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| Home > Publications database > Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy. |
| Journal Article | DKFZ-2025-01314 |
; ; ; ; ;
2025
Elsevier Science
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.ijrobp.2025.04.004
Abstract: Deformable image registration (DIR) represents an inherently ill-posed problem, and its quality highly depends on the algorithm and user input, which can severely affect its applications in adaptive radiation therapy. We propose an automated framework for integrating DIR uncertainty into dose accumulation.A hyperparameter perturbation approach was applied to estimate an ensemble of deformation vector fields for a given computed tomography (CT) to cone beam CT (CBCT) DIR. For each voxel, a principal component analysis was performed on the distribution of homologous points to construct voxel-specific DIR uncertainty confidence ellipsoids. During the resampling process for dose mapping, the complete dose within each ellipsoid was evaluated via interpolation to estimate the upper and lower dose limits for the particular voxel. We applied the proposed framework in a retrospective dose accumulation study of 20 patients with lung cancer who underwent image guided radiation therapy with weekly CBCTs.The average computational time was around 30 minutes, making the approach clinically feasible for automated offline evaluations. The uncertainty (ie, largest ellipsoid semiaxis length) for the fifth week CBCT DIR was 3.8 ± 1.8, 2.5 ± 0.7, 1.5 ± 0.4, 3.2 ± 1.3, and 4.5 ± 1.8 mm for the gross tumor volume, esophagus, spinal cord, lungs, and heart, respectively. Confidence ellipsoids were markedly elongated, with the largest semiaxis 5.5 and 2.5 times longer than the other axes. The dosimetric uncertainties were mainly within 4 Gy but exhibited significant spatial variation because of the interplay between dose gradient and DIR uncertainty. When DIR uncertainty was considered in dose accumulation, 3 cases exceeded institutional limits for dose-volume histogram metrics, highlighting the importance of considering the inherent uncertainty of DIR.This framework has the potential to facilitate the clinical implementation of dose accumulation, which can improve clinical decision-making in adaptive radiation therapy and provide more personalized radiation therapy treatments.
Keyword(s): Humans (MeSH) ; Cone-Beam Computed Tomography: methods (MeSH) ; Uncertainty (MeSH) ; Radiotherapy, Image-Guided: methods (MeSH) ; Lung Neoplasms: radiotherapy (MeSH) ; Lung Neoplasms: diagnostic imaging (MeSH) ; Radiotherapy Dosage (MeSH) ; Retrospective Studies (MeSH) ; Algorithms (MeSH) ; Radiotherapy Planning, Computer-Assisted: methods (MeSH) ; Organs at Risk: diagnostic imaging (MeSH)
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