Larrabee: a many-core x86 architecture for visual computing
ACM SIGGRAPH 2008 papers
Auto-tuning 3-D FFT library for CUDA GPUs
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Fast, robust total variation-based reconstruction of noisy, blurred images
IEEE Transactions on Image Processing
A multi-GPU programming library for real-time applications
ICA3PP'12 Proceedings of the 12th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
GPU-based iterative transmission reconstruction in 3D ultrasound computer tomography
Journal of Parallel and Distributed Computing
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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.