@inproceedings{ac18c54375f64573b20ea33d6c0c80bd,
title = "Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction",
abstract = "Accuracy in proton range prediction is critical in proton therapy to ensure conformal tumor dose. Our lab proposed a joint statistical image reconstruction method (JSIR) based on a basis vector model (BVM) for estimation of stopping power ratio maps and demonstrated that it outperforms competing Dual Energy CT (DECT) methods. However, no study has been performed on the clinical utility of our method. Here, we study the resulting dose prediction error, the difference between the dose delivered to tissue based on the more accurate JSIR-BVM method and the planned dose based on Single Energy CT (SECT).",
keywords = "dual-energy computed tomography, model-based image reconstruction, proton therapy, statistical image reconstruction",
author = "{Medrano Matamoros}, {Maria Jose} and Xinyuan Chen and Tao Ge and Tianyu Zhao and Rui Liao and Politte, {David G.} and Willamson, {Jeffrey F.} and Whiting, {Bruce R.} and Yao Hao and Baozhou Sun and O'Sullivan, {Joseph A.}",
note = "Funding Information: This project is supported by R01 CA212638 and Imaging Sciences Pathway T32EB014855 (MM) from the United States National Institutes of Health. We thank the Siteman Cancer Center for their help in the acquisition of clinical data and Dr. Choonsik Lee from the National Cancer Institute for providing the CT scans and the corresponding segmentation that were used for the development of our simulated patient study. Publisher Copyright: {\textcopyright} 2022 SPIE.; null ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2611801",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Wei Zhao and Lifeng Yu",
booktitle = "Medical Imaging 2022",
}