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Journal article
GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab
PloS one, v 10(3), pp e0118421-e0118421
2015
PMID: 25786263
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab
- Creators
- Yu-Han H Hsu - Children's Hospital of PhiladelphiaZiyin Huang - Children's Hospital of PhiladelphiaGregory Z Ferl - GenentechChee M Ng - Children's Hospital of PhiladelphiaZhuoran Huang - WELL Center
- Publication Details
- PloS one, v 10(3), pp e0118421-e0118421
- Publisher
- Public LIbrary of Science (PLOS)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Center for Weight, Eating and Lifestyle Science (WELL) [Historical]
- Web of Science ID
- WOS:000352138500032
- Scopus ID
- 2-s2.0-84925729459
- Other Identifier
- 991019173530004721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- Web of Science research areas
- Radiology, Nuclear Medicine & Medical Imaging