用条件U-Net实现呼吸运动肿瘤的无固定图像配准，实时还原未知时刻影像。
IMITATE: Image Registration with Context for unknown time frame recovery
- 基于条件U-Net建模，利用多张已知图像与对应生理条件推断未知状态图像。
- 在4D-CT数据上实现无伪影3D体积重建，实测延迟低至实时水平。
- 适用于放疗中呼吸不规律导致的器官运动插值，适合医学影像动态重建场景。
本文提出一种新型图像配准范式，用于从两幅或多幅已知图像及其关联条件中估计未知条件下的图像。通过引入新型条件U-Net架构，充分融合条件信息，无需固定参考图像。该方法应用于胸腹部4D-CT（3D+t）扫描中呼吸幅度不同的肿瘤运动重建任务。该应用极具挑战性，需将多组2D序列切片拼接为不同器官位置下的多个3D体积。传统插值方法因患者呼吸不规则、滞后效应及呼吸信号与内部运动相关性差，常产生显著伪影。在临床4D-CT数据上的实验表明，本方法可生成无伪影的3D体积，且具备实时处理延迟。代码已公开于 https://github.com/Kheil-Z/IMITATE。
原文摘要 · Abstract (English)
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 .
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