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Proceedings Paper

Deep learning-based relative stopping power mapping generation with cone-beam CT in proton radiation therapy
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Paper Abstract

Proton radiation therapy has shown highly conformal distribution of prescribed dose in target with outstanding normal tissue sparing stemming from its steep dose gradient at the distal end of the beam. However, the uncertainty in everyday patient setup can lead to a discrepancy between treatment dose distribution and the planning dose distribution. Conebeam CT (CBCT) can be acquired daily before treatment to evaluate such inter-fraction setup error, while a further evaluation on resulted dose distribution error is currently not available. In this study, we developed a novel deep-learning based method to predict the relative stopping power maps from daily CBCT images to allow for online dose calculation in a step towards adaptive proton radiation therapy. 20 head-and-neck patients with CT and CBCT images are included for training and testing. Our CBCT RSP results were evaluated with RSP maps created from CT images as the ground truth. Among all the 20 patients, the averaged mean absolute error between CT-based and CBCT-based RSP was 0.04±0.02, the averaged mean error was -0.01±0.03 and the averaged normalized correlation coefficient was 0.97±0.01. The proposed method provides sufficiently accurate RSP map generation from CBCT images, possibly allowing for CBCT-guided adaptive treatment planning for proton radiation therapy.

Paper Details

Date Published: 16 March 2020
PDF: 6 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124L (16 March 2020); doi: 10.1117/12.2549275
Show Author Affiliations
Tonghe Wang, Emory Univ. (United States)
Joseph Harms, Emory Univ. (United States)
Yang Lei, Emory Univ. (United States)
Beth Ghavidel, Emory Univ. (United States)
William Stokes, Emory Univ. (United States)
Tian Liu, Emory Univ. (United States)
Walter J. Curran, Emory Univ. (United States)
Mark McDonald, Emory Univ. (United States)
Jun Zhou, Emory Univ. (United States)
Xiaofeng Yang, Emory Univ. (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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