High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures
Compressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationa...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2011-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2011/473128 |
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| _version_ | 1832566206252449792 |
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| author | Daehyun Kim Joshua Trzasko Mikhail Smelyanskiy Clifton Haider Pradeep Dubey Armando Manduca |
| author_facet | Daehyun Kim Joshua Trzasko Mikhail Smelyanskiy Clifton Haider Pradeep Dubey Armando Manduca |
| author_sort | Daehyun Kim |
| collection | DOAJ |
| description | Compressive sensing (CS) describes how sparse
signals can be accurately reconstructed from many fewer samples
than required by the Nyquist criterion. Since MRI scan duration
is proportional to the number of acquired samples, CS has been
gaining significant attention in MRI. However, the computationally
intensive nature of CS reconstructions has precluded their
use in routine clinical practice. In this work, we investigate how
different throughput-oriented architectures can benefit one CS
algorithm and what levels of acceleration are feasible on different
modern platforms. We demonstrate that a CUDA-based code
running on an NVIDIA Tesla C2050 GPU can reconstruct a
256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds,
which is in itself a significant improvement over the state of the art. We then
show that Intel's Knights Ferry can perform the same 3D MRI
reconstruction in only 12 seconds, bringing CS methods even
closer to clinical viability. |
| format | Article |
| id | doaj-art-5aadbbe74f2948c0bc4e8cdac8c6feff |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2011-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-5aadbbe74f2948c0bc4e8cdac8c6feff2025-02-03T01:04:45ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962011-01-01201110.1155/2011/473128473128High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core ArchitecturesDaehyun Kim0Joshua Trzasko1Mikhail Smelyanskiy2Clifton Haider3Pradeep Dubey4Armando Manduca5Parallel Computing Lab, Intel Corporation, 2200 Mission College Boulevard Santa Clara, CA 95054, USAThe Center for Advanced Imaging Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USAParallel Computing Lab, Intel Corporation, 2200 Mission College Boulevard Santa Clara, CA 95054, USAThe Center for Advanced Imaging Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USAParallel Computing Lab, Intel Corporation, 2200 Mission College Boulevard Santa Clara, CA 95054, USAThe Center for Advanced Imaging Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USACompressive sensing (CS) describes how sparse signals can be accurately reconstructed from many fewer samples than required by the Nyquist criterion. Since MRI scan duration is proportional to the number of acquired samples, CS has been gaining significant attention in MRI. However, the computationally intensive nature of CS reconstructions has precluded their use in routine clinical practice. In this work, we investigate how different throughput-oriented architectures can benefit one CS algorithm and what levels of acceleration are feasible on different modern platforms. We demonstrate that a CUDA-based code running on an NVIDIA Tesla C2050 GPU can reconstruct a 256 × 160 × 80 volume from an 8-channel acquisition in 19 seconds, which is in itself a significant improvement over the state of the art. We then show that Intel's Knights Ferry can perform the same 3D MRI reconstruction in only 12 seconds, bringing CS methods even closer to clinical viability.http://dx.doi.org/10.1155/2011/473128 |
| spellingShingle | Daehyun Kim Joshua Trzasko Mikhail Smelyanskiy Clifton Haider Pradeep Dubey Armando Manduca High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures International Journal of Biomedical Imaging |
| title | High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures |
| title_full | High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures |
| title_fullStr | High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures |
| title_full_unstemmed | High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures |
| title_short | High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures |
| title_sort | high performance 3d compressive sensing mri reconstruction using many core architectures |
| url | http://dx.doi.org/10.1155/2011/473128 |
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