Extension of the Fermi-Eyges most-likely path in heterogeneous medium with prior knowledge information

• Verfasst von: Collins-Fekete, Charles-Antoine [VerfasserIn]
• Verfasst von: Volz, Lennart [VerfasserIn]
• Verfasst von: Seco, Joao [VerfasserIn]
• Titel: Extension of the Fermi-Eyges most-likely path in heterogeneous medium with prior knowledge information
• Verf.angabe: Charles-Antoine Collins-Fekete, Esther Bär, Lennart Volz, Hugo Bouchard, Luc Beaulieu and Joao Seco
• E-Jahr: 2017
• Jahr: 21 November 2017
• Umfang: 14 S.
• Teil: volume:62
• Teil: year:2017
• Teil: number:24
• Teil: pages:1-14
• Teil: extent:14
• Fussnoten: Published 21 November 2017 ; Gesehen am 15.08.2018
• Titel Quelle: Enthalten in: Physics in medicine and biology
• Ort Quelle: Bristol : IOP Publ., 1956
• Jahr Quelle: 2017
• Band/Heft Quelle: 62(2017,24) Artikel-Nummer 9207, 14 Seiten
• ISSN Quelle: 1361-6560
• DOI: doi:10.1088/1361-6560/aa955d
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1580053580

Abstract

Particle imaging suffers from poor spatial resolution due to the multiple Coulomb scattering deflections undergone by the particles throughout their path. To account for these deflections, a most-likely path (MLP) formalism was developed based on a Bayesian adaption of the Fermi-Eyges theory. Previous work calculated the MLP formalism in a homogeneous water medium as an initial estimate. However, this potentially reduces the accuracy of the MLP estimate as well as the achievable resolution of the subsequent tomographic reconstruction. This work investigates the potential gain of introducing prior-knowledge on the medium composition and density to improve the MLP accuracy. To do so, a Monte Carlo (MC) Geant4 algorithm was used to simulate protons ( ##IMG## [http://ej.iop.org/images/0031-9155/62/24/9207/pmbaa955dieqn001.gif] $n=10^6$ ) crossing three different anthropomorphic phantoms representing the lung, abdomen, and head. The prior-knowledge information is gathered from (1) the MC simulation for ground-truth (MLP-GT), or from (2) a recent DECT material decomposition technique (MLP-DECT). The reconstructed path accuracy using prior-knowledge methods is compared with (3) the path reconstructed in homogeneous water (MLP-Water) and (4) a path reconstruction method where the proton path is projected onto a Hull at the boundary of the phantom with a subsequent MLP-Water calculation (MLP-Hull). For each path reconstruction method, the maximal root-mean-square error (RMS max ) is compared between the reconstructed and the MC path. In every phantom, the RMS max is decreased between the MLP-Water and the three other path algorithms that take into account heterogeneities ( ##IMG## [http://ej.iop.org/images/0031-9155/62/24/9207/pmbaa955dieqn002.gif] $-33%$ for the lung, ##IMG## [http://ej.iop.org/images/0031-9155/62/24/9207/pmbaa955dieqn003.gif] $-38%$ for the abdomen and ##IMG## [http://ej.iop.org/images/0031-9155/62/24/9207/pmbaa955dieqn004.gif] $-81%$ for the head), with no significant differences between each (MLP-DECT, MLP-GT and MLP-Hull). In conclusion, the introduction of prior-knowledge in the MLP formalism decreases the RMS uncertainty to the MC path, but no further than the use of a simpler Hull contour algorithm. The use of this Hull algorithm is suggested for future particle imaging applications.