Learning-Based Synthetic Dual Energy CT Imaging from Single Energy CT for Stopping Power Ratio Calculation in Proton Radiation Therapy

• Type of Material: Article
• Authors: Serdar Charyyev, Emory University; Tonghe Wang, Emory University; Yang Lei, Emory University; Beth Ghavidel, Emory University; Jonathan J. Beitler, Emory University; Mark McDonald, Emory University; Walter J Curran, Emory University; Tian Liu, Emory University; Jun Zhou, Emory University; Xiaofeng Yang, Emory University
• Language: English
• Date: 2020-05-25
• Publisher: Wiley
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: 2020 Medical Physics
• Title of Journal or Parent Work: Medical Physics
• ISSN: 0094-2405
• Grant/Funding Information: This research is supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718, and Emory Winship Pilot Grant.
• Keywords: proton radiation therapy; accuracy; photon interaction; dual energy CT
• Research Categories: Health Sciences, Radiology

Abstract

Purpose: Dual-energy CT (DECT) has been shown to derive stopping power ratio (SPR) map with higher accuracy than conventional single energy CT (SECT) by obtaining the energy dependence of photon interactions. However, DECT is not as widely implemented as SECT in proton radiation therapy simulation. This work presents a learning-based method to synthetize DECT images from SECT for proton radiation therapy. Methods: The proposed method uses a residual attention generative adversarial network. Residual blocks with attention gates were used to force the model focus on the difference between DECT maps and SECT images. To evaluate the accuracy of the method, we retrospectively investigated 20 head-and-neck cancer patients with both DECT and SECT scans available. The high and low energy CT images acquired from DECT acted as learning targets in the training process for SECT datasets and were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. To evaluate our method in the context of a practical application, we generated SPR maps from sDECT using physics-based dual-energy stoichiometric method and compared the maps to those generated from DECT. Results: The synthesized DECT images showed an average mean absolute error around 30 Hounsfield Unit (HU) across the whole-body volume. The corresponding SPR maps generated from synthetic DECT showed an average normalized mean square error of about 1% with reduced noise level and artifacts than those from original DECT. Conclusions: The accuracy of the synthesized DECT image by our machine-learning-based method was evaluated on head and neck patient, and potential feasibility for proton treatment planning and dose calculation was shown by generating SPR map using the synthesized DECT.