Name
Title | ||
|---|---|---|
Toward effective noise reduction for sub-Nyquist high-frame-rate MRI techniques with deep learning. |
Abstract | ||
|---|---|---|
Cine Cardiac Magnetic Resonance (Cine-CMR) is one example of dynamic MRI approaches to image organs that exhibit periodic motion. Conventional routine clinical Cine-CMR are typically obtained at 20-35 frames per second (fps) with temporal window sizes of 40-50 milliseconds. We have recently shown the feasibility of significantly increasing this overall frame rate by an acquisition of MRI k-space using a highly optimized radial sampling pattern with respect to both spatial and temporal coverage. In brief, our proposed approach acquires a significantly undersampled radial MRI k-space while encoding spatially and temporally periodic noise characteristics through the undersampled radial MRI acquisition; however, remnant radial streaking noise remain under physiologic imaging conditions. In this research, we propose to further remove these streaking noise, employing a Spatio-Temporal Denoising Auto-Encoder (ST-DAE) based on deep learning. We evaluate performance of our method in addressing such remnant artifact using ST-DAE; PSNR is used to evaluate image quality, and computational time is also discussed. |
Year | Venue | Field |
|---|---|---|
2017 | Asia-Pacific Signal and Information Processing Association Annual Summit and Conference | Noise reduction,Iterative reconstruction,Computer vision,Computer science,Image quality,Artificial intelligence,Frame rate,Nyquist–Shannon sampling theorem,Dynamic contrast-enhanced MRI,Streaking,Magnetic resonance imaging |
DocType | ISSN | Citations |
Conference | 2309-9402 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yudai Suzuki | 1 | 0 | 0.34 |
| Keigo Kawaji | 2 | 0 | 0.34 |
| Amit Patel | 3 | 8 | 3.88 |
| Satoshi Tamura | 4 | 84 | 8.67 |
| Satoru Hayamizu | 5 | 399 | 72.33 |