期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 42, 期 4, 页码 922-934出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2022.3220681
关键词
Noise reduction; Noise measurement; Magnetic resonance imaging; Mathematical models; Training; Diffusion processes; Numerical models; Diffusion model; stochastic contraction; denoising; MRI
We propose a new denoising method based on score-based reverse diffusion sampling, which outperforms traditional MMSE denoisers in terms of both image quality and adaptability to real-world situations.
Patient scans from MRI often suffer from noise, which hampers the diagnostic capability of such images. As a method to mitigate such artifacts, 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 situations: 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 a 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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