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International Journal of Biomedical Imaging
Volume 2011 (2011), Article ID 473128, 11 pages
Research Article

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

1Parallel Computing Lab, Intel Corporation, 2200 Mission College Boulevard Santa Clara, CA 95054, USA
2The Center for Advanced Imaging Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA

Received 30 March 2011; Accepted 3 June 2011

Academic Editor: Yasser M. Kadah

Copyright © 2011 Daehyun Kim et al. 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.


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.