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Found 3 papers, 1 papers with code
Imaging Transformer for MRI Denoising: a Scalable Model Architecture that enables SNR << 1 Imaging
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.
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
Out-of-distribution tests were conducted on cardiac real-time cine, first-pass cardiac perfusion, and neuro and spine MRI, all acquired at 1. 5T, to test model generalization across imaging sequences, dynamically changing contrast, different anatomies, and field strengths.
Improving the generalizability of convolutional neural network-based segmentation on CMR images
We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy.
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