Kang, Beomgu Lee, Wonil Seo, Hyunseok Heo, Hye-Young Park, Hyunwook 2024-07-11T05:30:24Z 2024-07-11T05:30:24Z 2024-07-11 2024-11 0740-3194 https://pubs.kist.re.kr/handle/201004/150203 PurposeTo 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 MethodsQuantitative 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 simulated magnetization transfer contrast MR fingerprinting (MTC-MRF) dataset and in vivo DWI image dataset to show the generalizability.ResultsThe proposed method drastically improved denoising performance in the presence of mild-to-severe noise regardless of noise distributions compared to previous methods of the BM3D, tMPPCA, and Patch2self. The improvements were even pronounced in the following quantification results from the denoised images.ConclusionThe proposed MD-S2S (Multidimensional-Self2Self) denoising technique could be further applied to various multi-dimensional MRI data and improve the quantification accuracy of tissue parameter maps. English John Wiley & Sons Inc. Self-supervised learning for denoising of multidimensional MRI data Article 10.1002/mrm.30197 1 Magnetic Resonance in Medicine, v.92, no.5, pp.1980 - 1994 Magnetic Resonance in Medicine 92 5 1980 1994 Y scie scopus 001255473900001 2-s2.0-85197269016 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging Article DIFFUSION TENSOR MRI NOISE ARTIFACTS magnetization transfer contrast (MTC) quantitative MRI self-supervised learning denoising diffusion