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High-Performance 3D Compressive Sensing MRI Reconstruction Using Many-Core Architectures

Daehyun Kim, Joshua Trzasko, Mikhail Smelyanskiy, Clifton Haider, Pradeep Dubey, Armando Manduca
Parallel Computing Lab, Intel Corporation, 2200 Mission College Boulevard Santa Clara, CA 95054, USA
International Journal of Biomedical Imaging, Volume 2011, Article ID 473128, 11 pages, 2011

@article{kim2011high,

   title={},

   author={},

   year={2011}

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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.
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