Work

Calibrationless MRI Reconstruction With A Plug-In Denoiser

Year: 2021

Type: conference-paper

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.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi48211.2021.9433815

Authors: Shen Zhao, Lee C. Potter, Rizwan Ahmad

Institution: The Ohio State University

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green

Funders: National Institutes of Health, National Heart, Lung, and Blood Institute

Award: A New Paradigm for Rapid, Accurate Cardiac Magnetic Resonance Imaging