GPU-Accelerated Compartmental Modeling Analysis of DCE-MRI Data from Glioblastoma Patients Treated with Bevacizumab

<div><p>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.</p></div

Time performance of analyzing the GPU-accelerated method developed in this study versus a CPU-based methods with the same analysis steps.

<p>Time performance of analyzing the GPU-accelerated method developed in this study versus a CPU-based methods with the same analysis steps.</p

Flow chart of the analysis steps executed by the MATLAB script.

<p>Step done on the GPU is in bold font.</p

Compartment models for Gd-DTPA kinetics.

<p>The schematic shows the kinetics relationship between the tumor tissue (right box) and the remainder of the body (center and left box). Drug input (<i>I</i>) goes into arterial blood in compartment 1 (center box, volume: <i>V</i>_<i>1</i>), which exchanges with a general peripheral compartment 2 (left box, volume: <i>V</i>_<i>2</i>) via fractional clearances <i>k</i>_<i>12</i> and <i>k</i>_<i>21</i> and eliminates drug via <i>k</i>_<i>10</i>. The tumor (and other brain tissue) is represented by 2 compartments: compartment 3 (top of right box, volume: <i>V</i>_<i>3</i>) exchanges with compartment 1 via <i>k</i>_<i>13</i> and <i>k</i>_<i>30</i>; compartment 4 (bottom of right box, volume: <i>V</i>_<i>4</i>) exchanges with compartment 1 so rapidly that its drug concentration is practically the same as that of compartment 1.</p

Novel and previously identified BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in African ancestry discovery and replication samples, and European ancestry replication samples.

<p>Novel and previously identified BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in African ancestry discovery and replication samples, and European ancestry replication samples.</p

Additional novel BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in sex-stratified analyses of African ancestry discovery and replication samples.

<p>Additional novel BMI and WHR_adjBMI loci at <i>P</i> < 5×10⁻⁸ in sex-stratified analyses of African ancestry discovery and replication samples.</p