Nex2Nex – An unsupervised deep learning method for denoising MR images

Motivation: Supervised deep learning (DL) denoising methods for MR images requires appropriate modelling of MRI noise. Propose an approach learning MR intrinsic noise without the requirement of modelling the MRI noise accurately. Goal(s): Propose an unsupervised DL denoising solution for improving SNR of MR images, without requirement of accurate MRI noise modelling. Approach: The proposed unsupervised DL denoising approach utilized the multiple excitations of MR images acquired to learn the intrinsic MRI noise. Results: Proposed method was evaluated on data acquired at structural and DWI MRI data. Two experienced radiologists rated the DWI denoising as 'good' image quality which were of diagnostic image quality. Impact: Proposed method enables training MRI DL denoising models without requirement to accurately model MRI noise. This approach becomes helpful while training denoising methods for low SNR MRI data.