IMITATE: 面向未知时间帧恢复的带上下文图像配准
IMITATE: Image Registration with Context for unknown time frame recovery
AI总结：
针对4D-CT扫描中呼吸运动导致的肿瘤图像重建伪影问题，提出带条件U-Net的图像配准框架IMITATE，可无伪影重建不同呼吸幅度的3D体数据，具备实时性。
摘要：
本文提出了一种全新的图像配准形式体系，专门用于基于两张或更多已知图像及其关联条件，估计与未知条件相关的图像。我们展示了如何通过一种新型条件U-Net架构对该形式体系进行实际建模，该架构充分考虑了条件信息，且无需任何固定图像。随后，我们将该形式体系应用于胸腹部区域的4D-CT（3D+t）扫描，用于在不同呼吸幅度下为放射治疗生成移动肿瘤的图像。这一驱动应用尤为复杂，因为它需要将一系列连续的2D切片拼接成对应不同器官位置的多个3D体数据。由于患者呼吸不规律、滞后效应以及呼吸信号与内部运动的相关性较差，使用标准方法进行运动插值会在拼接后的体数据中产生众所周知的重建伪影。在4D-CT临床数据上获得的结果表明，该方法可实现无伪影的体数据重建，且具备实时延迟性能。代码已公开发布于https://github.com/Kheil-Z/IMITATE 。
英文摘要：
In this paper, we formulate a novel image registration formalism dedicated to the estimation of unknown condition-related images, based on two or more known images and their associated conditions. We show how to practically model this formalism by using a new conditional U-Net architecture, which fully takes into account the conditional information and does not need any fixed image. Our formalism is then applied to image moving tumors for radiotherapy treatment at different breathing amplitude using 4D-CT (3D+t) scans in thoracoabdominal regions. This driving application is particularly complex as it requires to stitch a collection of sequential 2D slices into several 3D volumes at different organ positions. Movement interpolation with standard methods then generates well known reconstruction artefacts in the assembled volumes due to irregular patient breathing, hysteresis and poor correlation of breathing signal to internal motion. Results obtained on 4D-CT clinical data showcase artefact-free volumes achieved through real-time latencies. The code is publicly available at https://github.com/Kheil-Z/IMITATE .
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。
