Lorenzo Polo, A., Nix, M. orcid.org/0000-0001-7228-7344, Thompson, C. et al. (6 more authors) (2024) Improving hybrid image and structure-based deformable image registration for large internal deformations. Physics in Medicine & Biology, 69 (9). 095011. ISSN 0031-9155
Abstract
Objective. Deformable image registration (DIR) is a widely used technique in radiotherapy. Complex deformations, resulting from large anatomical changes, are a regular challenge. DIR algorithms generally seek a balance between capturing large deformations and preserving a smooth deformation vector field (DVF). We propose a novel structure-based term that can enhance the registration efficacy while ensuring a smooth DVF. Approach. The proposed novel similarity metric for controlling structures was introduced as a new term into a commercially available algorithm. Its performance was compared to the original algorithm using a dataset of 46 patients who received pelvic re-irradiation, many of which exhibited complex deformations. Main results. The mean Dice Similarity Coefficient (DSC) under the improved algorithm was 0.96, 0.94, 0.76, and 0.91 for bladder, rectum, colon, and bone respectively, compared to 0.69, 0.89, 0.62, and 0.88 for the original algorithm. The improvement was more pronounced for complex deformations. Significance. With this work, we have demonstrated that the proposed term is able to improve registration accuracy for complex cases while maintaining realistic deformations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This item is protected by copyright. This Accepted Manuscript is available for reuse under a CC BY-NC-ND licence. This is an author produced version of an item published in Physics in Medicine & Biology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Pelvis; Humans; Tomography, X-Ray Computed; Radiotherapy Planning, Computer-Assisted; Algorithms; Image Processing, Computer-Assisted; Urinary Bladder |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Leeds Institute of Medical Research (LIMR) > Division of Oncology |
Funding Information: | Funder Grant number Cancer Research UK Supplier No: 138573 A28832 |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Jun 2024 13:26 |
Last Modified: | 18 Jun 2024 13:26 |
Published Version: | https://iopscience.iop.org/article/10.1088/1361-65... |
Status: | Published |
Publisher: | IOP Publishing |
Identification Number: | 10.1088/1361-6560/ad3723 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213541 |
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