On the use of a proton path probability map for proton computed tomography reconstruction

Dongxu Wang, T. Rockwell MacKie, Wolfgang A. Tome

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

Purpose: To describe a method to estimate the proton path in proton computed tomography (pCT) reconstruction, which is based on the probability of a proton passing through each point within an object to be imaged. Methods: Based on multiple Coulomb scattering and a semianalytically derived model, the conditional probability of a proton passing through each point within the object given its incoming and exit condition is calculated in a Bayesian inference framework, employing data obtained from Monte Carlo simulation using GEANT4. The conditional probability at all of the points in the reconstruction plane forms a conditional probability map and can be used for pCT reconstruction. Results: From the generated conditional probability map, a most-likely path (MLP) and a 90% probability envelope around the most-likely path can be extracted and used for pCT reconstruction. The reconstructed pCT image using the conditional probability map yields a smooth pCT image with minor artifacts. pCT reconstructions obtained using the extracted MLP and the 90% probability envelope compare well to reconstructions employing the method of cubic spline proton path estimation. Conclusions: The conditional probability of a proton passing through each point in an object given its entrance and exit condition can be obtained using the proposed method. The extracted MLP and the 90% probability envelope match the proton path recorded in the GEANT4 simulation well. The generated probability map also provides a benchmark for comparing different path estimation methods.

Original languageEnglish (US)
Pages (from-to)4138-4145
Number of pages8
JournalMedical Physics
Volume37
Issue number8
DOIs
StatePublished - Aug 2010
Externally publishedYes

Fingerprint

Protons
Tomography
Benchmarking
Artifacts

Keywords

  • algebraic reconstruction
  • Bayesian inference
  • image reconstruction
  • most-likely path estimation
  • pCT reconstruction

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

On the use of a proton path probability map for proton computed tomography reconstruction. / Wang, Dongxu; MacKie, T. Rockwell; Tome, Wolfgang A.

In: Medical Physics, Vol. 37, No. 8, 08.2010, p. 4138-4145.

Research output: Contribution to journalArticle

@article{8af7802340044d7a8a5c17092190c951,
title = "On the use of a proton path probability map for proton computed tomography reconstruction",
abstract = "Purpose: To describe a method to estimate the proton path in proton computed tomography (pCT) reconstruction, which is based on the probability of a proton passing through each point within an object to be imaged. Methods: Based on multiple Coulomb scattering and a semianalytically derived model, the conditional probability of a proton passing through each point within the object given its incoming and exit condition is calculated in a Bayesian inference framework, employing data obtained from Monte Carlo simulation using GEANT4. The conditional probability at all of the points in the reconstruction plane forms a conditional probability map and can be used for pCT reconstruction. Results: From the generated conditional probability map, a most-likely path (MLP) and a 90{\%} probability envelope around the most-likely path can be extracted and used for pCT reconstruction. The reconstructed pCT image using the conditional probability map yields a smooth pCT image with minor artifacts. pCT reconstructions obtained using the extracted MLP and the 90{\%} probability envelope compare well to reconstructions employing the method of cubic spline proton path estimation. Conclusions: The conditional probability of a proton passing through each point in an object given its entrance and exit condition can be obtained using the proposed method. The extracted MLP and the 90{\%} probability envelope match the proton path recorded in the GEANT4 simulation well. The generated probability map also provides a benchmark for comparing different path estimation methods.",
keywords = "algebraic reconstruction, Bayesian inference, image reconstruction, most-likely path estimation, pCT reconstruction",
author = "Dongxu Wang and MacKie, {T. Rockwell} and Tome, {Wolfgang A.}",
year = "2010",
month = "8",
doi = "10.1118/1.3453767",
language = "English (US)",
volume = "37",
pages = "4138--4145",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "8",

}

TY - JOUR

T1 - On the use of a proton path probability map for proton computed tomography reconstruction

AU - Wang, Dongxu

AU - MacKie, T. Rockwell

AU - Tome, Wolfgang A.

PY - 2010/8

Y1 - 2010/8

N2 - Purpose: To describe a method to estimate the proton path in proton computed tomography (pCT) reconstruction, which is based on the probability of a proton passing through each point within an object to be imaged. Methods: Based on multiple Coulomb scattering and a semianalytically derived model, the conditional probability of a proton passing through each point within the object given its incoming and exit condition is calculated in a Bayesian inference framework, employing data obtained from Monte Carlo simulation using GEANT4. The conditional probability at all of the points in the reconstruction plane forms a conditional probability map and can be used for pCT reconstruction. Results: From the generated conditional probability map, a most-likely path (MLP) and a 90% probability envelope around the most-likely path can be extracted and used for pCT reconstruction. The reconstructed pCT image using the conditional probability map yields a smooth pCT image with minor artifacts. pCT reconstructions obtained using the extracted MLP and the 90% probability envelope compare well to reconstructions employing the method of cubic spline proton path estimation. Conclusions: The conditional probability of a proton passing through each point in an object given its entrance and exit condition can be obtained using the proposed method. The extracted MLP and the 90% probability envelope match the proton path recorded in the GEANT4 simulation well. The generated probability map also provides a benchmark for comparing different path estimation methods.

AB - Purpose: To describe a method to estimate the proton path in proton computed tomography (pCT) reconstruction, which is based on the probability of a proton passing through each point within an object to be imaged. Methods: Based on multiple Coulomb scattering and a semianalytically derived model, the conditional probability of a proton passing through each point within the object given its incoming and exit condition is calculated in a Bayesian inference framework, employing data obtained from Monte Carlo simulation using GEANT4. The conditional probability at all of the points in the reconstruction plane forms a conditional probability map and can be used for pCT reconstruction. Results: From the generated conditional probability map, a most-likely path (MLP) and a 90% probability envelope around the most-likely path can be extracted and used for pCT reconstruction. The reconstructed pCT image using the conditional probability map yields a smooth pCT image with minor artifacts. pCT reconstructions obtained using the extracted MLP and the 90% probability envelope compare well to reconstructions employing the method of cubic spline proton path estimation. Conclusions: The conditional probability of a proton passing through each point in an object given its entrance and exit condition can be obtained using the proposed method. The extracted MLP and the 90% probability envelope match the proton path recorded in the GEANT4 simulation well. The generated probability map also provides a benchmark for comparing different path estimation methods.

KW - algebraic reconstruction

KW - Bayesian inference

KW - image reconstruction

KW - most-likely path estimation

KW - pCT reconstruction

UR - http://www.scopus.com/inward/record.url?scp=78149487406&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149487406&partnerID=8YFLogxK

U2 - 10.1118/1.3453767

DO - 10.1118/1.3453767

M3 - Article

VL - 37

SP - 4138

EP - 4145

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 8

ER -