论文标题

MR图像denoising和超分辨率使用正则反向扩散

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

论文作者

Chung, Hyungjin, Lee, Eun Sun, Ye, Jong Chul

论文摘要

患者从MRI中扫描通常会遇到噪音,这阻碍了此类图像的诊断能力。作为减轻这种人工制品的一种方法,在医学成像界和社区以外的一般主题中都在很大程度上研究了Denoising。但是,最近的基于神经网络的最深层方法主要取决于最小平方误差(MMSE)估计值,这些估计往往会产生模糊的输出。此外,当部署在现实世界中的站点中时,这种模型会受到影响:分发数据和复杂的噪声分布与通常的参数噪声模型。在这项工作中,我们提出了一种基于基于得分的反向扩散采样的新授予方法,该方法克服了上述所有缺点。我们的网络仅接受冠状膝关节扫描训练,即使在分布式分布术在体内肝MRI数据中也擅长,并被复杂的噪声混合物污染。更重要的是,我们提出了一种通过同一网络增强DeNo的图像分辨率的方法。通过广泛的实验,我们表明我们的方法建立了最先进的性能,同时具有先前MMSE DeNoisers没有的理想特性:灵活地选择Denoising的程度和量化不确定性。

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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