MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion. (arXiv:2203.12621v1 [eess.IV])

Patient scans from MRI often suffer from noise, which hampers the diagnostic
capability of such images. As a method to mitigate such artifact, denoising is
largely studied both within the medical imaging community and beyond the
community as a general subject. However, recent deep neural network-based
approaches mostly rely on the minimum mean squared error (MMSE) estimates,
which tend to produce a blurred output. Moreover, such models suffer when
deployed in real-world sitautions: out-of-distribution data, and complex noise
distributions that deviate from the usual parametric noise models. In this
work, we propose a new denoising method based on score-based reverse diffusion
sampling, which overcomes all the aforementioned drawbacks. Our network,
trained only with coronal knee scans, excels even on out-of-distribution in
vivo liver MRI data, contaminated with complex mixture of noise. Even more, we
propose a method to enhance the resolution of the denoised image with the same
network. With extensive experiments, we show that our method establishes
state-of-the-art performance, while having desirable properties which prior
MMSE denoisers did not have: flexibly choosing the extent of denoising, and
quantifying uncertainty.



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