根据图像复杂度动态选投射角度，降低剂量同时提升重建质量。
SAVER: Stochastic Adaptive Variance-Driven Exploration and Reconstruction for Low-Dose Computed Tomography
- 基于数据方差实时选择最信息丰富的投射角度。
- 在8个模拟体模上均优于传统随机采样，尤其对结构复杂的器官。
- 适合追求低剂量高精度CT成像的临床与科研人员。
计算机断层扫描（CT）在临床诊断中不可或缺，但如何在不损害图像质量的前提下降低辐射剂量仍是关键挑战。传统低剂量方案通常采用固定均匀的角度采样，与患者器官的结构复杂性无关。本文提出“随机自适应方差驱动探索与重建”（SAVER）框架，通过实时分析已采集数据的统计方差，动态选择最具结构信息的投射角度。采用基于Softmax的随机调度策略结合模拟退火机制，优先选取高信息量方向，同时保证必要探索。在8个不同模拟体模上的数值实验表明，SAVER在重建保真度上持续优于传统随机采样，尤其在结构各向异性较强的物体上表现显著。此外，该方法在强测量噪声下仍保持稳健性能。通过将辐射剂量动态分配至最相关信息投影，SAVER提供了一种数学严谨的策略，以单位辐射剂量最大化诊断质量，推动了面向样本、数据驱动的CT采集新范式。
原文摘要 · Abstract (English)
Computed Tomography (CT) is indispensable in clinical diagnostics, yet minimizing radiation dose without compromising image quality remains a critical challenge. Conventional low-dose protocols often rely on fixed, uniform angular sampling, independent of the underlying structural complexity of organs of individual patients. We propose ``Stochastic Adaptive Variance-Driven Exploration and Reconstruction'' (SAVER), an adaptive data acquisition framework that selects projection angles in real-time based on the statistical variance of acquired data. Utilizing a Softmax-based stochastic scheduling scheme with simulated annealing, SAVER prioritizes directions with high structural information while maintaining necessary exploration. Numerical experiments across 8 diverse phantoms demonstrate that SAVER achieves consistently higher reconstruction fidelity than conventional random sampling, particularly for objects with high structural anisotropy. Furthermore, the proposed method exhibits robust performance under significant measurement noise. By dynamically reallocating radiation dose to the most informative projections, SAVER provides a mathematically-grounded approach to maximize diagnostic quality per unit of radiation dose, marking a shift toward sample-dependent, data-driven CT acquisition.
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