کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
469525 698324 2015 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
GPU-based parallel group ICA for functional magnetic resonance data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
GPU-based parallel group ICA for functional magnetic resonance data
چکیده انگلیسی


• A parallel group ICA (PGICA) on GPU was proposed for fMRI data analysis.
• PGICA demonstrated significant speedup in comparison with serial group ICA.
• PGICA demonstrated comparable accuracy of functional networks detection.

The goal of our study is to develop a fast parallel implementation of group independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data using graphics processing units (GPU). Though ICA has become a standard method to identify brain functional connectivity of the fMRI data, it is computationally intensive, especially has a huge cost for the group data analysis. GPU with higher parallel computation power and lower cost are used for general purpose computing, which could contribute to fMRI data analysis significantly. In this study, a parallel group ICA (PGICA) on GPU, mainly consisting of GPU-based PCA using SVD and Infomax-ICA, is presented. In comparison to the serial group ICA, the proposed method demonstrated both significant speedup with 6–11 times and comparable accuracy of functional networks in our experiments. This proposed method is expected to perform the real-time post-processing for fMRI data analysis.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computer Methods and Programs in Biomedicine - Volume 119, Issue 1, April 2015, Pages 9–16
نویسندگان
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