Conference proceeding
Improving Deformable Image Registration Accuracy using a Hybrid Similarity Metric for Adaptive Radiation Therapy
MEDICAL IMAGING 2021: IMAGE PROCESSING, v 11596, 115963H
01 Jan 2021
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Adaptive radiation therapy uses deformable image registration to warp the dose from the planning computed tomography (CT) image to the daily cone-beam CT (CBCT) image acquired using the onboard volumetric imaging. Image quality of this CBCT image is usually inferior due to poor soft-tissue contrast of organs such as the prostate, causing registration algorithms to underperform in terms of accuracy. To alleviate this problem, we develop a hybrid image-similarity cost function that incorporates a point-to-distance map (PD) metric as one of its components. Given a pair of segmented images, structures on the fixed image are represented as sets of points while structures on the moving image are described as distance maps. The total distance of all fixed points to their associated boundaries on the moving image constitutes the PD metric, which is combined with the more traditional intensity similarity metric between the fixed and moving images. In this work, we use cubic B-splines as the registration transform. Our approach is validated using the pelvic reference dataset wherein the prostate, bladder, and rectum are manually contoured from the CT and CBCT images by a medical expert to obtain the segmented fixed and moving images. Accuracy of the deformable registration is quantified using the Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD), with and without the PD metric. Results demonstrate much improved overlap between the fixed and warped contours once the PD metric is applied. Moreover, the computational overhead associated with adding the PD metric is minimal.
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Details
- Title
- Improving Deformable Image Registration Accuracy using a Hybrid Similarity Metric for Adaptive Radiation Therapy
- Creators
- Keyur Shah - Drexel UniversityJames A. Shackleford - Drexel UniversityNagarajan Kandasamy - Drexel UniversityGregory C. Sharp - Massachusetts General Hospital
- Publication Details
- MEDICAL IMAGING 2021: IMAGE PROCESSING, v 11596, 115963H
- Series
- Proceedings of SPIE
- Publisher
- Spie-Int Soc Optical Engineering
- Number of pages
- 7
- Grant note
- NCI R01CA229178 / National Institutes of Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA 1553436; 1642345; 1642380 / National Science Foundation; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000672800200116
- Scopus ID
- 2-s2.0-85103662749
- Other Identifier
- 991019168716004721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Engineering, Biomedical
- Imaging Science & Photographic Technology
- Optics