Home > Publications database > Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy. > print

001     302295
005     20250628200202.0
024 7 _ |a 10.1016/j.ijrobp.2025.04.004
|2 doi
024 7 _ |a pmid:40239820
|2 pmid
024 7 _ |a pmc:PMC12202161
|2 pmc
024 7 _ |a 0360-3016
|2 ISSN
024 7 _ |a 1879-355X
|2 ISSN
037 _ _ |a DKFZ-2025-01314
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Meyer, Sebastian
|b 0
245 _ _ |a Deformable Image Registration Uncertainty-Encompassing Dose Accumulation for Adaptive Radiation Therapy.
260 _ _ |a Amsterdam [u.a.]
|c 2025
|b Elsevier Science
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1751029826_11140
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a 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.
536 _ _ |a 899 - ohne Topic (POF4-899)
|0 G:(DE-HGF)POF4-899
|c POF4-899
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a Cone-Beam Computed Tomography: methods
|2 MeSH
650 _ 2 |a Uncertainty
|2 MeSH
650 _ 2 |a Radiotherapy, Image-Guided: methods
|2 MeSH
650 _ 2 |a Lung Neoplasms: radiotherapy
|2 MeSH
650 _ 2 |a Lung Neoplasms: diagnostic imaging
|2 MeSH
650 _ 2 |a Radiotherapy Dosage
|2 MeSH
650 _ 2 |a Retrospective Studies
|2 MeSH
650 _ 2 |a Algorithms
|2 MeSH
650 _ 2 |a Radiotherapy Planning, Computer-Assisted: methods
|2 MeSH
650 _ 2 |a Organs at Risk: diagnostic imaging
|2 MeSH
700 1 _ |a Hu, Yu-Chi
|b 1
700 1 _ |a Rimner, Andreas
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Mechalakos, James
|b 3
700 1 _ |a Cerviño, Laura
|b 4
700 1 _ |a Zhang, Pengpeng
|b 5
773 _ _ |a 10.1016/j.ijrobp.2025.04.004
|g Vol. 122, no. 4, p. 818 - 826
|0 PERI:(DE-600)1500486-7
|n 4
|p 818 - 826
|t International journal of radiation oncology, biology, physics
|v 122
|y 2025
|x 0360-3016
909 C O |o oai:inrepo02.dkfz.de:302295
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 2
|6 P:(DE-HGF)0
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
914 1 _ |y 2025
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2024-12-31
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2024-12-31
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2024-12-31
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b INT J RADIAT ONCOL : 2022
|d 2024-12-31
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2024-12-31
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2024-12-31
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b INT J RADIAT ONCOL : 2022
|d 2024-12-31
920 1 _ |0 I:(DE-He78)FR01-20160331
|k FR01
|l DKTK Koordinierungsstelle Freiburg
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)FR01-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21