ArticleMedical physics2025
Tumor aware recurrent inter-patient deformable image registration of computed tomography scans with lung cancer.
Article in Medical physics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A survey of transformer-based architectures in medical image analysis: models, applications, and challenges.Frontiers in artificial intelligence · 2026Review
- Tumor-conditioned inter-patient registration using planning computed tomography for voxel-based analysis to predict radiation pneumonitis in lung cancer patients.Physics and imaging in radiation oncology · 2026Article
- Dual-metric Bayesian optimization of B-spline mesh size for 4DCT lung registration.European journal of radiology open · 2025Article
- Modern Approaches for Thoracic Image Registration and Respiratory Motion Management in Oncology.Radiology. Imaging cancer · 2025Review
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Authors and funding
5 authors.
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Abstract
backgroundVoxel-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.
methodsTRACER 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 computed tomography (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.
resultsTRACER accurately aligned normal tissues. It best preserved tumors, indicated 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 and 0.013 Gy when using a female and a male reference.
conclusionsTRACER is a suitable method for inter-patient registration involving LC occurring in both fixed and moving images and applicable to voxel-based analysis methods.
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