基于深度学习图像质量度量的相衬显微CT可微分抖动校正方法

中文摘要 AI 辅助

本文提出一种用于X射线相衬显微计算机断层扫描（micro computed tomography, micro-CT）的全可微分抖动校正方法，该方法采用基于深度学习的图像质量度量，可直接从采集的投影数据中估计并校正每个投影的刚性抖动，无需预扫描的无运动参考数据。该方法基于适配平行束几何的基于梯度的自动对焦策略。在受控研究中对一组候选目标函数进行了基准测试，并验证了视觉信息保真度（visual information fidelity, VIF）度量对目标相衬数据中抖动伪影的敏感性。为了在无干净参考的情况下运行，训练了一个紧凑的3D卷积神经网络，用于从单个受污染的体积中预测VIF分数。引入了仅应用于图像背景的空间选择性全变分惩罚，以惩罚优化过程中出现的虚假高频结构。对在不同同步加速器光束线采集的生物标本进行了实验，评估采用施加到模拟和实验采集的投影数据上的抖动运动。结果证实，该集成流程可可靠恢复因抖动丢失的精细结构细节，且在形态不同的样本上展现出泛化能力。

英文摘要

This paper proposes a fully differentiable jitter correction method for X-ray phase-contrast micro computed tomography using a deep learning-based image quality metric that estimates and compensates per-projection rigid jitter directly from the acquired projection data, without a pre-scan motion-free reference. The approach builds on a gradient-based auto-focus strategy adapted to parallel-beam geometry. A set of candidate objective functions is benchmarked in a controlled study, and the sensitivity of the visual information fidelity (VIF) metric to the jitter artifact is verified with the target phase-contrast data. To operate without a clean reference, a compact 3D convolutional neural network is trained to predict the VIF score from a single corrupted volume. A spatially selective total variation penalty applied exclusively to the image background is introduced to penalize spurious high-frequency structures that otherwise emerge during optimization. Experiments on biological specimens acquired at different synchrotron beamlines are conducted. Evaluation uses jitter motion applied to simulated and experimentally acquired projection data. The result confirms that the integrated pipeline reliably recovers fine structural detail lost due to jitter, with generalization demonstrated across morphologically distinct samples.

发表机构

• ImFusion GmbH（ImFusion公司）
• Technical University of Munich（慕尼黑工业大学）
• Deutsches Elektronen-Synchrotron (DESY)（德国电子同步加速器研究所）
• Helmholtz-Zentrum Hereon（亥姆霍兹重离子研究中心（赫雷翁））

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