Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging.

ABSTRACT:

Purpose

To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR.

Methods

Three independent attention modules were developed: spatial local, spatial global, and frame attentions. They capture long-range signal correlation and bring back the locality of information in images. An attention-cell-block design processes 5D tensors ([B, C, F, H, W]) for 2D, 2D+T, and 3D image data. A High Resolution (HRNet) backbone was built to hold IT blocks. Training dataset consists of 206,677 cine series and test datasets had 7,267 series. Ten input SNR levels from 0.05 to 8.0 were tested. IT models were compared to seven convolutional and transformer baselines

SUBMITTER: Xue H 

PROVIDER: S-EPMC12306840 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature