Improving deformable image registration accuracy using a hybrid similarity metric for adaptive radiation therapy

Keyur D. Shah; James A. Shackleford; Nagarajan Kandasamy; Gregory C. Sharp

doi:10.1117/12.2582164

15 February 2021 Improving deformable image registration accuracy using a hybrid similarity metric for adaptive radiation therapy

Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, Gregory C. Sharp

Author Affiliations +

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115963H (2021) https://doi.org/10.1117/12.2582164
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

Conference Presentation

Citation Download Citation

Keyur D. Shah, James A. Shackleford, Nagarajan Kandasamy, and Gregory C. Sharp "Improving deformable image registration accuracy using a hybrid similarity metric for adaptive radiation therapy", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115963H (15 February 2021); https://doi.org/10.1117/12.2582164

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