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Abstract #4240

Electromagnetic Interference Removal in Low-Field MRI via EDITER, Structured Low-Rank Modeling, and Denoising Diffusion Model

Ga San Jhun1, Hyun Jun Yook1, Jae Hyun Cho1, Sai Abitha Srinivas2, Clarissa Zimmerman Cooley3,4, Berkin Bilgic3,4,5, and Tae Hyung Kim1
1Computer Engineering, Hongik University, Seoul, Korea, Republic of, 2Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Radiology, Harvard Medical School, Boston, MA, United States, 5Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

Synopsis

Keywords: Low-Field MRI, Low-Field MRI

Motivation: Low-field MRI often suffers from severe electromagnetic interference (EMI), substantially deteriorating image quality. Existing methods, including EDITER (External Dynamic InTerference Estimation and Removal), struggle to completely eliminate EMI, especially at high EMI levels.

Goal(s): To develop a robust framework that enhances low-field MRI by effectively eliminating EMI artifacts and improving signal-to-noise ratio (SNR).

Approach: We propose integrating LORAKS, a structured low-rank matrix modeling method, and DDNM, a zero-shot denoising diffusion model, into the EDITER framework.

Results: Our method successfully eliminates residual EMI artifacts and improves SNR, leading to substantial enhancements in low-field MRI and thereby overcoming limitations of existing methods.

Impact: We propose a novel framework for enhanced low-field MRI by integrating LORAKS and DDNM into EDITER, which effectively eliminates electromagnetic interference (EMI) and improves SNR. The proposed method substantially improves low-field MRI, overcoming limitations of existing methods.

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Keywords