Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction

Maria Jose Medrano Matamoros; Xinyuan Chen; Tao Ge; Tianyu Zhao; Rui Liao; David G. Politte; Jeffrey F. Willamson; Bruce R. Whiting; Yao Hao; Baozhou Sun; Joseph A. O'Sullivan

doi:10.1117/12.2611801

Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction

Maria Jose Medrano Matamoros, Xinyuan Chen, Tao Ge, Tianyu Zhao, Rui Liao, David G. Politte, Jeffrey F. Willamson, Bruce R. Whiting, Yao Hao, Baozhou Sun, Joseph A. O'Sullivan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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).

Original language	English
Title of host publication	Medical Imaging 2022
Subtitle of host publication	Physics of Medical Imaging
Editors	Wei Zhao, Lifeng Yu
Publisher	SPIE
ISBN (Electronic)	9781510649378
DOIs	https://doi.org/10.1117/12.2611801
State	Published - 2022
Event	Medical Imaging 2022: Physics of Medical Imaging - Virtual, Online Duration: Mar 21 2022 → Mar 27 2022

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	12031
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2022: Physics of Medical Imaging
City	Virtual, Online
Period	03/21/22 → 03/27/22

Keywords

dual-energy computed tomography
model-based image reconstruction
proton therapy
statistical image reconstruction

Access to Document

10.1117/12.2611801

Cite this

Medrano Matamoros, M. J., Chen, X., Ge, T., Zhao, T., Liao, R., Politte, D. G., Willamson, J. F., Whiting, B. R., Hao, Y., Sun, B., & O'Sullivan, J. A. (2022). Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction. In W. Zhao, & L. Yu (Eds.), Medical Imaging 2022: Physics of Medical Imaging Article 1203119 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12031). SPIE. https://doi.org/10.1117/12.2611801

@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 = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Physics of Medical Imaging ; 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",

}

Medrano Matamoros, MJ, Chen, X, Ge, T, Zhao, T, Liao, R, Politte, DG, Willamson, JF, Whiting, BR, Hao, Y, Sun, B & O'Sullivan, JA 2022, Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction. in W Zhao & L Yu (eds), Medical Imaging 2022: Physics of Medical Imaging., 1203119, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12031, SPIE, Medical Imaging 2022: Physics of Medical Imaging, Virtual, Online, 03/21/22. https://doi.org/10.1117/12.2611801

Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction. / Medrano Matamoros, Maria Jose; Chen, Xinyuan; Ge, Tao et al.
Medical Imaging 2022: Physics of Medical Imaging. ed. / Wei Zhao; Lifeng Yu. SPIE, 2022. 1203119 (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12031).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction

AU - Medrano Matamoros, Maria Jose

AU - Chen, Xinyuan

AU - Ge, Tao

AU - Zhao, Tianyu

AU - Liao, Rui

AU - Politte, David G.

AU - Willamson, Jeffrey F.

AU - Whiting, Bruce R.

AU - Hao, Yao

AU - Sun, Baozhou

AU - O'Sullivan, Joseph A.

PY - 2022

Y1 - 2022

N2 - 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).

AB - 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).

KW - dual-energy computed tomography

KW - model-based image reconstruction

KW - proton therapy

KW - statistical image reconstruction

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U2 - 10.1117/12.2611801

DO - 10.1117/12.2611801

M3 - Conference contribution

AN - SCOPUS:85131174698

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2022

A2 - Zhao, Wei

A2 - Yu, Lifeng

PB - SPIE

T2 - Medical Imaging 2022: Physics of Medical Imaging

Y2 - 21 March 2022 through 27 March 2022

ER -

Medrano Matamoros MJ, Chen X, Ge T, Zhao T, Liao R, Politte DG et al. Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction. In Zhao W, Yu L, editors, Medical Imaging 2022: Physics of Medical Imaging. SPIE. 2022. 1203119. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2611801

Dose prediction in proton cancer therapy based on density maps from dual-energy CT using joint statistical image reconstruction

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