论文标题
patch2self：通过自我监督的学习来降级扩散MRI
Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning
论文作者
论文摘要
扩散加权的磁共振成像（DWI）是唯一一种量化微观结构并重建活体大脑中的白物途径的无创方法。来自多个来源的波动在DWI数据中产生了显着的加性噪声，在随后的微观结构分析之前，必须抑制这些噪声。我们介绍了一种自我监督的学习方法，用于DENOTODED DWI数据，Patch2Self，该方法使用整个卷来学习该卷的全级线性denoiser。通过利用DWI数据的过采样Q空间，Patch2自己可以将结构与噪声分开，而无需对任何一个明确的模型。我们通过定量和定性改进在微观结构建模，跟踪（通过光纤束相干性）和模型估计相对于其他无监督和模拟数据的其他方法来证明patch2自己的有效性。
Diffusion-weighted magnetic resonance imaging (DWI) is the only noninvasive method for quantifying microstructure and reconstructing white-matter pathways in the living human brain. Fluctuations from multiple sources create significant additive noise in DWI data which must be suppressed before subsequent microstructure analysis. We introduce a self-supervised learning method for denoising DWI data, Patch2Self, which uses the entire volume to learn a full-rank locally linear denoiser for that volume. By taking advantage of the oversampled q-space of DWI data, Patch2Self can separate structure from noise without requiring an explicit model for either. We demonstrate the effectiveness of Patch2Self via quantitative and qualitative improvements in microstructure modeling, tracking (via fiber bundle coherency) and model estimation relative to other unsupervised methods on real and simulated data.
