SPLIT：用于非线性断层扫描中学习反演的自监督分区
SPLIT: Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography
AI总结：
本文提出SPLIT框架，通过自监督学习在无地面真值数据的情况下重建非线性断层扫描图像，理论证明其与监督学习在期望上等价，并在多光谱CT中验证了其高重建质量与噪声鲁棒性。
摘要：
机器学习在断层扫描重建中取得了显著成效，但监督训练需要配对的测量数据和地面真值图像，这些数据通常不可用。这促使了自监督方法的发展，主要解决去噪和线性反问题。本文针对非线性反问题，提出SPLIT（用于非线性断层扫描中学习反演的自监督分区）框架，该框架在无地面真值样本的情况下，通过强制跨分区一致性与测量域保真度，并利用多个分区的互补信息，实现图像重建。主要理论结果表明，在温和条件下，所提自监督目标在期望上等价于监督目标。通过自动停止规则正则化训练，当无参考图像质量代理饱和时停止优化。作为具体应用，我们为多光谱CT导出了SPLIT变体。在稀疏视图采集实验中，展示了高重建质量和对噪声的鲁棒性，优于经典迭代重建和最近的自监督基线。
英文摘要：
Machine learning has achieved impressive performance in tomographic reconstruction, but supervised training requires paired measurements and ground-truth images that are often unavailable. This has motivated self-supervised approaches, which have primarily addressed denoising and, more recently, linear inverse problems. We address nonlinear inverse problems and introduce SPLIT (Self-supervised Partitioning for Learned Inversion in Nonlinear Tomography), a self-supervised machine-learning framework for reconstructing images from nonlinear, incomplete, and noisy projection data without any samples of ground-truth images. SPLIT enforces cross-partition consistency and measurement-domain fidelity while exploiting complementary information across multiple partitions. Our main theoretical result shows that, under mild conditions, the proposed self-supervised objective is equivalent to its supervised counterpart in expectation. We regularize training with an automatic stopping rule that halts optimization when a no-reference image-quality surrogate saturates. As a concrete application, we derive SPLIT variants for multispectral computed tomography. Experiments on sparse-view acquisitions demonstrate high reconstruction quality and robustness to noise, surpassing classical iterative reconstruction and recent self-supervised baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
