Name
Abstract | ||
|---|---|---|
Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides excellent soft-tissue contrast without using ionizing radiation. MRI's clinical application may be limited by long data acquisition time; therefore, MR image reconstruction from highly under-sampled k-space data has been an active research area. Calibrationless MRI not only enables a higher acceleration rate but also increases flexibility for sampling pattern design. To leverage non-linear machine learning priors, we pair our High-dimensional Fast Convolutional Framework (IIICU) [1] with a plug-in denoiser and demonstrate its feasibility using 2D brain data. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISBI48211.2021.9433815 | 2021 IEEE 18TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI) |
Keywords | DocType | ISSN |
Calibrationless MRI, parallel imaging, structured low-rank matrix completion, proximal gradient descent | Conference | 1945-7928 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shen Zhao | 1 | 0 | 0.34 |
| Lee C. Potter | 2 | 449 | 35.60 |
| Rizwan Ahmad | 3 | 3 | 1.06 |