Self-supervised learning for denoising of multidimensional MRI data.

ABSTRACT:

Purpose

To develop a fast denoising framework for high-dimensional MRI data based on a self-supervised learning scheme, which does not require ground truth clean image.

Theory and methods

Quantitative MRI faces limitations in SNR, because the variation of signal amplitude in a large set of images is the key mechanism for quantification. In addition, the complex non-linear signal models make the fitting process vulnerable to noise. To address these issues, we propose a fast deep-learning framework for denoising, which efficiently exploits the redundancy in multidimensional MRI data. A self-supervised model was designed to use only noisy images for training, bypassing the challenge of clean data paucity in clinical practice. For validation, we used two different datasets of si

SUBMITTER: Kang B 

PROVIDER: S-EPMC11341249 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature