Dataset Information

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.

SUBMITTER: Hsu YH

PROVIDER: S-EPMC4364976 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

Hsu Yu-Han H YH Huang Ziyin Z Ferl Gregory Z GZ Ng Chee M CM

PloS one 20150318 3

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 bevacizuma ...[more]

PMID: 25786263

Similar Datasets

Project description:PurposeTo investigate the ability of potential imaging biomarkers based on 18F-AlF-NOTA-PRGD2 positron emission tomography/computed tomography (18F-RGD PET/CT) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) imaging to predict the response to bevacizumab combined with conventional therapy in postoperative newly diagnosed glioblastoma.MethodsTwenty patients with newly diagnosed with glioblastoma after surgery were prospectively enrolled to receive bevacizumab plus conventional concurrent radiotherapy and temozolomide (CCRT). 18F-RGD PET/CT and DCE-MRI were performed at baseline, week 3, and week 10 for each patient. Statistical methods included the analysis of variance (ANOVA), Kaplan-Meier method and Cox proportional hazard analysis.ResultsAll patients completed CCRT plus bevacizumab therapy without interruption. The median follow-up time was 33.9 months (95% confidence interval [CI], 28.3-39.5 months). The median progression-free survival (PFS) and overall survival (OS) was 9.66 months (95% CI, 6.20-13.12 months) and 15.89 months (95% CI, 13.89-17.78), respectively. Treatment was generally well tolerated, and there were no Treatment emergent adverse events (TEAEs) with a toxicity grade equal to or exceeding 3 or that led to termination of treatment or patient death.Over the treatment interval of bevacizumab therapy from week 3 to week 10, patients with a large decrease of SUVmean was associated with a better PFS with a hazard ratio (HR) of 6.562, 95% CI (1.318-32.667), p=0.022. According to Kaplan-Meier analysis, patients with a decrease in the SUVmean of more than 0.115 on 18F-RGD PET/CT had a longer PFS than those with a decrease in the SUVmean of 0.115 or less (12.25 months vs.7.46 months, p=0.009). For OS, only a small decrease of Ktrans was also found to have certain prognostic value (HR=0.986, 95% CI (0.975-0.998), p=0.023). Patients with a decrease in Ktrans larger than 37.03 (min-1) on DCE-MRI had worse OS than those with a decrease in Ktrans of 37.03 (min-1) or less (15.93 months vs. 26.42 months, p=0.044).Conclusion18F-RGD PET/CT and DCE-MRI may be valuable in evaluating the response of glioblastoma to treatment with the combination of bevacizumab and CCRT, with a greater decrease in SUVmean predicting better PFS as well as a small decrease in Ktrans predicting improved OS.

Project description:Many scientific problems such as classifier training or medical image reconstruction can be expressed as minimization of differentiable real-valued cost functions and solved with iterative gradient-based methods. Adjoint algorithmic differentiation (AAD) enables automated computation of gradients of such cost functions implemented as computer programs. To backpropagate adjoint derivatives, excessive memory is potentially required to store the intermediate partial derivatives on a dedicated data structure, referred to as the "tape". Parallelization is difficult because threads need to synchronize their accesses during taping and backpropagation. This situation is aggravated for many-core architectures, such as Graphics Processing Units (GPUs), because of the large number of light-weight threads and the limited memory size in general as well as per thread. We show how these limitations can be mediated if the cost function is expressed using GPU-accelerated vector and matrix operations which are recognized as intrinsic functions by our AAD software. We compare this approach with naive and vectorized implementations for CPUs. We use four increasingly complex cost functions to evaluate the performance with respect to memory consumption and gradient computation times. Using vectorization, CPU and GPU memory consumption could be substantially reduced compared to the naive reference implementation, in some cases even by an order of complexity. The vectorization allowed usage of optimized parallel libraries during forward and reverse passes which resulted in high speedups for the vectorized CPU version compared to the naive reference implementation. The GPU version achieved an additional speedup of 7.5 ± 4.4, showing that the processing power of GPUs can be utilized for AAD using this concept. Furthermore, we show how this software can be systematically extended for more complex problems such as nonlinear absorption reconstruction for fluorescence-mediated tomography.

Dataset Information

GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

Publications

GPU-accelerated compartmental modeling analysis of DCE-MRI data from glioblastoma patients treated with bevacizumab.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets