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.
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.
Authors: Tianfang Li; Zhengrong Liang; Jayalakshmi V Singanallur; Todd J Satogata; David C Williams; Reinhard W Schulte Journal: Med Phys Date: 2006-03 Impact factor: 4.071
Authors: K M Hanson; J N Bradbury; R A Koeppe; R J Macek; D R Machen; R Morgado; M A Paciotti; S A Sandford; V W Steward Journal: Phys Med Biol Date: 1982-01 Impact factor: 3.609
Authors: Valentina Giacometti; Vladimir A Bashkirov; Pierluigi Piersimoni; Susanna Guatelli; Tia E Plautz; Hartmut F-W Sadrozinski; Robert P Johnson; Andriy Zatserklyaniy; Thomas Tessonnier; Katia Parodi; Anatoly B Rosenfeld; Reinhard W Schulte Journal: Med Phys Date: 2017-03 Impact factor: 4.506