Sparse-view computed tomography (CT) -- using a small number of projections for tomographic reconstruction -- enables much lower radiation dose to patients and accelerated data acquisition. The reconstructed images, however, suffer from strong artifacts, greatly limiting their diagnostic value. Current trends for sparse-view CT turn to the raw data for better information recovery. The resultant dual-domain methods, nonetheless, suffer from secondary artifacts, especially in ultra-sparse view scenarios, and their generalization to other scanners/protocols is greatly limited. A crucial question arises: have the image post-processing methods reached the limit? Our answer is not yet. In this paper, we stick to image post-processing methods due to great flexibility and propose global representation (GloRe) distillation framework for sparse-view CT, termed GloReDi. First, we propose to learn GloRe with Fourier convolution, so each element in GloRe has an image-wide receptive field. Second, unlike methods that only use the full-view images for supervision, we propose to distill GloRe from intermediate-view reconstructed images that are readily available but not explored in previous literature. The success of GloRe distillation is attributed to two key components: representation directional distillation to align the GloRe directions, and band-pass-specific contrastive distillation to gain clinically important details. Extensive experiments demonstrate the superiority of the proposed GloReDi over the state-of-the-art methods, including dual-domain ones. The source code is available at https://github.com/longzilicart/GloReDi.

翻译：稀疏视图计算机断层扫描（CT）——利用少量投影数据进行断层重建——可显著降低患者辐射剂量并加速数据采集。然而，重建图像存在严重伪影，极大限制了其诊断价值。当前稀疏视图CT的研究趋势转向利用原始数据以更好地恢复信息，但由此产生的双域方法在超稀疏视角场景中尤其易产生二次伪影，且其在不同扫描仪/协议间的泛化能力严重受限。一个关键问题随之浮现：图像后处理方法是否已达极限？我们的答案是否定的。本文坚持采用图像后处理方法（因其具有高度灵活性），并提出面向稀疏视图CT的全局表示（GloRe）蒸馏框架，命名为GloReDi。首先，我们提出通过傅里叶卷积学习全局表示，使得GloRe中的每个元素均具备图像级感受野。其次，不同于仅利用全视图图像进行监督的方法，我们提出从中视图重建图像（该数据易于获取但此前文献中未被探索）中蒸馏GloRe。GloRe蒸馏的成功归因于两个关键组件：表示方向蒸馏（用于对齐GloRe方向）与带通特定对比蒸馏（用于获取临床重要细节）。大量实验表明，所提出的GloReDi方法在性能上优于包括双域方法在内的当前最优方法。源代码已发布于https://github.com/longzilicart/GloReDi。