title: On the use of a proton path probability map for proton computed tomography reconstruction.
abstract: OBJECTIVE
  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.
authors.affiliation: Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, 53792, USA.
authors.forename: Dongxu
authors.initials: D
authors.lastname: Wang
authors.forename: T Rockwell
authors.initials: TR
authors.lastname: Mackie
authors.forename: Wolfgang A
authors.initials: WA
authors.lastname: Tomé
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011522:Protons; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity
chemical_list: D011522:Protons