Name
Title | ||
|---|---|---|
Real-Time Compressive Sensing Mri Reconstruction Using Gpu Computing And Split Bregman Methods |
Abstract | ||
|---|---|---|
Compressive sensing (CS) has been shown to enable dramatic acceleration of MRI acquisition in some applications. Being an iterative reconstruction technique, CS MRI reconstructions can be more time-consuming than traditional inverse Fourier reconstruction. We have accelerated our CS MRI reconstruction by factors of up to 27 by using a split Bregman solver combined with a graphics processing unit (GPU) computing platform. The increases in speed we find are similar to those we measure for matrix multiplication on this platform, suggesting that the split Bregman methods parallelize efficiently. We demonstrate that the combination of the rapid convergence of the split Bregman algorithm and the massively parallel strategy of GPU computing can enable real-time CS reconstruction of even acquisition data matrices of dimension 40962 or more, depending on available GPU VRAM. Reconstruction of two-dimensional data matrices of dimension 10242 and smaller took similar to 0.3 s or less, showing that this platform also provides very fast iterative reconstruction for small-to-moderate size images. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1155/2012/864827 | INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING |
Keywords | Field | DocType |
bioinformatics,biomedical research | Data mining,Massively parallel,Computer science,CUDA,Image processing,Computational science,Artificial intelligence,Compressed sensing,Iterative reconstruction,Computer vision,General-purpose computing on graphics processing units,Graphics processing unit,Matrix multiplication | Journal |
Volume | ISSN | Citations |
2012 | 1687-4188 | 9 |
PageRank | References | Authors |
0.61 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David S. Smith | 1 | 50 | 5.85 |
| John C Gore | 2 | 616 | 41.36 |
| Thomas E. Yankeelov | 3 | 20 | 7.14 |
| E. Brian Welch | 4 | 45 | 16.66 |