| Literature DB >> 35211244 |
Shen Zhao1, Lee C Potter1, Rizwan Ahmad2.
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 (HICU) [1] with a plug-in denoiser and demonstrate its feasibility using 2D brain data.Entities:
Keywords: Calibrationless MRI; parallel imaging; proximal gradient descent; structured low-rank matrix completion
Year: 2021 PMID: 35211244 PMCID: PMC8865188 DOI: 10.1109/isbi48211.2021.9433815
Source DB: PubMed Journal: Proc IEEE Int Symp Biomed Imaging ISSN: 1945-7928