摘要：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.
