ORRN：一种基于常微分方程的递归配准网络，用于肺4DCT图像的可变形呼吸运动估计
ORRN: An ODE-based Recursive Registration Network for Deformable Respiratory Motion Estimation with Lung 4DCT Images
AI总结：
本文提出基于常微分方程的递归配准网络ORRN，通过估计时变体素速度并对ODE积分来建模肺4DCT中的呼吸运动，在DIRLab和CREATIS数据集上以低于1秒的计算速度取得最小目标配准误差，优于现有学习方法。
摘要：
可变形图像配准（DIR）在量化医学数据中的形变方面发挥着重要作用。近年来，深度学习方法在配准一对医学图像方面已展现出良好的精度和加速效果。然而，在4D（3D+时间）医学数据中，器官运动（如呼吸运动和心脏跳动）无法通过成对方法有效建模，因为这些方法仅针对图像对进行优化，并未考虑处理4D数据时所必需的器官运动模式。本文提出了ORRN，一种基于常微分方程（ODE）的递归图像配准网络。该网络学习为建模4D图像数据形变的ODE估计时变体素速度。它采用递归配准策略，通过对体素速度进行ODE积分来逐步估计形变场。我们在两个公开可用的肺4DCT数据集DIRLab和CREATIS上评估了所提方法，并针对两项任务进行评估：1）将所有图像配准到最大吸气相图像，用于3D+t形变跟踪；2）将最大呼气相图像配准到吸气相图像。我们的方法在这两项任务中均优于其他基于学习的方法，分别取得了1.24mm和1.26mm的最小目标配准误差（Target Registration Error）。此外，该方法产生的不真实图像折叠比例低于0.001%，且每个CT体积的计算速度小于1秒。ORRN在组配准和成对配准任务上展示了良好的配准精度、形变合理性和计算效率。这对于在放射治疗中的治疗计划或胸部穿刺中的机器人运动规划中实现快速准确的呼吸运动估计具有重要意义。
英文摘要：
Deformable Image Registration (DIR) plays a significant role in quantifying deformation in medical data. Recent Deep Learning methods have shown promising accuracy and speedup for registering a pair of medical images. However, in 4D (3D + time) medical data, organ motion, such as respiratory motion and heart beating, can not be effectively modeled by pair-wise methods as they were optimized for image pairs but did not consider the organ motion patterns necessary when considering 4D data. This paper presents ORRN, an Ordinary Differential Equations (ODE)-based recursive image registration network. Our network learns to estimate time-varying voxel velocities for an ODE that models deformation in 4D image data. It adopts a recursive registration strategy to progressively estimate a deformation field through ODE integration of voxel velocities. We evaluate the proposed method on two publicly available lung 4DCT datasets, DIRLab and CREATIS, for two tasks: 1) registering all images to the extreme inhale image for 3D+t deformation tracking and 2) registering extreme exhale to inhale phase images. Our method outperforms other learning-based methods in both tasks, producing the smallest Target Registration Error of 1.24mm and 1.26mm, respectively. Additionally, it produces less than 0.001\% unrealistic image folding, and the computation speed is less than 1 second for each CT volume. ORRN demonstrates promising registration accuracy, deformation plausibility, and computation efficiency on group-wise and pair-wise registration tasks. It has significant implications in enabling fast and accurate respiratory motion estimation for treatment planning in radiation therapy or robot motion planning in thoracic needle insertion.
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。
