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CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking

Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking...

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Detalles Bibliográficos
Autores principales: van Aart, Evert, Sepasian, Neda, Jalba, Andrei, Vilanova, Anna
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176496/
https://www.ncbi.nlm.nih.gov/pubmed/21941525
http://dx.doi.org/10.1155/2011/698908
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author van Aart, Evert
Sepasian, Neda
Jalba, Andrei
Vilanova, Anna
author_facet van Aart, Evert
Sepasian, Neda
Jalba, Andrei
Vilanova, Anna
author_sort van Aart, Evert
collection PubMed
description Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking algorithm using a Graphics Processing Unit (GPU). This algorithm, which is based on the calculation of geodesics, has shown promising results for both synthetic and real data, but is limited in its applicability by its high computational requirements. We present a solution which uses the parallelism offered by modern GPUs, in combination with the CUDA platform by NVIDIA, to significantly reduce the execution time of the fiber-tracking algorithm. Compared to a multithreaded CPU implementation of the same algorithm, our GPU mapping achieves a speedup factor of up to 40 times.
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spelling pubmed-31764962011-09-22 CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking van Aart, Evert Sepasian, Neda Jalba, Andrei Vilanova, Anna Int J Biomed Imaging Research Article Diffusion Tensor Imaging (DTI) allows to noninvasively measure the diffusion of water in fibrous tissue. By reconstructing the fibers from DTI data using a fiber-tracking algorithm, we can deduce the structure of the tissue. In this paper, we outline an approach to accelerating such a fiber-tracking algorithm using a Graphics Processing Unit (GPU). This algorithm, which is based on the calculation of geodesics, has shown promising results for both synthetic and real data, but is limited in its applicability by its high computational requirements. We present a solution which uses the parallelism offered by modern GPUs, in combination with the CUDA platform by NVIDIA, to significantly reduce the execution time of the fiber-tracking algorithm. Compared to a multithreaded CPU implementation of the same algorithm, our GPU mapping achieves a speedup factor of up to 40 times. Hindawi Publishing Corporation 2011 2011-09-20 /pmc/articles/PMC3176496/ /pubmed/21941525 http://dx.doi.org/10.1155/2011/698908 Text en Copyright © 2011 Evert van Aart et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
van Aart, Evert
Sepasian, Neda
Jalba, Andrei
Vilanova, Anna
CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title_full CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title_fullStr CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title_full_unstemmed CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title_short CUDA-Accelerated Geodesic Ray-Tracing for Fiber Tracking
title_sort cuda-accelerated geodesic ray-tracing for fiber tracking
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3176496/
https://www.ncbi.nlm.nih.gov/pubmed/21941525
http://dx.doi.org/10.1155/2011/698908
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