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.
展开▼
机译：压缩感测（CS）描述了如何从比奈奎斯特标准所需的更少的样本中准确地重建稀疏信号。由于MRI扫描持续时间与获取的样本数量成正比，因此CS在MRI中引起了极大的关注。但是，CS重建的计算量很大，因此无法在常规临床实践中使用它们。在这项工作中，我们研究了不同的面向吞吐量的体系结构如何使一种CS算法受益，以及在不同的现代平台上可行的加速程度。我们证明，在NVIDIA Tesla C2050 GPU上运行的基于CUDA的代码可以在19秒内通过8通道采集重建256×160×80的体积，这本身就是对现有技术水平的重大改进。然后，我们证明英特尔的Knights Ferry可以在12秒内完成相同的3D MRI重建，使CS方法更接近于临床可行性。
展开▼
