models used, the results obtained are consistent and in good agreement with the physiological patterns and values reported in the literature. Our development has been built in a modular framework to allow code customization or the addition of alternative algorithms not yet implemented.
Several techniques are available for assessing brain perfusion or hemodynamics in the clinical setting, in general falling into two basic categories: those using diffusible versus nondiffusible tracers. H215O PET, 99mTc-HMPAO, or 99mTc-ECD SPECT, stable xenon CT, and arterial spin labeled MRI are examples of diffusible tracer techniques, where the tracer is not confined to the vessels and enters the tissue. The major nondiffusible tracer techniques in use are bolus contrast CT and MR perfusion methods, where the tracer remains within the vasculature as long as the blood–brain barrier is intact. Clinical experience in MRI is greatest for bolus contrast or dynamic susceptibility contrast (DSC) perfusion MRI, although use of dynamic contrast-enhanced (DCE) and arterial spin labeled (ASL) techniques is increasing in the clinical setting. Improvements in acquisition and postprocessing technology and techniques have led to improvements for both DSC and ASL perfusion MRI. This chapter focuses primarily on practical clinical applications of perfusion imaging in cerebrovascular diseases and CNS neoplasms, using selected examples to illustrate strengths, weaknesses, or complementary roles of these perfusion MRI approaches.KeywordsContrast-enhanced perfusionArterial spin labeled perfusionMRIIschemiaNeoplastic disordersBiopsy guidancePrognosisRadiogenomicsResponse to therapyNecrosis
The use of dynamic contrast-agent-enhanced magnetic resonance imaging (MRI) can provide insight into hemodynamic processes not detectable using conventional contrast-enhanced magnetic resonance (MR) techniques. This additional data may allow refinement of differential diagnoses based on microvascular physiology. The dominant dynamic gadolinium-based contrast agent (GBCA) injection MRI techniques currently utilized in brain imaging are: (1) T1-weighted dynamic contrast-enhanced (DCE) MRI, and (2) T2/T2*-weighted dynamic susceptibility contrast (DSC) MRI. DSC-MRI is much more commonly used for clinical perfusion imaging of the brain, especially for the evaluation of stroke and tumor. On the other hand, DCE-MRI is the dominant method of dynamic contrast-enhanced MRI outside of the brain. In both DCE-MRI and DSC-MRI, dynamic images are acquired before, during, and after the administration of an exogenous GBCA. This chapter will provide an overview of the general physical principles of these techniques.KeywordsMagnetic resonance imagingDynamic susceptibility contrast MRIDynamic contrast-enhanced MRI
Purpose:
To apply total generalized variation (TGV) and its combination with low-rank and sparse decomposition (LRSD) (LTGV) to cerebral perfusion studies using low-dose dynamic contrast-enhanced (DCE) CT and to quantitatively evaluate their performances through comparisons with those without any regularizers and those of total variation (TV) and its combination with LRSD (LTV) using simulation and clinical data.
Methods:
The simulation study used a realistic digital brain phantom. Low-dose DCE-CT images were reconstructed using the regularizers and primal-dual algorithm. Subsequently, cerebral perfusion parameter (CPP) images were generated from them. Thereafter, their quality was evaluated based on the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Further, the accuracy of CPP estimation was evaluated through a linear regression analysis between the CPP values obtained by the above regularizers and those obtained from the noise-free DCE-CT images. In addition, the mean and standard deviation of the CPP were calculated (region analysis). In the clinical study, low-dose DCE-CT images were generated using normal-dose images acquired from a patient, and CPP images were generated from them similar to that in the simulation study.
Results:
When using LTV and LTGV, both PSNR and SSIM were higher than those of the other methods with increasing regularization parameter values. The results of the linear regression and region analyses demonstrated that TGV generally exhibited the best performance, followed by LTGV, and finally that of TV was significantly different from those of the other regularizers. Despite an overall consistency between the simulation and clinical results, certain inconsistencies appeared owing to the difference in generating low-dose DCE-CT images.
Conclusions:
The results implied that TGV and LTGV were useful in improving the accuracy of CPP estimation using low-dose DCE-CT. This study provides an improved understanding of the performance of regularizers and is expected to aid in the selection of a suitable regularizer for low-dose DCE-CT perfusion studies.
Objective
Although computed tomography perfusion (CTP) is used to select and guide decision-making processes in patients with acute ischemic stroke, there is no clear standardization of the optimal threshold to predict ischemic core volume accurately. The infarct core volume with a relative cerebral blood flow(rCBF) threshold of < 30% is commonly used. We aimed to assess the volumetric agreement of the infarct core volume with different CTP parameters and thresholds using CTP software (RAPID, VITREA) and the infarct volume on diffusion-weighted imaging (DWI), with a short interval time (within 60 min) between CTP and follow-up DWI.
Materials and methods
This retrospective study included 42 acute ischemic stroke patients with occlusion of the large artery in the anterior circulation between April 2017-November 2020. RAPID identified infarct core as tissue rCBF < 20-38%. VITREA defined the infarct core as cerebral blood volume (CBV) < 26-56%. Olea Sphere was used to measure infarct core volume on DWI. The CTP-infarct core volume with different thresholds of perfusion parameters (CBF threshold vs CBV threshold) were compared with DWI-infarct core volumes.
Results
The median time between CTP and DWI was 37.5min. The commonly used threshold of CBV< 41% (4.3 mL) resulted in lower median infarct core volume difference compared to the commonly used thresholds of rCBF < 30% (8.2mL). On the other hand, the optimal thresholds of CBV < 26% (-1.0mL; 95% CI, -53.9 to 58.1 mL; 0.945) resulted in the lowest median infarct core volume difference, narrowest limits of agreement, and largest interclass correlation coefficient compared with the optimal thresholds of rCBF < 38% (4.9 mL; 95% CI, -36.4 to 62.9 mL; 0.939).
Conclusion
Our study found that the both optimal and commonly used thresholds of CBV provided a more accurate prediction of the infarct core volume in patients with AIS than rCBF.
Purpose:
Accurate quantification of ischemic core and ischemic penumbra is mandatory for late-presenting acute ischemic stroke. Substantial differences between MR perfusion software packages have been reported, suggesting that the optimal Time-to-Maximum (Tmax) threshold may be variable. We performed a pilot study to assess the optimal Tmax threshold of two MR perfusion software packages (A: RAPID®; B: OleaSphere®) by comparing perfusion deficit volumes to final infarct volumes as ground truth.
Methods:
The HIBISCUS-STROKE cohort includes acute ischemic stroke patients treated by mechanical thrombectomy after MRI triage. Mechanical thrombectomy failure was defined as a modified thrombolysis in cerebral infarction score of 0. Admission MR perfusion were post-processed using two packages with increasing Tmax thresholds (≥ 6 s, ≥ 8 s and ≥ 10 s) and compared to final infarct volume evaluated with day-6 MRI.
Results:
Eighteen patients were included. Lengthening the threshold from ≥ 6 s to ≥ 10 s led to significantly smaller perfusion deficit volumes for both packages. For package A, Tmax ≥ 6 s and ≥ 8 s moderately overestimated final infarct volume (median absolute difference: - 9.5 mL, interquartile range (IQR) [- 17.5; 0.9] and 0.2 mL, IQR [- 8.1; 4.8], respectively). Bland-Altman analysis indicated that they were closer to final infarct volume and had narrower ranges of agreement compared with Tmax ≥ 10 s. For package B, Tmax ≥ 10 s was closer to final infarct volume (median absolute difference: - 10.1 mL, IQR: [- 17.7; - 2.9]) versus - 21.8 mL (IQR: [- 36.7; - 9.5]) for Tmax ≥ 6 s. Bland-Altman plots confirmed these findings (mean absolute difference: 2.2 mL versus 31.5 mL, respectively).
Conclusions:
The optimal Tmax threshold for defining the ischemic penumbra appeared to be most accurate at ≥ 6 s for package A and ≥ 10 s for package B. This implies that the widely recommended Tmax threshold ≥ 6 s may not be optimal for all available MRP software package. Future validation studies are required to define the optimal Tmax threshold to use for each package.
Object: Dynamic susceptibility contrast MRI (DSC-MRI) is the current standard for cerebral perfusion estimation. Model-dependent approaches for DSC-MRI analysis involve assuming a parametric transit time distribution (TTD) to characterize the passage of contrast agent through tissue microvasculature. Here we compare the utility of four TTD models: namely, skewed-Gaussian, gamma, gamma-variate, and Weibull, to identify the optimal TTD for quantifying brain perfusion.
Materials and Methods: DSC-MRI data were acquired in nine subjects at 1.5T, and normal-appearing white- and gray-matter signals were assessed. TTDs were compared in terms of: goodness-of-fit, evaluated using RMSE; noise sensitivity, assessed via Monte-Carlo-simulated noisy conditions; and fit stability, quantified as the proportion of total fits converging to the global minimum. Computation times for model-fitting were also calculated.
Results: The gamma TTD showed higher fit stability, shorter computation times (p<0.008), and higher robustness against experimental noise as compared to other models. All functions showed similar RMSEs and the parameter estimates (p>0.008) were congruent with literature values.
Discussion: The gamma distribution represents the most suitable TTD for perfusion analysis. Moreover, due to its robustness against noise, the gamma TTD is expected to yield more reproducible estimates than the other models for establishing a standard, multi-center analysis pipeline.
Hyperpolarized carbon-13 MRI is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a model-free formalism for quantifying the metabolic information in hyperpolarized imaging.
This study investigated a novel model-free perfusion and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain.
Simulations showed that the proposed model was robust to perturbations in T 1 , transmit B 1 , and k PL . A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2 %, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02 s ⁻¹ , p = 0.03, respectively) and a decrease in mean transit time (MTT) (31 ± 8 vs 60 ± 2, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.
Hyperpolarized carbon-13 MRI is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a model-free formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel model-free perfusion and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T1, transmit B1, and kPL. A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 ± 2 vs 89 ± 6 mL/100g/min, p < 0.01, respectively) and pigs (139 ± 12 vs 95 ± 5 mL/100g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 ± 5 vs 4 ± 2 %, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 ± 0.007 vs 0.017 ± 0.02 s-1 , p = 0.03, respectively) and a decrease in mean transit time (MTT) (31 ± 8 vs 60 ± 2, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging. 3
Clinical MR Neuroimaging, second edition, provides radiologists, neuroscientists and researchers with a clear understanding of each physiological MR methodology and their applications to the major neurological diseases. Section 1 describes the physical principles underlying each technique and their associated artefacts and pitfalls. Subsequent sections review the application of MRI in a range of clinical disorders: cerebrovascular disease, neoplasia, infection/inflammation/demyelination disorders, seizures, psychiatric/neurodegenerative conditions, and trauma. This new edition includes all recent advances and applications, with greatly increased coverage of permeability imaging, susceptibility imaging, iron imaging, MR spectroscopy and fMRI. All illustrations are completely new, taking advantage of the latest scan capabilities to give images of the highest possible quality. In addition, over 35 new case studies have been included. Editors and contributors are the leading neuroimaging experts worldwide; their unique combination of technical knowledge and clinical expertise makes Clinical MR Neuroimaging the leading text on the subject.
The blur identification problem is formulated as a constrained
maximum-likelihood problem. The constraints directly incorporate a
priori known relations between the blur (and image model) coefficients,
such as symmetry properties, into the identification procedure. The
resulting nonlinear minimization problem is solved iteratively, yielding
a very general identification algorithm. An example of blur
identification using synthetic data is given
Characteristics of blood flow in tissue can be measured by administering an intravascular tracer and then deconvolving and analysing the resulting indicator-dilution curves. Existing deconvolution methods are not typically generalizable to a variety of tissues. The authors have developed a more general deconvolution method using simulated indicator-dilution data. This method involves filtering the Fourier transform of indicator-dilution data with a modification of the Wiener filter, an adaptive deconvolution filter. Unlike the Wiener filter, this adaptive filter requires no previous knowledge of the noise frequency spectrum; it is derived by varying the magnitude of the noise spectrum until the oscillations in the deconvolved data fall below an optimal value. The optimal value corresponds to the setting of the noise spectrum that allows the most accurate and precise measurement of vascular characteristics from deconvolved data. Vascular characteristics measured in brain tissues using this deconvolution method on actual indicator-dilution data were similar to established values. It should be possible to use this method on time-concentration data collected from a variety of tissues using a number of different tracer measurement techniques, thereby allowing the accurate characterization of vascular physiology.
In order to solve the image identification and restoration problem, mathematical models are required for the real-world processes involved in image generation, formation and recording. It may even be argued that the ultimate goal of image restoration, that is the recovery of the original undistorted image, can only be understood and formulated through the use of mathematical models which reflect in one way or another the a priori information one has regarding the observed image, the image formation process and the original image. Without such knowledge, the identification and restoration of a degraded image could probably not be accomplished.
Microscopic susceptibility variations invariably increase apparent transverse relaxation rates. In this paper, we present comparisons between Monte Carlo simulations and experiments with polystyrene microspheres to demonstrate that this enhanced relaxation can be explained quantitatively for both spin echo and gradient echo imaging experiments. The spheres used (1 to 30 μ), and degree of susceptibility variation (caused by 0-12 mM Dy-DTPA) covered a wide range of biologically relevant compartment sizes and contrast agent concentrations. These results show that several regimes of behavior exist, and that contrast dependence is quite different in these regimes. For a given susceptibility, δχ, a small range of particle sizes show peak transverse relaxation. For the range of susceptibilities found in the first pass of a clinical IV contrast agent bolus, this size range is 5 to 10 μ, or roughly capillary sized compartments. In both our simulations and experiments, smaller spheres showed quadratic relaxation versus concentration curves, and larger particles showed sublinear behavior. For particles corresponding to the peak relaxivity, the relaxation-concentration curves were linear. In addition, we demonstrated that increasing the diffusion coefficient can increase, decrease, or, paradoxically, leave unaffected the apparent relaxation rate. The regime for which the diffusion coefficient is relatively unimportant corresponds to the region of peak relaxivity. By using the Bloch-Torrey equation to produce scaling rules, the specific Monte Carlo simulations were extended to more general cases. We use these scaling rules to demonstrate why we often find that susceptibility-induced relaxation rates vary approximately linearly with concentration of injected agent.
Regional cerebral blood flow (rCBF) was assessed using dynamic susceptibility-contrast MRI at 1.5 T. A simultaneous dual FLASH pulse sequence and Gd-DTPA-BMA (0.3 mmol/kg b.w.) were used for examination of 43 volunteers, measuring rCBF in frontal white matter (WM) and in gray matter in the thalamus (GM). Arterial input functions (AIFs) were registered 1) in the carotid artery and 2) in an artery within the GM/WM slice. The measured concentration-vs.-time curve was deconvolved with the AIF using both Fourier Transform (FT) and Singular Value Decomposition (SVD). Relative rCBF was given by the height of the deconvolved response curve. For each volunteer, eight different rCBF maps were calculated, representing different combinations of deconvolution techniques, AIFs, and filters. The average GM–WM rCBF ratios ranged from 2.0–2.2, depending on methodology. Absolute rCBF was 68 ± 28 ml/(min 100 g) in GM and 35 ± 13 ml/(min 100g) in WM (mean ± SD, n = 39). GM–WM rCBF ratios obtained using SVD were 6–10% higher than corresponding ratios obtained using FT. Magn Reson Med 43:691–700, 2000. © 2000 Wiley-Liss, Inc.
Singular value decomposition (SVD) is a promising deconvolution technique for use in dynamic contrast agent magnetic resonance perfusion imaging. Computer simulations, however, show that the selection of the threshold for SVD affects the accuracy of the cerebral blood flow measurements and may distort the shape of the vascular residue function. In this report, a pixel-by-pixel thresholding method is proposed based on the signal-to-noise ratio of the concentration time curve at maximum concentration (SNRC). Monte Carlo simulations were used to determine the optimal threshold for different SNRC. This technique was used to analyze data from six healthy volunteers, resulting in a mean gray to white matter cerebral blood flow ratio of 2.67 ± 0.07. This value is in excellent agreement with values published in the literature. Magn Reson Med 42:167–172, 1999. © 1999 Wiley-Liss, Inc.
Power spectrum or magnitude images are frequently presented in magnetic resonance imaging. In such images, measurement of signal intensity at low signal levels is compounded with the noise. This report describes how to extract true intensity measurements in the presence of noise.
This report evaluates several methods to map relative cerebral blood flow (rCBF) by applying both parametric and nonparametric techniques to deconvolve high resolution dynamic MRI measurements of paramagnetic bolus passages with noninvasively determined arterial inputs. We found a nonparametric (singular value decomposition (SVD)) deconvolution technique produced the most robust results, giving mean gray:white flow ratio of 2.7 +/- 0.5 (SEM) in six normal volunteers, in excellent agreement with recent PET literature values for age-matched subjects. Similar results were obtained by using a model-dependent approach that assumes an exponential residue function, but not for a Gaussian-shaped residue function or for either Fourier or regularization-based model-independent approaches. Pilot studies of our CBF mapping techniques in patients with tumor, stroke, and migraine aura demonstrated that these techniques can be readily used on data routinely acquired by using current echo planar imaging technology. By using these techniques, the authors visualized important regional hemodynamic changes not detectable with rCBV mapping algorithms.
The authors review the theoretical basis of determination of cerebral blood flow (CBF) using dynamic measurements of nondiffusible contrast agents, and demonstrate how parametric and nonparametric deconvolution techniques can be modified for the special requirements of CBF determination using dynamic MRI. Using Monte Carlo modeling, the use of simple, analytical residue models is shown to introduce large errors in flow estimates when actual, underlying vascular characteristics are not sufficiently described by the chosen function. The determination of the shape of the residue function on a regional basis is shown to be possible only at high signal-to-noise ratio. Comparison of several nonparametric deconvolution techniques showed that a nonparametric deconvolution technique (singular value decomposition) allows estimation of flow relatively independent of underlying vascular structure and volume even at low signal-to-noise ratio associated with pixel-by-pixel deconvolution.
In the investigation of ischemic stroke, conventional structural magnetic resonance (MR) techniques (e.g., T1-weighted imaging, T2-weighted imaging, and proton density-weighted imaging) are valuable for the assessment of infarct extent and location beyond the first 12 to 24 hours after onset, and can be combined with MR angiography to noninvasively assess the intracranial and extracranial vasculature. However, during the critical first 6 to 12 hours, the probable period of greatest therapeutic opportunity, these methods do not adequately assess the extent and severity of ischemia. Recent developments in functional MR imaging are showing great promise for the detection of developing focal cerebral ischemic lesions within the first hours. These include (1) diffusion-weighted imaging, which provides physiologic information about the self-diffusion of water, thereby detecting one of the first elements in the pathophysiologic cascade leading to ischemic injury; and (2) perfusion imaging. The detection of acute intraparenchymal hemorrhagic stroke by susceptibility weighted MR has also been reported. In combination with MR angiography, these methods may allow the detection of the site, extent, mechanism, and tissue viability of acute stroke lesions in one imaging study. Imaging of cerebral metabolites with MR spectroscopy along with diffusion-weighted imaging and perfusion imaging may also provide new insights into ischemic stroke pathophysiology. In light of these advances in structural and functional MR, their potential uses in the study of the cerebral ischemic pathophysiology and in clinical practice are described, along with their advantages and limitations.
In six young, healthy volunteers, a novel method to determine cerebral blood flow (CBF) using magnetic resonance (MR) bolus tracking was compared with [(15)O]H2O positron emission tomography (PET). The method yielded parametric CBF images with tissue contrast in good agreement with parametric PET CBF images. Introducing a common conversion factor, MR CBF values could be converted into absolute flow rates, allowing comparison of CBF values among normal subjects.
For quantification of cerebral blood flow (CBF) using dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI), knowledge of the tissue response function is necessary. To obtain this, the tissue contrast passage measurement must be corrected for the arterial input. This study proposes an iterative maximum likelihood expectation maximization (ML-EM) algorithm for this correction, which takes into account the noise in T2- or T2*-weighted image sequences. The ML-EM algorithm does not assume a priori knowledge of the shape of the response function; it automatically corrects for arrival time offsets and inherently yields positive response values. The results on synthetic image sequences are presented, for which the recovered flow values and the response functions are in good agreement with their expectation values. The method is illustrated by calculating the gray and white matter flow in a clinical example.
To investigate additional information provided by maps of relative cerebral blood flow in functional magnetic resonance (MR) imaging of human hyperacute cerebral ischemic stroke.
Diffusion-weighted and hemodynamic MR imaging were performed in 23 patients less than 12 hours after the onset of symptoms. Maps of relative cerebral blood flow and tracer mean tissue transit time were computed, as were maps of apparent diffusion and relative cerebral blood volume. Acute lesion volumes on the maps were compared with follow-up imaging findings.
In 15 of 23 subjects (65%), blood flow maps revealed hemodynamic abnormalities not visible on blood volume maps. A mismatch between initial blood flow and diffusion findings predicted growth of infarct more often (12 of 15 subjects with infarcts that grew) than did a mismatch between initial blood volume and diffusion findings (eight of 15). However, lesion volumes on blood volume and diffusion maps correlated better with eventual infarct volumes (r > 0.90) than did those on blood flow and tracer mean transit time maps (r approximately 0.6), likely as a result of threshold effects. In eight patients, blood volume was elevated around the diffusion abnormality, suggesting a compensatory hemodynamic response.
MR imaging can delineate areas of altered blood flow, blood volume, and water mobility in hyperacute human stroke. Predictive models of tissue outcome may benefit by including computation of both relative cerebral blood flow and blood volume.
Existing model-free approaches to determine cerebral blood flow by external residue detection show a marked dependence of flow estimates on tracer arrival delays and dispersion. In theory, this dependence can be circumvented by applying a specific model of vascular transport and tissue flow heterogeneity. The authors present a method to determine flow heterogeneity by magnetic resonance residue detection of a plasma marker. Probability density functions of relative flows measured in six healthy volunteers were similar among tissue types and volunteers, and were in qualitative agreement with literature measurements of capillary red blood cell and plasma velocities. Combining the measured flow distribution with a model of vascular transport yielded excellent model fits to experimental residue data. Fitted gray-to-white flow-rate ratios were in good agreement with PET literature values, as well as a model-free singular value decomposition (SVD) method in the same subjects. The vascular model was found somewhat sensitive to data noise, but showed far less dependence on vascular delay and dispersion than the model-free SVD approach.
Singular value decomposition (SVD) is a promising deconvolution technique for use in dynamic contrast agent magnetic resonance perfusion imaging. Computer simulations, however, show that the selection of the threshold for SVD affects the accuracy of the cerebral blood flow measurements and may distort the shape of the vascular residue function. In this report, a pixel-by-pixel thresholding method is proposed based on the signal-to-noise ratio of the concentration time curve at maximum concentration (SNRC). Monte Carlo simulations were used to determine the optimal threshold for different SNRC. This technique was used to analyze data from six healthy volunteers, resulting in a mean gray to white matter cerebral blood flow ratio of 2.67 +/- 0.07. This value is in excellent agreement with values published in the literature.
Quantitative, multislice dynamic susceptibility contrast-enhanced MRI perfusion measurements were used to determine the patterns of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), and normalized first moment of the tissue deltaR2-time curve (N) in 11 subjects with carotid artery occlusion or stenosis. MTT correlated with degree of carotid stenosis, whereas a range of alterations in CBF and CBV were found presumably reflecting variables degrees of collateral flow. There was no significant correlation between MRI and SPET flow perfusion measurements, with increasing disparity between the two techniques at higher inter-hemispheric flow ratios. The effect of obtaining the arterial input function (AIF) from the middle cerebral artery (MCA) ipsilateral or contralateral to the stenosis was determined. Despite the use of an AIF from the MCA, which is distal to the circle of Willis, and hence the major sources of collateral supply, there was still some extra dispersion of the contrast agent bolus due to differences in arrival time.
Three different deconvolution techniques for quantifying cerebral blood flow (CBF) from whole brain T*(2)-weighted bolus tracking images were implemented (parametric Fourier transform P-FT, parametric single value decomposition P-SVD and nonparametric single value decomposition NP-SVD). The techniques were tested on 206 regions from 38 hyperacute stroke patients. In the P-FT and P-SVD techniques, the tissue and arterial concentration time curves were fit to a gamma variate function and the resulting CBF values correlated very well (CBF(P-FT) = 1.02 x CBF(P-SVD), r(2) = 0.96). The NP-SVD CBF values (i.e., original unfitted curves were used) correlated well with the P-FT CBF values only when a sufficient number of time series volumes were acquired to minimize tracer time curve truncation (CBF(P-FT) x 0.92 x CBF(NP-SVD), r(2) = 0.88). The correlation between the fitted CBV and the unfitted CBV values was also maximized in regions with minimal tracer time curve truncation (CBV(fit) = 1.00 x CBV(unfit), r(2) = 0.89). When a sufficient number of time series volumes could not be acquired (due to scanner limitations) to avoid tracer time curve truncation, the P-FT and P-SVD techniques gave more reliable estimates of CBF than the NP-SVD technique.
Regional cerebral blood flow (rCBF) was assessed using dynamic susceptibility-contrast MRI at 1.5 T. A simultaneous dual FLASH pulse sequence and Gd-DTPA-BMA (0.3 mmol/kg b.w.) were used for examination of 43 volunteers, measuring rCBF in frontal white matter (WM) and in gray matter in the thalamus (GM). Arterial input functions (AIFs) were registered 1) in the carotid artery and 2) in an artery within the GM/WM slice. The measured concentration-vs. -time curve was deconvolved with the AIF using both Fourier Transform (FT) and Singular Value Decomposition (SVD). Relative rCBF was given by the height of the deconvolved response curve. For each volunteer, eight different rCBF maps were calculated, representing different combinations of deconvolution techniques, AIFs, and filters. The average GM-WM rCBF ratios ranged from 2.0-2.2, depending on methodology. Absolute rCBF was 68 +/- 28 ml/(min 100 g) in GM and 35 +/- 13 ml/(min 100g) in WM (mean +/- SD, n = 39). GM-WM rCBF ratios obtained using SVD were 6-10% higher than corresponding ratios obtained using FT.
Relative regional cerebral blood flow (rCBF) at rest was measured in 44 volunteers using both dynamic susceptibility contrast (DSC) MRI and (99m)Tc-HMPAO SPECT on the same day.
In MRI, a Gd-DTPA-BMA contrast agent bolus (0.3 mmol/kg body wt) was monitored with a simultaneous dual FLASH pulse sequence (time resolution 1.5 s). MRI-based rCBF images were calculated by singular value decomposition-based deconvolution of the measured tissue concentration-time curve with an arterial input function from a small artery within the imaging slice. In the SPECT investigation, 900 MBq of (99m)Tc-HMPAO was injected intravenously. Relative rCBF in gray matter in the thalamus and in frontal white matter was determined.
The ratio of relative rCBF in gray matter to relative rCBF in white matter was 2.21 +/- 0.57 using MRI and 2.24 +/- 0.54 using SPECT (mean +/- SD).
Relative rCBF maps from DSC MRI and (99m)Tc-HMPAO SPECT showed good agreement, and the MRI-based rCBF ratio correlated with the corresponding SPECT-based ratio (r = 0.79, p < 0.0000006).
Dynamic susceptibility contrast (DSC) MRI is now increasingly used for measuring perfusion in many different applications. The quantification of DSC data requires the measurement of the arterial input function (AIF) and the deconvolution of the tissue concentration time curve. One of the most accepted deconvolution methods is the use of singular value decomposition (SVD). Simulations were performed to evaluate the effects on DSC quantification of the presence of delay and dispersion in the estimated AIF. Both delay and dispersion were found to introduce significant underestimation of cerebral blood flow (CBF) and overestimation of mean transit time (MTT). While the error introduced by the delay can be corrected by using the information of the arrival time of the bolus, the correction for the dispersion is less straightforward and requires a model for the vasculature.
Tissue signatures from acute MR imaging of the brain may be able to categorize physiological status and thereby assist clinical decision making. We designed and analyzed statistical algorithms to evaluate the risk of infarction for each voxel of tissue using acute human functional MRI.
Diffusion-weighted MR images (DWI) and perfusion-weighted MR images (PWI) from acute stroke patients scanned within 12 hours of symptom onset were retrospectively studied and used to develop thresholding and generalized linear model (GLM) algorithms predicting tissue outcome as determined by follow-up MRI. The performances of the algorithms were evaluated for each patient by using receiver operating characteristic curves.
At their optimal operating points, thresholding algorithms combining DWI and PWI provided 66% sensitivity and 83% specificity, and GLM algorithms combining DWI and PWI predicted with 66% sensitivity and 84% specificity voxels that proceeded to infarct. Thresholding algorithms that combined DWI and PWI provided significant improvement to algorithms that utilized DWI alone (P=0.02) but no significant improvement over algorithms utilizing PWI alone (P=0.21). GLM algorithms that combined DWI and PWI showed significant improvement over algorithms that used only DWI (P=0.02) or PWI (P=0.04). The performances of thresholding and GLM algorithms were comparable (P>0.2).
Algorithms that combine acute DWI and PWI can assess the risk of infarction with higher specificity and sensitivity than algorithms that use DWI or PWI individually. Methods for quantitatively assessing the risk of infarction on a voxel-by-voxel basis show promise as techniques for investigating the natural spatial evolution of ischemic damage in humans.
To compare predictors of infarct growth in hyperacute stroke from a retrospective review of various relative and quantitative parameters calculated at perfusion-weighted magnetic resonance (MR) imaging performed within 6 hours after ictus.
Fluid-attenuated inversion recovery and diffusion- and perfusion-weighted images were obtained in 66 patients. The initial infarct was delineated on diffusion-weighted images; the hemodynamic disturbance, on apparent mean transit time (MTT) maps; and the final infarct, on follow-up fluid-attenuated inversion recovery images. Relative (without and with deconvolution) and quantitative values of the bolus arrival time, time to peak (TTP), apparent MTT or MTT, cerebral blood volume (CBV), peak height, and cerebral blood flow (CBF) index or CBF were calculated for initial infarct, infarct growth (final minus initial infarct contour), viable hemodynamic disturbance (apparent MTT minus final infarct contour), and contralateral mirror regions. Univariate and multivariate analyses (receiver operating characteristic curves and discriminant analysis) were performed to compare the diagnostic performance of these parameters for predicting infarct growth.
At univariate analysis, relative peak height and quantitative CBF were the best predictors of infarct growth; at multivariate analysis, a function of peak height and TTP for relative measurements and CBF alone for quantitative measurements. Quantitative and relative measurements (without or with deconvolution) worked equally well. A combined relative peak height or TTP threshold (<54% or >5.2 seconds, respectively) had a sensitivity of 71% and a specificity of 98%. A quantitative CBF threshold (<35 mL/min/100 g) had a sensitivity of 69% and a specificity of 85%.
A combination of relative peak height and TTP measurements allowed the best prediction of infarct growth, which obviates more complex quantitative calculation.
The discrete Fourier transform. In: Dis-crete-time signal processing
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Oppenheim AV, Schafer RW. The discrete Fourier transform. In: Dis-crete-time signal processing. Englewood Cliffs, NJ: Prentice Hall;
A Schlaug G Defining a local input function for perfusion quantification with bolus contrast MRI
- D Alsop
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Alsop D, Wedmid A, Schlaug G. Defining a local input function for perfusion quantification with bolus contrast MRI [abstract]. In: Pro-ceedings of the 10th Annual Meeting of ISMRM, Honolulu, 2002.
BR Arterial input function measurement with MRI
- L Porkka
- Neuder
- R M Hunter G Weisskoff
- J W Belliveau
- Rosen
Porkka L, Neuder M, Hunter G, Weisskoff RM, Belliveau JW, Rosen BR. Arterial input function measurement with MRI. In: Proceedings of the 10th Annual Meeting of SMRM, San Francisco, 1991. p 120.
AG Perfusion calculations using singular value decomposition are biased by tracer arrival timing
- O Wu
- Østergaard
- B R Benner T Rosen
- R M Weisskoff
- Sorensen
Wu O, Østergaard L, Benner T, Rosen BR, Weisskoff RM, Sorensen AG. Perfusion calculations using singular value decomposition are biased by tracer arrival timing [abstract]. In: Proceedings of the 10th Annual Meeting of ISMRM, Honolulu, 2002.
Whole brain quantitative CBF and CBV measurements using MRI bolus tracking: comparison of methodologies
- Smith
Delay and dispersion effects in dynamic susceptibility contrast MRI: simulations using singular value decomposition
- Calamante
Hyperacute stroke: simultaneous measurement of relative cerebral blood volume, relative cerebral blood flow, and mean tissue transit time
- Sorensen
Maximum likelihood estimation of cerebral blood flow in dynamic susceptibility contrast MRI
- Vonken
Perfusion calculations using singular value decomposition are biased by tracer arrival timing[abstract
- Wuo Østergaardl Bennert Rosenbr Weisskoffrm
- Sorensenag
Defining a local input function for perfusion quantification with bolus contrast MRI[abstract
- Alsopd Wedmida Schlaugg