SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation
Journal:
arXiv
Published Date:
Mar 23, 2025
Abstract
To develop and evaluate a new deep learning MR denoising method that
leverages quantitative noise distribution information from the reconstruction
process to improve denoising performance and generalization.
This retrospective study trained 14 different transformer and convolutional
models with two backbone architectures on a large dataset of 2,885,236 images
from 96,605 cardiac retro-gated cine complex series acquired at 3T. The
proposed training scheme, termed SNRAware, leverages knowledge of the MRI
reconstruction process to improve denoising performance by simulating large,
high quality, and diverse synthetic datasets, and providing quantitative
information about the noise distribution to the model. In-distribution testing
was performed on a hold-out dataset of 3000 samples with performance measured
using PSNR and SSIM, with ablation comparison without the noise 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. The best model found in the
in-distribution test generalized well to out-of-distribution samples,
delivering 6.5x and 2.9x CNR improvement for real-time cine and perfusion
imaging, respectively. Further, a model trained with 100% cardiac cine data
generalized well to a T1 MPRAGE neuro 3D scan and T2 TSE spine MRI.
