### 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 language | English (US) |
---|---|

Pages (from-to) | 4138-4145 |

Number of pages | 8 |

Journal | Medical Physics |

Volume | 37 |

Issue number | 8 |

DOIs | |

State | Published - Aug 2010 |

Externally published | Yes |

### Fingerprint

### 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

*Medical Physics*,

*37*(8), 4138-4145. https://doi.org/10.1118/1.3453767

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

Research output: Contribution to journal › Article

*Medical Physics*, vol. 37, no. 8, pp. 4138-4145. https://doi.org/10.1118/1.3453767

}

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 -