title: GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.
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.
authors.affiliation: Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
authors.forename: Yu-Han H
authors.initials: YH
authors.lastname: Hsu
authors.affiliation: Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America.
authors.forename: Ziyin
authors.initials: Z
authors.lastname: Huang
authors.affiliation: Early Development Pharmacokinetics and Pharmacodynamics, Genentech, South San Francisco, CA, United States of America.
authors.forename: Gregory Z
authors.initials: GZ
authors.lastname: Ferl
authors.affiliation: Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States of America; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
authors.forename: Chee M
authors.initials: CM
authors.lastname: Ng
mesh_terms: D000465:Algorithms; D020533:Angiogenesis Inhibitors / Q000627:therapeutic use*; D000068258:Bevacizumab / Q000627:therapeutic use*; D001932:Brain Neoplasms / Q000175:diagnosis / Q000188:drug therapy*; D003201:Computers; D005909:Glioblastoma / Q000175:diagnosis / Q000188:drug therapy*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008954:Models, Biological
chemical_list: D020533:Angiogenesis Inhibitors; D000068258:Bevacizumab