Convolutional neural network based proton stopping-power-ratio estimation with dual-energy CT: a feasibility study. (4th November 2020)

• Record Type: Journal Article
• Authors: Lee, H HC; Park, YK; Duan, X; Jia, X; Jiang, S; Yang, M
• Is Part Of: Physics in medicine & biology. Volume 65:Number 21(2020:Nov.)
• Publication Date: 2020-11-04
• Subjects: dual-energy CT (DECT) -- range uncertainty -- stopping-power-ratio (SPR) estimation -- beam hardening artifact -- convolutional neural network (CNN); Biophysics -- Periodicals; Medical physics -- Periodicals; 610.153
• DOI: 10.1088/1361-6560/abab57
• Languages: English
• ISSNs: 0031-9155

Abstract

Abstract: Dual-energy computed tomography (DECT) has shown a great potential for lowering range uncertainties, which is necessary for truly leveraging the Bragg peak in proton therapy. However, analytical stopping-power-ratio (SPR) estimation methods have limitations in resolving the influence from the beam-hardening artifact, i.e. CT number variation of the same object scanned under different imaging conditions, such as different patient size and location in the field-of-view (FOV). We present a convolutional neural network (CNN)-based framework to estimate proton SPR that accounts for patient geometry variation and addresses CT number variation. The proposed framework was tested on both prostate and head-and-neck (HN) patient datasets. Simulated CT images were used in order to have a well-defined ground-truth SPR for evaluation. Two training scenarios were evaluated: training with patient CT images (ideal scenario) and training with computational phantoms (realistic scenario). For the training in ideal scenario, computational phantoms were created based on 120 kVp patient CT images using a custom-defined density and material translation curve. Then, 80 kVp and 150 kVp Sn DECT image pairs were obtained using ray-tracing simulation, and their corresponding SPR was calculated from the known density and elemental compositions. For the training in realistic scenario, computational phantoms were created based on the geometry of calibration phantoms. For both scenarios, evaluation was performed on the phantoms created from patient CT images. Compared to a conventional parametric model, U-net trained with computational phantoms (realistic scenario) reduced the SPR estimation uncertainty (95th percentile) of the prostate patient from 1.10% to 0.71%, and HN patient from 2.11% to 1.20%. With the U-net trained with patient images (ideal scenario) uncertainty values were 0.32% and 0.42% for prostate and HN patients, respectively. These results suggest that CNN has great potential to improve the accuracy of SPR estimation in proton therapy by incorporating individual patient geometry information.