标题： SS-CTML：用于CT图像重建的自监督跨任务互学习 显示英文标题

标题： SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction

摘要： 监督深度学习（SDL）技术在配对训练数据集上已被广泛研究用于X射线计算机断层扫描（CT）图像重建。 然而，由于在临床常规中获取配对训练数据集存在困难，SDL方法在临床实践中仍难以广泛应用。 近年来，自监督深度学习（SSDL）技术在CT图像重建研究中显示出巨大潜力。 在本工作中，我们提出了一种自监督跨任务互学习（SS-CTML）框架用于CT图像重建。 具体来说，首先从全视角扫描的sinogram数据中提取出稀疏视角扫描和有限视角扫描的sinogram数据，这导致了三个独立的重建任务，即全视角CT（FVCT）重建、稀疏视角CT（SVCT）重建和有限视角CT（LVCT）重建。 然后，为这三个重建任务构建了三个神经网络。 考虑到这三个任务的最终目标都是重建高质量的CT图像，因此我们为这三个任务构建了一组跨任务互学习目标，通过这种方式，三个神经网络可以通过相互学习进行自监督优化。 采用临床数据集来评估所提出框架的有效性。 实验结果表明，SS-CTML框架在定量和定性测量方面都能获得有前景的CT图像重建性能。 显示英文摘要

摘要： Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements.