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Overcoming spatio-temporal limitations using dynamically scaled in vitro PC-MRI — A flow field comparison to true-scale computer simulations of idealized, stented and patient-specific left main bifurcations
Abstract
The majority of patients with angina or heart failure have coronary artery disease. Left main bifurcations are particularly susceptible to pathological narrowing. Flow is a major factor of atheroma development, but limitations in imaging technology such as spatio-temporal resolution, signal-to-noise ratio (SNRv), and imaging artefacts prevent in vivo investigations. Computational fluid dynamics (CFD) modelling is a common numerical approach to study flow, but it requires a cautious and rigorous application for meaningful results. Left main bifurcation angles of 40°, 80° and 110° were found to represent the spread of an atlas based 100 computed tomography angiograms. Three left mains with these bifurcation angles were reconstructed with 1) idealized, 2) stented, and 3) patient-specific geometry. These were then approximately 7× scaled-up and 3D printing as large phantoms. Their flow was reproduced using a blood-analogous, dynamically scaled steady flow circuit, enabling in vitro phase-contrast magnetic resonance (PC-MRI) measurements. After threshold segmentation the image data was registered to true-scale CFD of the same coronary geometry using a coherent point drift algorithm, yielding a small covariance error (σ(2) <;5.8×10(-4)). Natural-neighbour interpolation of the CFD data onto the PC-MRI grid enabled direct flow field comparison, showing very good agreement in magnitude (error 2-12%) and directional changes (r(2) 0.87-0.91), and stent induced flow alternations were measureable for the first time. PC-MRI over-estimated velocities close to the wall, possibly due to partial voluming. Bifurcation shape determined the development of slow flow regions, which created lower SNRv regions and increased discrepancies. These can likely be minimised in future by testing different similarity parameters to reduce acquisition error and improve correlation further. It was demonstrated that in vitro large phantom acquisition correlates to true-scale coronary flow simulations when dynamically scaled, and thus can overcome current PC-MRI's spatio-temporal limitations. This novel method enables experimental assessment of stent induced flow alternations, and in future may elevate CFD coronary flow simulations by providing sophisticated boundary conditions, and enable investigations of stenosis phantoms.
... While steady-state efforts are promising [5], and even enable stent-induced flow detection when dynamically scaled [93], transient tests remain unsuccessful [20]. ...
... MRI's limited spatio-temporal resolution inhibits detailed noninvasive flow acquisition. The use of in vitro steady-state studies can eliminate temporal challenges [19], and dynamically scaling such experiments can provide higher effective spatial resolution [5], making even small flow patterns accessible, such as stent-induced changes [93]. ...
This chapter discusses coronary artery flow assessment for atherosclerosis investigations. The overall goal is to foster the reader’s understanding of coronary flow assessment with CFD and experimental MRI, including advantages, shortcomings, and potential for clinical applicability. In Section 1 , we begin by introducing coronary artery disease and how it links to local blood flow and hemodynamic parameters, before introducing strategies to investigating coronary flow for risk assessment—computational modeling and experimental studies. Both of these need the artery geometry and embedded stents to be retrieved first, as detailed in Section 2 . Section 3 details the concepts of computational coronary flow modeling with computational fluid dynamics (CFD) including the governing equations, mesh discretization, and boundary and initial conditions. Section 4 introduces experimental approaches using in vitro flow sensitive magnetic resonance imaging (MRI), including dynamic scaling for steady or transient state considerations, creation of phantom, consideration of vessel compliance and motion, non-Newtonian blood properties, and the design of an experimental circuit. Postprocessing, analysis, and comparison of both methods are explained in Section 5 , before discussion of the accuracy and reliability of the results in Section 6 . Finally, current developments, particularly patient-specific profiling, are discussed in Section 7 .
The coronary artery, which divides into the left coronary artery and the right coronary artery, is the first branch of the ascending aorta. The left coronary artery is a short trunk that originates from the left aortic sinus. It passes through the beginning of the pulmonary artery and the left atrial appendage and travels 3–5 mm along the coronary sulcus to the front of the left side of the heart. It is immediately divided into the anterior descending branch and the circumflex branch. The anterior descending branch descends along the anterior interventricular sulcus, bypassing the apical notch to the diaphragm of the heart, and anastomoses with the posterior interventricular branch of the right coronary artery. The right coronary artery originates from the right aortic sinus, travels along the right coronary sulcus between the root of the pulmonary artery and the right auricle, bypasses the right edge of the heart, continues along the coronary sulcus of the diaphragm, and projects into the posterior descending branch near the atrioventricular junction, that is, the posterior interventricular branch.
Abstract The Insight Toolkit (ITK) is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with a MRI scan in order to combine the information contained in both. ITK is implemented in C++. It is cross-platform, using a build environment known as CMake to manage the compilation process in a platform-independent way. In addition, an automated wrapping process (Cable) generates interfaces between C++ and interpreted programming languages such as Tcl, Java, and Python …
In this study, wall motion analysis of common carotid artery has been done to classify images as normal or abnormal. B mode transverse ultrasound images of common carotid artery have been obtained for 100 healthy subjects and 100 subjects with known cardiovascular complaints. The diastolic and systolic blood pressures of subjects were also measured before and after image acquisition and are averaged. The quality of the images has been enhanced by image normalization process. Block matching technique was used to track the movement of the carotid wall. The displacements in x and y directions are obtained and are averaged over three cardiac cycles. The stiffness indices have been obtained for all subjects from the displacements and blood pressure. The mean stiffness for different age groups of the healthy volunteers vary from 4.5 to 7.6. For subjects with cardiac complaints, the mean stiffness index vary from 8.01 to 18.86 for different age groups. The back propagation algorithm has been used to train neural network to classify the subjects as normal or abnormal.
Stent induced hemodynamic changes in the coronary arteries are associated with higher risk of adverse clinical outcome. The purpose of this study was to evaluate the impact of stent design on wall shear stress (WSS), time average WSS, and WSS gradient (WSSG), in idealized stent geometries using computational fluid dynamics. Strut spacing, thickness, luminal protrusion, and malapposition were systematically investigated and a comparison made between two commercially available stents (Omega and Biomatrix). Narrower strut spacing led to larger areas of adverse low WSS and high WSSG but these effects were mitigated when strut size was reduced, particularly for WSSG. Local hemodynamics worsened with luminal protrusion of the stent and with stent malapposition, adverse high WSS and WSSG were identified around peak flow and throughout the cardiac cycle respectively. For the Biomatrix stent, the adverse effect of thicker struts was mitigated by greater strut spacing, radial cell offset and flow-aligned struts. In conclusion, adverse hemodynamic effects of specific design features (such as strut size and narrow spacing) can be mitigated when combined with other hemodynamically beneficial design features but increased luminal protrusion can worsen the stent's hemodynamic profile significantly.
The continuous integration of innovative imaging modalities into conventional vascular surgery rooms has led to an urgent need for computer assistance solutions that support the smooth integration of imaging within the surgical workflow. In particular, endovascular interventions performed under 2D fluoroscopic or angiographic imaging only, require reliable and fast navigation support for complex treatment procedures such as endovascular aortic repair. Despite the vast variety of image-based guide wire and catheter tracking methods, an adoption of these for detecting and tracking the stent graft delivery device is not possible due to its special geometry and intensity appearance.
In this paper, we present, for the first time, the automatic detection and tracking of the stent graft delivery device in 2D fluoroscopic sequences on the fly. The proposed approach is based on the robust principal component analysis and extends the conventional batch processing towards an online tracking system that is able to detect and track medical devices on the fly.
The proposed method has been tested on interventional sequences of four different clinical cases. In the lack of publicly available ground truth data, we have further initiated a crowd sourcing strategy that has resulted in 200 annotations by unexperienced users, 120 of which were used to establish a ground truth dataset for quantitatively evaluating our algorithm. In addition, we have performed a user study amongst our clinical partners for qualitative evaluation of the results.
Although we calculated an average error in the range of nine pixels, the fact that our tracking method functions on the fly and is able to detect stent grafts in all unfolding stages without fine-tuning of parameters has convinced our clinical partners and they all agreed on the very high clinical relevance of our method.
In standard B-mode ultrasound (BMUS), segmentation of the lumen of atherosclerotic carotid arteries and studying the lumen geometry over time are difficult owing to irregular lumen shapes, noise, artifacts, and echolucent plaques. Contrast enhanced ultrasound (CEUS) improves lumen visualization, but lumen segmentation remains challenging owing to varying intensities, CEUS-specific artifacts and lack of tissue visualization. To overcome these challenges, we propose a novel method using simultaneously acquired BMUS&CEUS image sequences. Initially, the method estimates nonrigid motion (NME) from the image sequences, using intensity-based image registration. The motion-compensated image sequence is then averaged to obtain a single 'epitome' image with improved signal-to-noise ratio. The lumen is segmented from the epitome image through an intensity joint-histogram classification and a graph-based segmentation. NME was validated by comparing displacements with manual annotations in eleven carotids. The average root-mean-squareerror (RMSE) was 112 73 μm. Segmentation results were validated against manual delineations in the epitome images of two different datasets, respectively containing eleven (RMSE 191 43 μm) and ten (RMSE 351 176 μm) carotids. From the deformation fields, we derived arterial distensibility with values comparable to the literature. The average errors in all experiments were in the inter-observer variability range. To the best of our knowledge, this is the first study exploiting combined BMUS&CEUS images for atherosclerotic carotid lumen segmentation.
Background and purpose:
Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms.
Materials and methods:
A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated.
Results:
We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles.
Conclusions:
The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.
Dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) is used to track the first pass of an exogenous, paramagnetic, nondiffusible contrast agent through brain tissue, and has emerged as a powerful tool in the characterization of brain tumor hemodynamics. DSC-MRI parameters can be helpful in many aspects, including tumor grading, prediction of treatment response, likelihood of malignant transformation, discrimination between tumor recurrence and radiation necrosis, and differentiation between true early progression and pseudoprogression. This review aims to provide a conceptual overview of the underlying principles of DSC-MRI of the brain for clinical neuroradiologists, scientists, or students wishing to improve their understanding of the technical aspects, pitfalls, and controversies of DSC perfusion MRI of the brain. Future consensus on image acquisition parameters and postprocessing of DSC-MRI will most likely allow this technique to be evaluated and used in high-quality multicenter studies and ultimately help guide clinical care.
Carotid and coronary vascular incidents are mostly caused by vulnerable plaques. Detection and characterization of vulnerable plaques are important for early disease diagnosis and treatment. For this purpose, the echomorphology and composition have been studied. Several distributions have been used to describe ultrasonic data depending on tissues, acquisition conditions, and equipment. Among them, the Rayleigh distribution is a one-parameter model used to describe the raw envelope RF ultrasound signal for its simplicity, whereas the Nakagami distribution (a generalization of the Rayleigh distribution) is the two-parameter model which is commonly accepted. However, it fails to describe B-mode images or Cartesian interpolated or subsampled RF images because linear filtering changes the statistics of the signal. In this work, a gamma mixture model (GMM) is proposed to describe the subsampled/interpolated RF images and it is shown that the parameters and coefficients of the mixture are useful descriptors of speckle pattern for different types of plaque tissues. This new model outperforms recently proposed probabilistic and textural methods with respect to plaque description and characterization of echogenic contents. Classification results provide an overall accuracy of 86.56% for four classes and 95.16% for three classes. These results evidence the classifier usefulness for plaque characterization. Additionally, the classifier provides probability maps according to each tissue type, which can be displayed for inspecting local tissue composition, or used for automatic filtering and segmentation.
To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour's local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points' expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.
Blood-brain barrier damage, which can be quantified by measuring vascular permeability, is a potential predictor for hemorrhagic transformation in acute ischemic stroke. Permeability is commonly estimated by applying Patlak analysis to computed tomography (CT) perfusion data, but this method lacks precision. Applying more elaborate kinetic models by means of nonlinear regression (NLR) may improve precision, but is more time consuming and therefore less appropriate in an acute stroke setting. We propose a simplified NLR method that may be faster and still precise enough for clinical use. The aim of this study is to evaluate the reliability of in total 12 variations of Patlak analysis and NLR methods, including the simplified NLR method. Confidence intervals for the permeability estimates were evaluated using simulated CT attenuation-time curves with realistic noise, and clinical data from 20 patients. Although fixating the blood volume improved Patlak analysis, the NLR methods yielded significantly more reliable estimates, but took up to 12 × longer to calculate. The simplified NLR method was ∼4 × faster than other NLR methods, while maintaining the same confidence intervals (CIs). In conclusion, the simplified NLR method is a new, reliable way to estimate permeability in stroke, fast enough for clinical application in an acute stroke setting.Journal of Cerebral Blood Flow & Metabolism advance online publication, 24 July 2013; doi:10.1038/jcbfm.2013.122.
The extraction and analysis of the aortic arch in chest com- puted tomography (CT) data can be an important preliminary step for the diagnosis and treatment planning of e.g. lung cancer. We here present a new method for automatic aortic arch extraction and detection of the main arterial branchings that may serve as segmentation seeds or as land- marks for intra- and interpatient registration of the mediastinum. Our method, which is based on Hough and Euclidean distance transforms and probability weighting, works on both contrast enhanced and non- contrast CT. A comparison to data manually extracted from 40 cases shows its robustness at an acceptable overall runtime.
We present a prototype software to virtually deploy pipeline flow diverting stents in intracranial vessels with aneurysms. The final objective is to understand the biological and biomechanical mechanisms underlying the stent-induced clot formation within the aneurysm. The weaving pattern of the selected pipeline stent is described w.r.t. cylindrical coordinates. Patient-specific 3D vascular geometry is extracted by a level-set image segmentation algorithm. A deformable cylindrical simplex mesh model is used to simulate the virtual stent positioning. A continuous Right Generalized Cylinder model is then used to obtain the mapping of the stent geometry onto the deformed cylindrical surface. Meshes representing the vessel/aneurysm surface and the filaments of the stent are used as input to simulate the fluid dynamics without and with the virtual stent. Lattice Boltzmann method is used to solve the equations. We show preliminary visual results on a first patient.
Automatic aorta segmentation in thoracic computed tomography (CT) scans is important for aortic calcification quantification and to guide the segmentation of other central vessels. We propose an aorta segmentation algorithm consisting of an initial boundary detection step followed by 3D level set segmentation for refinement. Our algorithm exploits aortic cross-sectional circularity: we first detect aorta boundaries with a circular Hough transform on axial slices to detect ascending and descending aorta regions, and we apply the Hough transform on oblique slices to detect the aortic arch. The centers and radii of circles detected by Hough transform are fitted to smooth cubic spline functions using least-squares fitting. From these center and radius spline functions, we reconstruct an initial aorta surface using the Frenet frame. This reconstructed tubular surface is further refined with 3D level set evolutions. The level set framework we employ optimizes a functional that depends on both edge strength and smoothness terms and evolves the surface to the position of nearby edge location corresponding to the aorta wall. After aorta segmentation, we first detect the aortic calcifications with thresholding applied to the segmented aorta region. We then filter out the false positive regions due to nearby high intensity structures. We tested the algorithm on 45 CT scans and obtained a closest point mean error of 0.52 ± 0.10 mm between the manually and automatically segmented surfaces. The true positive detection rate of calcification algorithm was 0.96 over all CT scans.
We present Spotlight, an automated user guidance technique for improving quality and efficiency of interactive segmentation tasks. Spotlight augments interactive segmentation algorithms by automatically highlighting areas in need of attention to the user during the interaction phase. We employ a 3D Livewire algorithm as our base segmentation method where the user quickly provides a minimal initial contour seeding. The quality of the initial segmentation is then evaluated based on three different metrics that probe the contour edge strength, contour stability and object connectivity. The result of this evaluation is fed into a novel algorithm that autonomously suggests regions that require user intervention. Essentially, Spotlight flags potentially problematic image regions in a prioritized fashion based on an optimization process for improving the final 3D segmentation. We present a variety of qualitative and quantitative examples demonstrating Spotlight’s intuitive use and proven utility in reducing user input by increasing automation. 1
To compare the long-term outcome after percutaneous coronary intervention with 'new-generation' drug-eluting stents (n-DES) to 'older generation' DES (o-DES), and bare-metal stents (BMS) in a real-world population.
We evaluated 94 384 consecutive stent implantations (BMS, n = 64 631; o-DES, n = 19 202; n-DES, n = 10 551) in Sweden from November 2006 to October 2010. All cases of definite stent thrombosis (ST) and restenosis were documented in the Swedish Coronary Angiography and Angioplasty Registry (SCAAR). Older generation DES were classified as: Cypher and Cypher Select (Cordis Corporation, Miami, FL, USA), Taxus Express and Taxus Liberté (Boston Scientific Corporation), and Endeavor (Medtronic Inc.) and n-DES as: Endeavor Resolute (Medtronic Inc.), XienceV, Xience Prime (Abbott Laboratories) and Promus, Promus Element (Boston Scientific Corporation). The Cox regression analyses unadjusted and adjusted for clinical and angiographic covariates showed a statistically significant lower risk of restenosis in n-DES compared with BMS [adjusted hazard ratio (HR) 0.29; 95% confidence interval (CI): 0.25-0.33] and o-DES (HR 0.62; 95% CI: 0.53-0.72). A lower risk of definite ST was found in n-DES compared with BMS (HR 0.38; 95% CI: 0.28-0.52) and o-DES (HR, 0.57; 95% CI: 0.41-0.79). The risk of death was significantly lower in n-DES compared with o-DES (adjusted HR: 0.77; 95% CI: 0.63-0.95) and BMS (adjusted HR: 0.55; 95% CI: 0.46-0.67).
Percutaneous coronary intervention with n-DES is associated with a 38% lower risk of clinically meaningful restenosis, a 43% lower risk of definite ST, and a 23% lower risk of death compared with o-DES in this observational study from a large real-world population.
Aortic calcification has been shown to be related to cardiovascular disease. In this paper, we present a novel method for localization and segmentation of thoracic aorta in non- contrast CT images using dynamic programming concepts to detect and quantify aortic calcium. The localization and segmentation of the aorta are formulated as optimal path detection problems, which are solved using dynamic programming principles. We apply these methods on Hough space for aorta localization and a transformed polar coordinate space for aorta segmentation. We evaluate the proposed approach by comparing it with the manual annotations in terms of aorta location, boundary distance, and volume overlap.
Accurate aortic arch quantification is important for diagnosis and treatment of cardiovascular diseases. We introduce a new
approach for the quantification of the aortic arch morphology with improved computational efficiency which combines 3D model-based
segmentation with intensity-based image registration. The performance of the approach has been evaluated based on 3D synthetic
images and clinically relevant 3D CTA images including pathologies. We also performed a quantitative comparison with a previous
approach.
In the current clinical workflow of endovascular abdominal aortic repairs (EVAR) a stent graft is inserted into the aneurysmatic aorta under 2D angiographic imaging. Due to the missing depth information in the X-ray visualization, it is highly difficult in particular for junior physicians to place the stent graft in the preoperatively defined position within the aorta. Therefore, advanced 3D visualization of stent grafts is highly required. In this paper, we present a novel algorithm to automatically match a 3D model of the stent graft to an intraoperative 2D image showing the device. By automatic preprocessing and a global-to-local registration approach, we are able to abandon user interaction and still meet the desired robustness. The complexity of our registration scheme is reduced by a semi-simultaneous optimization strategy incorporating constraints that correspond to the geometric model of the stent graft. Via experiments on synthetic, phantom, and real interventional data, we are able to show that the presented method matches the stent graft model to the 2D image data with good accuracy.
We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only. Here, we frame interactive 3D image segmentation as a classification problem and incorporate active learning in order to alleviate the user from choosing where to provide interactive input. Specifically, we evaluate a given segmentation by constructing an "uncertainty field" over the image domain based on boundary, regional, smoothness and entropy terms. We then calculate and highlight the plane of maximal uncertainty in a batch query step. The user can proceed to guide the labeling of the data on the query plane, hence actively providing additional training data where the classifier has the least confidence. We validate our method against random plane selection showing an average DSC improvement of 10% in the first five plane suggestions (batch queries). Furthermore, our user study shows that our method saves the user 64% of their time, on average.
Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.
Accurate quantification of the morphology of vessels is important for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for the quantification of the aortic arch morphology that combines 3D model-based segmentation with elastic image registration. The performance of the approach has been evaluated using 3D synthetic images and clinically relevant 3D CTA images including pathologies. We also performed a comparison with a previous approach.
The finite element (FE) method is a powerful investigative tool in the field of biomedical engineering, particularly in the analysis of medical devices such as coronary stents whose performance is extremely difficult to evaluate in vivo. In recent years, a number of FE studies have been carried out to simulate the deployment of coronary stents, and the results of these studies have been utilised to assess and optimise the performance of these devices. The aim of this paper is to provide a thorough review of the state-of-the-art research in this area, discussing the aims, methods and conclusions drawn from a number of significant studies. It is intended that this paper will provide a valuable reference for future research in this area.
In computed tomography fiducial markers are frequently used to obtain accurate point correspondences for further processing. These markers typically cause metal artefacts, decreasing image quality of the subsequent reconstruction and are therefore often removed from the projection data. The placement of such markers is usually done on a surface, separating two materials, e.g. skin and air. Hence, a correct restoration of the occluded area is difficult. In this work six state-of-the-art interpolation techniques for the removal of high-density fiducial markers from cone-beam CT projection data are compared. We conducted a qualitative and quantitative evaluation for the removal of such markers and the ability to reconstruct the adjoining edge. Results indicate that an iterative spectral deconvolution is best suited for this application, showing promising results in terms of edge, as well as noise restoration.
An abdominal aortic aneurysm (AAA) is a pathological dilation of the abdominal aorta that may lead to a rupture with fatal consequences. Endovascular aneurysm repair (EVAR) is a minimally invasive surgical procedure consisting of the deployment and fixation of a stent-graft that isolates the damaged vessel wall from blood circulation. The technique requires adequate endovascular device sizing, which may be performed by vascular analysis and quantification on Computerized Tomography Angiography (CTA) scans. This paper presents a novel 3D CTA image-based software for AAA inspection and EVAR sizing, eVida Vascular, which allows fast and accurate 3D endograft sizing for standard and fenestrated endografts. We provide a description of the system and its innovations, including the underlying vascular image analysis and visualization technology, functional modules and user interaction. Furthermore, an experimental validation of the tool is described, assessing the degree of agreement with a commercial, clinically validated software, when comparing measurements obtained for standard endograft sizing in a group of 14 patients.
Copyright © 2015 Elsevier Ltd. All rights reserved.
Describing the detailed statistical anatomy of the coronary artery tree is important for determining the aetiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.
Objectives:
The objective of this work is to present the software tool ANTONIA, which has been developed to facilitate a quantitative analysis of perfusion-weighted MRI (PWI) datasets in general as well as the subsequent multi-parametric analysis of additional datasets for the specific purpose of acute ischemic stroke patient dataset evaluation.
Methods:
Three different methods for the analysis of DSC or DCE PWI datasets are currently implemented in ANTONIA, which can be case-specifically selected based on the study protocol. These methods comprise a curve fitting method as well as a deconvolution-based and deconvolution-free method integrating a previously defined arterial input function. The perfusion analysis is extended for the purpose of acute ischemic stroke analysis by additional methods that enable an automatic atlas-based selection of the arterial input function, an analysis of the perfusion-diffusion and DWI-FLAIR mismatch as well as segmentation-based volumetric analyses.
Results:
For reliability evaluation, the described software tool was used by two observers for quantitative analysis of 15 datasets from acute ischemic stroke patients to extract the acute lesion core volume, FLAIR ratio, perfusion-diffusion mismatch volume with manually as well as automatically selected arterial input functions, and follow-up lesion volume. The results of this evaluation revealed that the described software tool leads to highly reproducible results for all parameters if the automatic arterial input function selection method is used.
Conclusion:
Due to the broad selection of processing methods that are available in the software tool, ANTONIA is especially helpful to support image-based perfusion and acute ischemic stroke research projects.
Pulmonary insufficiency (PI) can render the right ventricle dysfunctional due to volume overloading and hypertrophy. The treatment requires a pulmonary valve replacement surgery. However, determining the right time for the valve replacement surgery has been difficult with currently employed clinical techniques such as, echocardiography and cardiac MRI. Therefore, there is a clinical need to improve the diagnosis of PI by using patient-specific (PS) hemodynamic endpoints. While there are many reported studies on the use of PS geometry with time varying boundary conditions (BC) for hemodynamic computation, few use spatially varying PS velocity measurement at each time point of the cardiac cycle. In other words, the gap is that, there are limited number of studies which implement both spatially- and time-varying physiologic BC directly with patient specific geometry. The uniqueness of this research is in the incorporation of spatially varying PS velocity data obtained from phase-contrast MRI (PC-MRI) at each time point of the cardiac cycle with PS geometry obtained from angiographic MRI. This methodology was applied to model the complex developing flow in human pulmonary artery (PA) distal to pulmonary valve, in a normal and a subject with PI.
Although stenting is the most commonly performed procedure for the treatment of coronary atherosclerotic lesions, in-stent restenosis (ISR) remains one of the most serious clinical complications. An important stimulus to ISR is the altered hemodynamics with abnormal shear stresses on endothelial cells generated by the stent presence. Computational fluid dynamics is a valid tool for studying the local hemodynamics of stented vessels, allowing the calculation of the wall shear stress (WSS), which is otherwise not directly possible to be measured in vivo. However, in these numerical simulations the arterial wall and the stent are considered rigid and fixed, an assumption that may influence the WSS and flow patterns. Therefore, the aim of this work is to perform fluid-structure interaction (FSI) analyses of a stented coronary artery in order to understand the effects of the wall compliance on the hemodynamic quantities. Two different materials are considered for the stent: cobalt-chromium (CoCr) and poly-l-lactide (PLLA). The results of the FSI and the corresponding rigid-wall models are compared, focusing in particular on the analysis of the WSS distribution. Results showed similar trends in terms of instantaneous and time-averaged WSS between compliant and rigid-wall cases. In particular, the difference of percentage area exposed to TAWSS lower than 0.4Pa between the CoCr FSI and the rigid-wall cases was about 1.5% while between the PLLA cases 1.0%. The results indicate that, for idealized models of a stented coronary artery, the rigid-wall assumption for fluid dynamic simulations appears adequate when the aim of the study is the analysis of near-wall quantities like WSS.
Cerebral perfusion, also referred to as cerebral blood flow (CBF), is one of the most important parameters related to brain physiology and function. The technique of dynamic-susceptibility contrast (DSC) MRI is currently the most commonly used MRI method to measure perfusion. It relies on the intravenous injection of a contrast agent and the rapid measurement of the transient signal changes during the passage of the bolus through the brain. Central to quantification of CBF using this technique is the so-called arterial input function (AIF), which describes the contrast agent input to the tissue of interest. Due to its fundamental role, there has been a lot of progress in recent years regarding how and where to measure the AIF, how it influences DSC-MRI quantification, what artefacts one should avoid, and the design of automatic methods to measure the AIF. The AIF is also directly linked to most of the major sources of artefacts in CBF quantification, including partial volume effect, bolus delay and dispersion, peak truncation effects, contrast agent non-linearity, etc. While there have been a number of good review articles on DSC-MRI over the years, these are often comprehensive but, by necessity, with limited in-depth discussion of the various topics covered. This review article covers in greater depth the issues associated with the AIF and their implications for perfusion quantification using DSC-MRI.
Introduction and objectivesCarotid intima-media thickness as measured with ultrasonography is an inexpensive and noninvasive predictor of cardiovascular events. The objectives of this study were to determine the population reference ranges of carotid intima-media thickness for individuals aged 35-84 years in Spain and to analyze the association of carotid intima-media thickness with cardiovascular risk factors (age, smoking, diabetes, pulse pressure, lipid profile, and body mass index).Methods
Population-based cross-sectional study conducted in Gerona (Spain). We described the mean and maximal values of carotid intima-media thickness of the carotid artery and of its 3 segments (common carotid, carotid bulb and internal carotid). We assessed cardiovascular risk factors and analyzed their association with carotid intima-media thickness using adjusted linear regression models.ResultsA total of 3161 individuals (54% women) were included, with mean age 58 years. Men showed significantly higher mean common carotid intima-media thickness than did women (0.71 vs 0.67 mm). The strongest predictors of this measure were age (coefficients for 10-year increase: 0.65 and 0.58 for women and men, respectively), smoking in men (coefficient: 0.26), high-density lipoprotein cholesterol in women (coefficient for 10 mg/dL, increase: −0.08) and pulse pressure in both sexes (coefficients for 10 mmHg increase: 0.08 and 0.23 for women and men, respectively). The results were similar for the mean carotid intima-media thickness of all the segments.Conclusions
This population-based study presents the reference ranges for carotid intima-media thickness in the Spanish population. The main determinants of carotid intima-media thickness were age and pulse pressure in both sexes.Full English text available from:www.revespcardiol.org
Objectives:
In recent years, the use of computer-based techniques has been advocated to improve intima-media thickness (IMT) quantification and its reproducibility. The purpose of this study was to test the diagnostic performance of a new IMT automated algorithm, CARES 3.0, which is a patented class of IMT measurement systems called AtheroEdge (AtheroPoint, LLC, Roseville, CA).
Methods:
From 2 different institutions, we analyzed the carotid arteries of 250 patients. The automated CARES 3.0 algorithm was tested versus 2 other automated algorithms, 1 semiautomated algorithm, and a reader reference to assess the IMT measurements. Bland-Altman analysis, regression analysis, and the Student t test were performed.
Results:
CARES 3.0 showed an IMT measurement bias ± SD of -0.022 ± 0.288 mm compared with the expert reader. The average IMT by CARES 3.0 was 0.852 ± 0.248 mm, and that of the reader was 0.872 ± 0.325 mm. In the Bland-Altman plots, the CARES 3.0 IMT measurements showed accurate values, with about 80% of the images having an IMT measurement bias ranging between -50% and +50%. These values were better than those of the previous CARES releases and the semiautomated algorithm. Regression analysis showed that, among all techniques, the best t value was between CARES 3.0 and the reader.
Conclusions:
We have developed an improved fully automated technique for carotid IMT measurement on longitudinal ultrasound images. This new version, called CARES 3.0, consists of a new heuristic for lumen-intima and media-adventitia detection, which showed high accuracy and reproducibility for IMT measurement.
In biological tissues such as bone, cell function and activity crucially depend on the physical properties of the extracellular matrix which the cells synthesize and condition. During bone formation and remodeling, osteoblasts get embedded into the matrix they deposit and differentiate to osteocytes. These cells form a dense network throughout the entire bone material. Osteocytes are known to orchestrate bone remodeling. However, the precise role of osteocytes during mineral homeostasis and their potential influence on bone material quality remains unclear. To understand the mutual influence of osteocytes and extracellular matrix, it is crucial to reveal their network organization in relation to the properties of their surrounding material. Here we visualize and topologically quantify the osteocyte network in mineralized bone sections with confocal laser scanning microscopy. At the same region of the sample, synchrotron small angle x-ray scattering is used to determine nanoscopic bone mineral particle size and arrangement relative to the cell network. Major findings are that most of the mineral particles reside within less than a micrometer from the nearest cell network channel and that mineral particle characteristics depend on the distance from the cell network. The architecture of the network reveals optimization with respect to transport costs between cells and to blood vessels. In conclusion, these findings quantitatively show that the osteocyte network provides access to a huge mineral reservoir in bone due to its dense organization. The observed correlation between the architecture of osteocyte networks and bone material properties supports the hypothesis that osteocytes interact with their mineralized vicinity and thus, participate in bone mineral homeostasis. © 2013 American Society for Bone and Mineral Research.
Stents are playing an increasing role in the treatment of arterial stenoses and aneurysms. The goal of this work is to help the clinician in the pre-operative choice of the stent's length and diameter. This is done by embedding a model of the stent within a real vascular 3D image. Two models are used. First, a simple geometrical model, composed of a set of circles or polygons stacked along the vessel's centerline, is used to simulate the introduction and the deployment of the stent. Second, a simplex-mesh model with an adapted cylindrical constraint is used to represent the stent surface. Another axially constrained simplex-mesh deformable model is used to reconstruct the 3D vessel wall. We simulate the interaction between the vessel wall and the stent by imposing that the model of the vessel locally fit the shape of the deployed-stent model. Preliminary quantitative results of the vessel reconstruction accuracy are given.
This work explores the segmentation of the intima-media complex (IMC) of the common carotid artery (CCA) wall for the evaluation of the intima media thickness (IMT) on B-mode ultrasound images. The IMT provides important clinical information for the evaluation of the risk of developing atherosclerosis. The algorithm begins with speckle removal, which is followed by the use of a Hough transform for boundary detection and image normalization. The corresponding results provide the initial statistical information needed for a Markov random field (MRF) segmentation. The method lends itself to the development of a fully automatic method for the delineation of the IMC. The mean and standard deviation of the automatically segmented results are 0.7855 and 0.1738 mm and the corresponding value for the ground truth IMT are 0.7959 and 0.1875 mm. The Wilcoxon rank sum test shows no significant differences. Future work will investigate the proposed method using a larger number of tissue classes and on more subjects.
The gamma variate function has been often used to describe the dispersion of a bolus as it passes through a series of compartments. For this reason, it is frequently chosen to fit first-pass data in studies quantifying cardiac output and left-to-right cardiac shunts. Although the gamma variate is an appropriate function to model these situations, it has several undesirable mathematical properties. Changes in the alpha and beta parameters affect not only the rise and fall times of the function, but also change the location and magnitude of the function maximum. This makes it difficult to anticipate how the function will be altered by varying the parameters and often requires an additional renormalization step when the gamma variate is being used to fit a curve. A different but entirely equivalent form of the gamma variate is derived in which these problems are eliminated.
The knowledge of local vascular anatomy and function in the human body is of high interest for the diagnosis and treatment of cardiovascular disease. A comprehensive analysis of the hemodynamics in the thoracic aorta is presented based on the integration of flow-sensitive 4D MRI with state-of-the-art rapid prototyping technology and computational fluid dynamics (CFD). Rapid prototyping was used to transform aortic geometries as measured by contrast-enhanced MR angiography into realistic vascular models with large anatomical coverage. Integration into a flow circuit with patient-specific pulsatile in-flow conditions and application of flow-sensitive 4D MRI permitted detailed analysis of local and global 3D flow dynamics in a realistic vascular geometry. Visualization of characteristic 3D flow patterns and quantitative comparisons of the in vitro experiments with in vivo data and CFD simulations in identical vascular geometries were performed to evaluate the accuracy of vascular model systems. The results indicate the potential of such patient-specific model systems for detailed experimental simulation of realistic vascular hemodynamics. Further studies are warranted to examine the influence of refined boundary conditions of the human circulatory system such as fluid-wall interaction and their effect on normal and pathological blood flow characteristics associated with vascular geometry. Magn Reson Med 59:535–546, 2008. © 2008 Wiley-Liss, Inc.
Both 4D flow-sensitive MRI and computational fluid dynamics (CFD) have successfully been applied to analyze complex 3D flow
patterns in the cardiovascular system. However, both modalities suffer from limitations related to spatiotemporal resolution,
measurement errors, and noise (MRI) or incomplete model assumptions and boundary conditions (CFD). The aim of this study was
to directly compare the results of 4D flow-sensitive MRI and CFD in a simple model system in vitro and in complex models of
the thoracic aorta in vivo. By comparing both modalities within a single framework, discrepancies were observed but the overall
patterns were coherent. If adequate methods are used (e.g., patient-specific boundary conditions, fine boundary layer mesh),
CFD can compute very accurate flow and vessel wall parameters, such as wall shear stress (WSS). The combination of 4D flow-sensitive
MRI and CFD can be used to refine both methodologies, which may help to enhance the assessment and understanding of blood
flow in vivo.
Keywords4D flow-sensitive MRI-CFD-Hemodynamics-Blood flow
This paper presents two methods to measure aneurysms and stenosis, and introduces a method for visualizing models of tube- and Y-stents virtually placed into preoperative CT-data. The measurement algorithms obtain characteristic dimensions of a vessel disease used to select a proper stent. A physical simulation of the forces interacting between stent and vessel walls allows the prediction of the stent shape after being expanded in the artery. For both measurement and simulation, our methods are based on active contours (ACM). Generating an initial contour requires the segmentation of the vascular structures followed by centerline determination. Starting from this centerline, the initial contours are constructed and fitted to the vessel walls. The paper presents the results of measurements and stent simulations for different clinical images (AAA, TAA, iliac aneurysm and carotid stenosis)
Automated quantification of the morphology of the aortic arch is crucial for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for fully automatic segmentation and characterization of the aortic arch morphology for endovascular aortic repair. Supraaortic branches are detected based on an analysis of the connected components within a spherical volume around the vessel. Segmentation and quantification is based on a 3D parametric intensity model that is iteratively fitted to the image intensities and includes a fast and robust scheme for initialization. The performance of the approach has been evaluated using synthetic and real 3D CTA images.
We describe a new sequential learning scheme called "stacked sequential learning". Stacked se- quential learning is a meta-learning algorithm, in which an arbitrary base learner is augmented so as make it aware of the labels of nearby exam- ples. We evaluate the method on several "sequen- tial partitioning problems", which are characterized by long runs of identical labels. We demonstrate that on these problems, sequential stacking consis- tently improves the performance of non-sequential base learners; that sequential stacking often im- proves performance of learners (such as CRFs) that are designed specifically for sequential tasks; and that a sequentially stacked maximum-entropy learner generally outperforms CRFs.
In this paper, an effective method is developed to extract coronary vessels from X-ray digital subtraction angiography (DSA). Unlike the traditional image subtraction technique, we used the independent component analysis (ICA) to separate the coronary vessels and backgrounds. Experimental results indicate that the proposed method can achieve better performance than the traditional subtraction technique and the segmentation method.
Using generic interpolation machinery based on solving Poisson equations, a variety of novel tools are introduced for seamless editing of image regions. The first set of tools permits the seamless importation of both opaque and transparent source image regions into a destination region. The second set is based on similar mathematical ideas and allows the user to modify the appearance of the image seamlessly, within a selected region. These changes can be arranged to affect the texture, the illumination, and the color of objects lying in the region, or to make tileable a rectangular selection.
Common carotid artery intima-media thickness (IMT), which is usually measured upon ultrasound images, is an important indicator to cardiovascular diseases. This paper proposes a snake model based segmentation method to automatically detect the boundary of intima-media for IMT measurement. In the proposed method, two contours are initialized from line segments generated by Hough transform and then evolved simultaneously by dual snake model for boundary detection. Experimental results show that the proposed method has strong robustness against ultrasound artifacts, gives better results than traditional snake model and dynamic programming based methods, and achieves similar clinical parameters to ground truth data.
Diffusion-perfusion mismatch can be used to identify acute stroke patients that could benefit from reperfusion therapies. Early assessment of the mismatch facilitates necessary diagnosis and treatment decisions in acute stroke. We developed the RApid processing of PerfusIon and Diffusion (RAPID) for unsupervised, fully automated processing of perfusion and diffusion data for the purpose of expedited routine clinical assessment. The RAPID system computes quantitative perfusion maps (cerebral blood volume, CBV; cerebral blood flow, CBF; mean transit time, MTT; and the time until the residue function reaches its peak, T(max)) using deconvolution of tissue and arterial signals. Diffusion-weighted imaging/perfusion-weighted imaging (DWI/PWI) mismatch is automatically determined using infarct core segmentation of ADC maps and perfusion deficits segmented from T(max) maps. The performance of RAPID was evaluated on 63 acute stroke cases, in which diffusion and perfusion lesion volumes were outlined by both a human reader and the RAPID system. The correlation of outlined lesion volumes obtained from both methods was r(2) = 0.99 for DWI and r(2) = 0.96 for PWI. For mismatch identification, RAPID showed 100% sensitivity and 91% specificity. The mismatch information is made available on the hospital's PACS within 5-7 min. Results indicate that the automated system is sufficiently accurate and fast enough to be used for routine care as well as in clinical trials.
This paper proposes scalable and fast algorithms for solving the Robust PCA
problem, namely recovering a low-rank matrix with an unknown fraction of its
entries being arbitrarily corrupted. This problem arises in many applications,
such as image processing, web data ranking, and bioinformatic data analysis. It
was recently shown that under surprisingly broad conditions, the Robust PCA
problem can be exactly solved via convex optimization that minimizes a
combination of the nuclear norm and the $\ell^1$-norm . In this paper, we apply
the method of augmented Lagrange multipliers (ALM) to solve this convex
program. As the objective function is non-smooth, we show how to extend the
classical analysis of ALM to such new objective functions and prove the
optimality of the proposed algorithms and characterize their convergence rate.
Empirically, the proposed new algorithms can be more than five times faster
than the previous state-of-the-art algorithms for Robust PCA, such as the
accelerated proximal gradient (APG) algorithm. Moreover, the new algorithms
achieve higher precision, yet being less storage/memory demanding. We also show
that the ALM technique can be used to solve the (related but somewhat simpler)
matrix completion problem and obtain rather promising results too. We further
prove the necessary and sufficient condition for the inexact ALM to converge
globally. Matlab code of all algorithms discussed are available at
http://perception.csl.illinois.edu/matrix-rank/home.html