Abstract
The compartment model analysis using medical imaging data is the well-established but extremely time consuming technique for quantifying the changes in microvascular physiology of targeted organs in clinical patients after antivascular therapies. In this paper, we present a first graphics processing unit-accelerated method for compartmental modeling of medical imaging data. Using this approach, we performed the analysis of dynamic contrast-enhanced magnetic resonance imaging data from bevacizumab-treated glioblastoma patients in less than one minute per slice without losing accuracy. This approach reduced the computation time by more than 120-fold comparing to a central processing unit-based method that performed the analogous analysis steps in serial and more than 17-fold comparing to the algorithm that optimized for central processing unit computation. The method developed in this study could be of significant utility in reducing the computational times required to assess tumor physiology from dynamic contrast-enhanced magnetic resonance imaging data in preclinical and clinical development of antivascular therapies and related fields.
Highlights
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive quantitative tool that allows analysis of tumor vascular characteristics that might change in response to drug treatments without the use of ionizing radiation
DCE-MRI scans taken one day before and one day after administration of a single 10mg/kg bevacizumab dose in a glioblastoma patient were used to assess the performance of the graphics processing unit (GPU)-accelerated kinetic analysis method implemented in this study
Results generated from the GPU script were compared to results of a central processing unit (CPU) script that performs the same analysis steps in serial to verify that parallelization on the GPU does not alter the outcome of the analysis method: most of the GPU and CPU results (99.9%) are very similar and show less than 5% difference
Summary
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a noninvasive quantitative tool that allows analysis of tumor vascular characteristics that might change in response to drug treatments without the use of ionizing radiation. With DCE-MRI, vascular properties of tumors such as vascular permeability, rate of perfusion, and vascular leakage space can be quantified using standard MRI imaging system in routine clinical practice [1,2,3]. Two tumor properties, fractional interstitial volume (ve) and fractional transfer rate (Ktrans), are commonly derived and used to assess drug effects on tumor While this kinetic modeling approach to analyze DCE-MRI data is straightforward in concept, it is often very time consuming and computationally expensive, as model fitting has to be performed separately on each voxel in a DCE-MRI image, requiring tens of thousands of minimization function calls. When a dataset contains multiple DCE-MRI scans from many patients, with multiple slices in each scan, the analysis time cost may become a significant burden in a fast-paced clinical or research environment
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