A control point interpolation method for the non-parametric quantification of cerebral haemodynamics from dynamic susceptibility contrast MRI

Institute of Biomedical Engineering, University of Oxford, United Kingdom. Electronic address: .
NeuroImage (Impact Factor: 6.13). 09/2012; 64C:560-570. DOI: 10.1016/j.neuroimage.2012.08.083
Source: PubMed

ABSTRACT DSC-MRI analysis is based on tracer kinetic theory and typically involves the deconvolution of the MRI signal in tissue with an arterial input function (AIF), which is an ill-posed inverse problem. The current standard singular value decomposition (SVD) method typically underestimates perfusion and introduces non-physiological oscillations in the resulting residue function. An alternative vascular model (VM) based approach permits only a restricted family of shapes for the residue function, which might not be appropriate in pathologies like stroke. In this work a novel deconvolution algorithm is presented that can estimate both perfusion and residue function shape accurately without requiring the latter to belong to a specific class of functional shapes. A control point interpolation (CPI) method is proposed that represents the residue function by a number of control points (CPs), each having two degrees of freedom (in amplitude and time). A complete residue function shape is then generated from the CPs using a cubic spline interpolation. The CPI method is shown in simulation to be able to estimate cerebral blood flow (CBF) with greater accuracy giving a regression coefficient between true and estimated CBF of 0.96 compared to 0.83 for VM and 0.71 for the circular SVD (oSVD) method. The CPI method was able to accurately estimate the residue function over a wide range of simulated conditions. The CPI method has also been demonstrated on clinical data where a marked difference was observed between the residue function of normally appearing brain parenchyma and infarcted tissue. The CPI method could serve as a viable means to examine the residue function shape under pathological variations.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The microvasculature plays a vital part in the cardiovascular system. Any impairment to its function can lead to significant pathophysiological effects, particularly in organs such as the brain where there is a very tight coupling between structure and function. However, it is extremely difficult to quantify the health of the microvasculature in vivo, other than by assessing perfusion, using techniques such as arterial spin labelling. Recent work has suggested that the flow distribution within a voxel could also be a valuable measure. This can also be measured clinically, but as yet has not been related to the properties of the microvasculature due to the difficulties in modelling and characterizing these strongly inter-connected networks. In this paper, we present a new technique for characterizing an existing physiologically accurate model of the cerebral microvasculature in terms of its residue function. A new analytical mathematical framework for calculation of the residue function, based on the mass transport equation, of any arbitrary network is presented together with results from simulations. We then present a method for characterizing this function, which can be directly related to clinical data, and show how the resulting parameters are affected under conditions of both reduced perfusion and reduced network density. It is found that the residue function parameters are affected in different ways by these two effects, opening up the possibility of using such parameters, when acquired from clinical data, to infer information about both the network properties and the perfusion distribution. These results open up the possibility of obtaining valuable clinical information about the health of the microvasculature in vivo, providing additional tools to clinicians working in cerebrovascular diseases, such as stroke and dementia.
    Interface focus: a theme supplement of Journal of the Royal Society interface 04/2013; 3(2):20120078. DOI:10.1098/rsfs.2012.0078 · 3.12 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: PurposeTo propose a new clustering method for the automatic detection of arterial input function (AIF) with high accuracy in dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI).Materials and MethodsA novel method for automatically determining the AIF was proposed to facilitate the analysis of MR perfusion, which relied on normalized cut (Ncut) clustering. Its performance was compared with those of two other previously reported clustering methods: k-means and fuzzy c-means (FCM) techniques, in terms of the detection accuracy and computational time. Both simulated perfusion data and data collected from 42 healthy human subjects were applied to investigate the feasibility of the proposed approach.ResultsIn the simulation study, the partial volume effect (PVE) level, peak value (PV), time to peak (TTP), full width at half maximum (FWHM), area under AIF curve (AUC), root mean square error (RMSE) between the estimated AIF and true AIF, and M value given by [PV/(FWHM×TTP)] were 45.45, 4.2737, 29.92, 6.4563, 76.4836, 0.0519, and 0.0221 for Ncut-based AIF, 96.45, 3.8385, 31.74, 7.5133, 75.7364, 0.3295, and 0.0161 for FCM-based AIF, 91.18, 3.8990, 31.73, 7.4544, 76.0476, 0.3128, and 0.0165 for k-means-based AIF, 0, 4.4592, 29.51, 6.2016, 76.8669, 0, and 0.0244 for true AIF. In the clinical study, the mean PV, TTP, FWHM, AUC, M, error between estimated AIF and manual AIF were 1.7395, 30.95, 5.5923, 19.1081, 0.0397, and 0.4406 for Ncut-based AIF, 1.3629, 31.31, 6.8616, 17.9992, 0.0123, and 0.0846 for k-means-based AIF, 1.2101, 31.61, 7.1729, 16.6238, 0.0102, and 0.1016 for FCM-based AIF. The differences in PV, M, FWHM, and error reached a significant level (P = 0.032, 0.010, 0.003, and 0.002, respectively) between Ncut and k-means methods as well as between Ncut and FCM methods (P = 0.013, 0.008, 0.007, and 0.009, respectively). There was no significant difference in TTP between Ncut and each of the other two methods (P = 0.173 and 0.097, respectively). For AUC, a significant difference was found between Ncut and FCM algorithms (P = 0.025), but not between Ncut and k-means methods (P = 0.138). The mean execution time was 0.4406 for the Ncut method, 0.2649 for the k-means method, and 0.1371 for the FCM method, and the differences were significant both between Ncut and k-means methods (P = 0.002) and between Ncut and FCM methods (P = 0.004).Conclusion Ncut clustering yield AIFs more in line with the expected AIF, and might be preferred to FCM and k-means clustering methods sensitive to randomly selected initial centers. J. Magn. Reson. Imaging 2014. © 2014 Wiley Periodicals, Inc.
    Journal of Magnetic Resonance Imaging 04/2014; 41(4). DOI:10.1002/jmri.24642 · 2.79 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: During dynamic susceptibility contrast-magnetic resonance imaging (DSC-MRI), it has been demonstrated that the arterial input function (AIF) can be obtained using fuzzy c-means (FCM) and k-means clustering methods. However, due to the dependence on the initial centers of clusters, both clustering methods have poor reproducibility between the calculation and recalculation steps. To address this problem, the present study developed an alternative clustering technique based on the agglomerative hierarchy (AH) method for AIF determination. The performance of AH method was evaluated using simulated data and clinical data based on comparisons with the two previously demonstrated clustering-based methods in terms of the detection accuracy, calculation reproducibility, and computational complexity. The statistical analysis demonstrated that, at the cost of a significantly longer execution time, AH method obtained AIFs more in line with the expected AIF, and it was perfectly reproducible at different time points. In our opinion, the disadvantage of AH method in terms of the execution time can be alleviated by introducing a professional high-performance workstation. The findings of this study support the feasibility of using AH clustering method for detecting the AIF automatically.
    PLoS ONE 06/2014; 9(6):e100308. DOI:10.1371/journal.pone.0100308 · 3.53 Impact Factor