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.
Highlights
Cerebral perfusion describes the steady-state delivery of nutrients and oxygen via blood to the brain tissue parenchyma, and it comprises several cerebral hemodynamic parameters, i.e., cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), which play important roles in the diagnosis and management of many diseases [1,2,3,4,5]
Compared with the two previously reported clustering methods, agglomerative hierarchy (AH) clustering method produced a larger area under each curve (AUC), higher peak, narrower fullwidth half maximum (FWHM), and a lower trend line at the tail, which demonstrate that AH method was affected less by Partial volume effect (PVE) during arterial input function (AIF) detection [29,30,31].TP occurred earlier with AH clustering method than fuzzy c-means (FCM) and k-means algorithms, which shows that AH method was affected less by tracer transport delays [30]
The difference in FWHM was significant between AH and FCM methods (P, 0.05),but not between AH and k-means clustering methods (P. 0.05).There were significant differences in both the AUC and M values between AH and FCM clustering, and between AH and kmeans clustering (P,0.05).These results indicate that AH algorithm can obtain a more accurate AIF than each of the other two methods [29,30]
Summary
Cerebral perfusion describes the steady-state delivery of nutrients and oxygen via blood to the brain tissue parenchyma, and it comprises several cerebral hemodynamic parameters, i.e., cerebral blood flow (CBF), cerebral blood volume (CBV), and mean transit time (MTT), which play important roles in the diagnosis and management of many diseases [1,2,3,4,5]. Several imaging techniques are used to analyze cerebral perfusion, including positron emission tomography, single photon emission computed tomography, and CT. The technique of dynamic susceptibility contrast (DSC) using an intravascular contrast agent is applied most frequently to the quantification of cerebral hemodynamics [10]. This method monitors the signal changes induced by the paramagnetic contrast agent as it passes through cerebral vessels and a series of T2- or T2*-weighted images are acquired over time [7,11,16]
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