Name
Title | ||
|---|---|---|
Distributed Memory-Efficient Physics-Guided Deep Learning Reconstruction for Large-Scale 3d Non-Cartesian MRI |
Abstract | ||
|---|---|---|
Physics-guided deep learning (PG-DL) reconstruction has emerged as a powerful strategy for accelerated MRI. However, adopting PG-DL on 3D non-Cartesian MRI remains a challenge due to GPU hardware limitations. In this paper, we utilize multiple memory-efficient techniques to accomplish PG-DL on large-scale 3D kooshball coronary MRI. We first leverage a recently proposed approach to keep only one un... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISBI52829.2022.9761485 | 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) |
Keywords | DocType | ISSN |
Training,Deep learning,Image quality,Three-dimensional displays,Magnetic resonance imaging,Graphics processing units,Hardware | Conference | 1945-7928 |
ISBN | Citations | PageRank |
978-1-6654-2923-8 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chi Zhang | 1 | 0 | 0.34 |
| Davide Piccini | 2 | 0 | 0.34 |
| Omer Burak Demirel | 3 | 0 | 0.34 |
| Gabriele Bonanno | 4 | 17 | 6.40 |
| Burhaneddin Yaman | 5 | 0 | 0.34 |
| Matthias Stuber | 6 | 0 | 0.34 |
| Steen Moeller | 7 | 0 | 0.34 |
| Mehmet Akçakaya | 8 | 0 | 0.34 |