English(EN) Differentiable Jitter Correction using Deep Learning-based Image Quality Metric for Phase-Contrast Micro-CT

深度学习校正X射线微焦点CT图像抖动

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-28 04:00

研究人员开发了一种新颖的方法来校正X射线相衬微计算机断层扫描中的图像失真。该技术利用深度学习模型直接从采集的数据中估计和补偿投影抖动，无需预扫描参考。该方法已在生物标本上得到验证，证明了其能够恢复因运动伪影而丢失的精细结构细节。 AI

影响这项研究推进了医学成像中的图像处理技术，有可能通过更清晰的重建来提高诊断准确性。

排序理由详细介绍新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Junan Chen, Yiting Jia, Joscha Maier, Dominik John, Sami Wirtensohn, Imke Greving, Silja Flenner, Matthias Wieczorek, Julia Herzen · 2026-08-28 04:00

基于深度学习图像质量评价的相衬微CT可微抖动校正

arXiv:2608.27034v1 Announce Type: new Abstract: 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 direc…