无需外部数据，快速恢复低数据量下的高精度谱CT图像
SCOUT: Fast Spectral CT Imaging in Ultra LOw-data Regimes via PseUdo-label GeneraTion
- 利用空间非局部相似性和投影域共轭特性生成伪3D数据
- 在极低原始数据下实现高质量重建，显著减少环形伪影
- 适合无标注数据场景，尤其适用于临床急用的快速成像
CT扫描中的噪声和伪影严重影响疾病诊断。现有方法或重建耗时过长，或依赖数据驱动模型优化，未能充分挖掘数据本身蕴含的信息，尤其是医学3D数据。本文提出一种在超低原始数据条件下进行重建的方法，无需外部数据，避免冗长预训练。通过利用空间非局部相似性及投影域共轭特性生成伪3D数据，实现自监督训练，可在极短时间内获得高保真结果。大量实验表明，该方法不仅能有效抑制探测器引起的环形伪影，还展现出前所未有的细节恢复能力。为利用未标注原始投影数据提供了新范式。代码已开源：https://github.com/yqx7150/SCOUT。
原文摘要 · Abstract (English)
Noise and artifacts during computed tomography (CT) scans are a fundamental challenge affecting disease diagnosis. However, current methods either involve excessively long reconstruction times or rely on data-driven models for optimization, failing to adequately consider the valuable information inherent in the data itself, especially medical 3D data. This work proposes a reconstruction method under ultra-low raw data conditions, requiring no external data and avoiding lengthy pre-training processes. By leveraging spatial nonlocal similarity and the conjugate properties of the projection domain to generate pseudo-3D data for self-supervised training, high-fidelity results can be achieved in a very short time. Extensive experiments demonstrate that this method not only mitigates detector-induced ring artifacts but also exhibits unprecedented capabilities in detail recovery. This method provides a new paradigm for research using unlabeled raw projection data. Code is available at https://github.com/yqx7150/SCOUT.
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