## 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) |
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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 |

## Keywords

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

## ASJC Scopus subject areas

- Biophysics
- Radiology Nuclear Medicine and imaging