Background: Voxel-based analysis (VBA) for population level radiotherapy (RT) outcomes modeling requires topology preserving inter-patient deformable image registration (DIR) that preserves tumors on moving images while avoiding unrealistic deformations due to tumors occurring on fixed images. Purpose: We developed a tumor-aware recurrent registration (TRACER) deep learning (DL) method and evaluated its suitability for VBA. Methods: TRACER consists of encoder layers implemented with stacked 3D convolutional long short term memory network (3D-CLSTM) followed by decoder and spatial transform layers to compute dense deformation vector field (DVF). Multiple CLSTM steps are used to compute a progressive sequence of deformations. Input conditioning was applied by including tumor segmentations with 3D image pairs as input channels. Bidirectional tumor rigidity, image similarity, and deformation smoothness losses were used to optimize the network in an unsupervised manner. TRACER and multiple DL methods were trained with 204 3D CT image pairs from patients with lung cancers (LC) and evaluated using (a) Dataset I (N = 308 pairs) with DL segmented LCs, (b) Dataset II (N = 765 pairs) with manually delineated LCs, and (c) Dataset III with 42 LC patients treated with RT. Results: TRACER accurately aligned normal tissues. It best preserved tumors, blackindicated by the smallest tumor volume difference of 0.24\%, 0.40\%, and 0.13 \% and mean square error in CT intensities of 0.005, 0.005, 0.004, computed between original and resampled moving image tumors, for Datasets I, II, and III, respectively. It resulted in the smallest planned RT tumor dose difference computed between original and resampled moving images of 0.01 Gy and 0.013 Gy when using a female and a male reference.

翻译：背景：基于体素分析（VBA）用于群体水平放射治疗（RT）结果建模，需要保持拓扑结构的患者间可变形图像配准（DIR），该配准需在移动图像上保留肿瘤，同时避免因固定图像上的肿瘤导致不现实的形变。目的：我们开发了一种肿瘤感知的循环配准（TRACER）深度学习方法，并评估了其对于VBA的适用性。方法：TRACER由编码器层、解码器层和空间变换层组成，其中编码器层通过堆叠的三维卷积长短期记忆网络（3D-CLSTM）实现，用于计算密集形变矢量场（DVF）。使用多个CLSTM步骤来计算渐进式形变序列。输入条件通过将肿瘤分割与三维图像对作为输入通道来实现。采用双向肿瘤刚性、图像相似性和形变平滑性损失以无监督方式优化网络。使用204对肺癌（LC）患者的三维CT图像对训练TRACER和多种深度学习方法，并使用以下数据集进行评估：（a）数据集I（N = 308对），包含深度学习分割的LC；（b）数据集II（N = 765对），包含手动勾画的LC；（c）数据集III，包含42名接受RT治疗的LC患者。结果：TRACER准确对齐了正常组织。它最好地保留了肿瘤，表现为在数据集I、II和III中，原始与重采样移动图像肿瘤之间的肿瘤体积差异最小，分别为0.24%、0.40%和0.13%，CT强度均方误差分别为0.005、0.005和0.004。当使用女性和男性参考时，它在原始与重采样移动图像之间计算出的计划RT肿瘤剂量差异最小，分别为0.01 Gy和0.013 Gy。