Abstract Background Accurate four‐dimensional dose calculation (4DDC) is essential for carbon‐ion lung radiotherapy and relies on deformable image registration (DIR). However, conventional DIR methods are computationally intensive, hindering the implementation of online adaptive workflows. Purpose This study investigates the feasibility and efficacy of unsupervised deep learning‐based DIR models, TransMatch and VoxelMorph, in accelerating clinical lung four‐dimensional computed tomography (4DCT) registration and facilitating accurate carbon‐ion 4D dose calculation. Methods A total of 150 clinical lung 4DCT datasets were utilized (120 for training, 20 for validation, and 10 for testing), with a conventional B‐spline method serving as the baseline. Registration accuracy was evaluated using the Mean Absolute Error (MAE), Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Jacobian determinant (| J |). Carbon‐ion 4D dose distributions were accumulated using the generated deformation vector fields (DVFs). Dosimetric impacts on the gross tumor volume (GTV) and organs at risk (OARs) were quantified using Dose‐Volume Histogram (DVH) metrics under a gating with 6× rescanning scenario. Results TransMatch and VoxelMorph achieved superior registration accuracy with lower MAE, mean DSC > 0.97, and HD95 < 2.5 mm. In DVF analysis, TransMatch and VoxelMorph showed negligible folding rates (<0.02%) and significantly higher Jacobian standard deviations than the B‐spline method, indicating superior capability in capturing fine local deformations. Dosimetrically, differences in GTV and OARs metrics between the deep learning and B‐spline methods were less than 2% of the prescription dose, falling within clinically acceptable tolerances. Crucially, TransMatch and VoxelMorph models achieved sub‐second registration times (<1 s), whereas the conventional B‐spline method required more than 10 min. Conclusions TransMatch and VoxelMorph achieve geometric and dosimetric accuracy comparable to the conventional B‐spline method for carbon‐ion lung radiotherapy while offering substantially higher computational speed, highlighting their potential for real‐time adaptive carbon‐ion therapy.
An et al. (Sun,) studied this question.