论文标题
在自我监督的低剂量CT环境中双重域denoing的好处
On the Benefit of Dual-domain Denoising in a Self-supervised Low-dose CT Setting
论文作者
论文摘要
计算机断层扫描（CT）通常用于三维非侵入性成像。提出了许多数据驱动的图像降级算法，以恢复低剂量获取的图像质量。但是，由于访问适当的投影数据或正确的重建算法，研究的研究研究已经介入了原始探测器数据中已经介入的方法要少得多。在这项工作中，我们提出了一个可端到端的CT重建管道，该管道包含投影和图像域中的非授权运算符，并且在不需要地面真相高剂量CT数据的情况下同时进行了优化。我们的实验表明，在腹部CT上，包括额外的投影denoising操作员将整体脱糖性效果提高了82.4-94.1％/12.5-41.7％（PSNR/SSIM），而XRM数据相对于低剂量基线，XRM数据的总体脱氧性能和1.5-2.9％/0.4-0.5％（psnr/ssim）的总体脱氧性能提高了。我们使整个螺旋CT重建框架公开可用，其中包含一个原始的投影重新介绍步骤，以渲染适用于可区分的风扇束重建操作员和端到端学习的螺旋投影数据。
Computed tomography (CT) is routinely used for three-dimensional non-invasive imaging. Numerous data-driven image denoising algorithms were proposed to restore image quality in low-dose acquisitions. However, considerably less research investigates methods already intervening in the raw detector data due to limited access to suitable projection data or correct reconstruction algorithms. In this work, we present an end-to-end trainable CT reconstruction pipeline that contains denoising operators in both the projection and the image domain and that are optimized simultaneously without requiring ground-truth high-dose CT data. Our experiments demonstrate that including an additional projection denoising operator improved the overall denoising performance by 82.4-94.1%/12.5-41.7% (PSNR/SSIM) on abdomen CT and 1.5-2.9%/0.4-0.5% (PSNR/SSIM) on XRM data relative to the low-dose baseline. We make our entire helical CT reconstruction framework publicly available that contains a raw projection rebinning step to render helical projection data suitable for differentiable fan-beam reconstruction operators and end-to-end learning.
