Harini Veeraraghavan

Jue Jiang, Chloe Min Seo Choi, Maria Thor et al.

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.

Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma et al.

Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 motion phases simulating digestive GI motion as 4D sequences were generated from static 3D patient scans using published analytical GI motion models through a semi-automated pipeline. Eleven datasets, including six T2w FSE MRI (T2w MRI), two T1w 4D golden-angle stack-of-stars, and three contrast-enhanced CT scans. The motion amplitudes of the DTs were assessed against real patient stomach motion amplitudes extracted from independent 4D MRI datasets. The generated DTs were then used to assess six different DIR methods using target registration error, Dice similarity coefficient, and the 95th percentile Hausdorff distance using summary metrics and voxel-level granular visualizations. Finally, for a subset of T2w MRI scans from patients treated with MR-guided radiation therapy, dose distributions were warped and accumulated to assess dose warping errors, including evaluations of DIR performance in both low- and high-dose regions for patient-specific error estimation. Main results: Our proposed pipeline synthesized DTs modeling realistic GI motion, achieving mean and maximum motion amplitudes and a mean log Jacobian determinant within 0.8 mm and 0.01, respectively, similar to published real-patient gastric motion data. It also enables the extraction of detailed quantitative DIR performance metrics and rigorous validation of dose mapping accuracy. Significance: The pipeline enables rigorously testing DIR tools for dynamic, anatomically complex regions enabling granular spatial and dosimetric accuracies.