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

Tonghe Wang; Joseph Harms; Yang Lei; Beth Ghavidel; William Stokes; Tian Liu; Walter J. Curran; Mark McDonald; Jun Zhou; Xiaofeng Yang

doi:10.1117/12.2549275

16 March 2020 Deep learning-based relative stopping power mapping generation with cone-beam CT in proton radiation therapy

Tonghe Wang, Joseph Harms, Yang Lei, Beth Ghavidel, William Stokes, Tian Liu, Walter J. Curran, Mark McDonald, Jun Zhou, Xiaofeng Yang

Author Affiliations +

Proceedings Volume 11312, Medical Imaging 2020: Physics of Medical Imaging; 113124L (2020) https://doi.org/10.1117/12.2549275
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

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.

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Tonghe Wang, Joseph Harms, Yang Lei, Beth Ghavidel, William Stokes, Tian Liu, Walter J. Curran, Mark McDonald, Jun Zhou, and Xiaofeng Yang "Deep learning-based relative stopping power mapping generation with cone-beam CT in proton radiation therapy", Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 113124L (16 March 2020); https://doi.org/10.1117/12.2549275

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KEYWORDS

Computed tomography

Radiotherapy

Therapeutics

Cancer

Calibration

Medical physics

Tissues

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