Name
Title | ||
|---|---|---|
More IMPATIENT: A Gridding-Accelerated Toeplitz-based Strategy for Non-Cartesian High-Resolution 3D MRI on GPUs. |
Abstract | ||
|---|---|---|
Several recent methods have been proposed to obtain significant speed-ups in MRI image reconstruction by leveraging the computational power of GPUs. Previously, we implemented a GPU-based image reconstruction technique called the Illinois Massively Parallel Acquisition Toolkit for Image reconstruction with ENhanced Throughput in MRI (IMPATIENT MRI) for reconstructing data collected along arbitrary 3D trajectories. In this paper, we improve IMPATIENT by removing computational bottlenecks by using a gridding approach to accelerate the computation of various data structures needed by the previous routine. Further, we enhance the routine with capabilities for off-resonance correction and multi-sensor parallel imaging reconstruction. Through implementation of optimized gridding into our iterative reconstruction scheme, speed-ups of more than a factor of 200 are provided in the improved GPU implementation compared to the previous accelerated GPU code. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jpdc.2013.01.001 | J. Parallel Distrib. Comput. |
Keywords | Field | DocType |
gridding approach,mri image reconstruction,gpu,non-cartesian,cuda,iterative reconstruction scheme,toeplitz,multi-sensor parallel imaging reconstruction,gpu-based image reconstruction technique,image reconstruction,improved gpu implementation,gridding,impatient mri,mri,computational bottleneck,non-cartesian high-resolution,computational power,gridding-accelerated toeplitz-based strategy,bioinformatics,biomedical research | Iterative reconstruction,Data structure,Computer science,CUDA,Massively parallel,Parallel computing,Toeplitz matrix,Throughput,Cartesian coordinate system,Computation | Journal |
Volume | Issue | ISSN |
73 | 5 | 0743-7315 |
Citations | PageRank | References |
4 | 0.42 | 17 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiading Gai | 1 | 34 | 4.86 |
| Nady Obeid | 2 | 11 | 1.59 |
| Joseph L Holtrop | 3 | 4 | 1.10 |
| Xiao-Long Wu | 4 | 4 | 0.42 |
| Fan Lam | 5 | 50 | 9.14 |
| Maojing Fu | 6 | 20 | 3.51 |
| Justin P. Haldar | 7 | 350 | 35.40 |
| Wen-mei W. Hwu | 8 | 4322 | 511.62 |
| Zhi-Pei Liang | 9 | 522 | 64.94 |
| Brad Sutton | 10 | 199 | 27.18 |