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Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures
PURPOSE: The non‐uniform fast Fourier transform (NUFFT) involves interpolation of non‐uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non‐local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347498/ https://www.ncbi.nlm.nih.gov/pubmed/30329181 http://dx.doi.org/10.1002/mrm.27497 |
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author | Smith, David S. Sengupta, Saikat Smith, Seth A. Brian Welch, E. |
author_facet | Smith, David S. Sengupta, Saikat Smith, Seth A. Brian Welch, E. |
author_sort | Smith, David S. |
collection | PubMed |
description | PURPOSE: The non‐uniform fast Fourier transform (NUFFT) involves interpolation of non‐uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non‐local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non‐Cartesian acquisition methods. METHODS: Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for TRajectory Optimized NUFFT. We benchmark the speed and accuracy TRON on a Shepp‐Logan phantom and on whole‐body continuous golden‐angle radial MRI. RESULTS: TRON was 6–30× faster than the closest competitor, depending on test data set, and was the most accurate code tested. CONCLUSIONS: Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at http://github.com/davidssmith/TRON. |
format | Online Article Text |
id | pubmed-6347498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63474982019-03-01 Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures Smith, David S. Sengupta, Saikat Smith, Seth A. Brian Welch, E. Magn Reson Med Full Papers—Computer Processing and Modeling PURPOSE: The non‐uniform fast Fourier transform (NUFFT) involves interpolation of non‐uniformly sampled Fourier data onto a Cartesian grid, an interpolation that is slowed by complex, non‐local data access patterns. A faster NUFFT would increase the clinical relevance of the plethora of advanced non‐Cartesian acquisition methods. METHODS: Here we customize the NUFFT procedure for a radial trajectory and GPU architecture to eliminate the bottlenecks encountered when allowing for arbitrary trajectories and hardware. We call the result TRON, for TRajectory Optimized NUFFT. We benchmark the speed and accuracy TRON on a Shepp‐Logan phantom and on whole‐body continuous golden‐angle radial MRI. RESULTS: TRON was 6–30× faster than the closest competitor, depending on test data set, and was the most accurate code tested. CONCLUSIONS: Specialization of the NUFFT algorithm for a particular trajectory yielded significant speed gains. TRON can be easily extended to other trajectories, such as spiral and PROPELLER. TRON can be downloaded at http://github.com/davidssmith/TRON. John Wiley and Sons Inc. 2018-10-17 2019-03 /pmc/articles/PMC6347498/ /pubmed/30329181 http://dx.doi.org/10.1002/mrm.27497 Text en © 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Full Papers—Computer Processing and Modeling Smith, David S. Sengupta, Saikat Smith, Seth A. Brian Welch, E. Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title | Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title_full | Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title_fullStr | Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title_full_unstemmed | Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title_short | Trajectory optimized NUFFT: Faster non‐Cartesian MRI reconstruction through prior knowledge and parallel architectures |
title_sort | trajectory optimized nufft: faster non‐cartesian mri reconstruction through prior knowledge and parallel architectures |
topic | Full Papers—Computer Processing and Modeling |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6347498/ https://www.ncbi.nlm.nih.gov/pubmed/30329181 http://dx.doi.org/10.1002/mrm.27497 |
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