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Proton radiography in three dimensions: A proof of principle of a new technique
Author(s) -
Raytchev Milen,
Seco Joao
Publication year - 2013
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4822487
Subject(s) - imaging phantom , monte carlo method , proton , fluence , radiography , proton therapy , range (aeronautics) , scattering , materials science , physics , computational physics , optics , mathematics , nuclear physics , laser , statistics , composite material
Purpose: Monte Carlo simulations were used to investigate a range of phantom configurations to establish enabling three‐dimensional proton radiographic techniques.Methods: A large parameter space of stacked phantom geometries composed of tissue inhomogeneity materials such as lung, bone, and cartilage inserted within water background were simulated using a purposefully modified version of TOPAS , an application running on top of the GEANT4 Monte Carlo code. The phantoms were grouped in two classes, one with the inhomogeneity inserted only half‐way in the lateral direction and another with complete inhomogeneity insertion. The former class was used to calculate the track count and the energy fluence of the protons as they exit the phantoms either having traversed the inhomogeneity or not. The latter class was used to calculate one yield value accounting for loss of protons due to physical processes only and another yield value accounting for deliberately discarded protons due to large scattering angles. A graphical fingerprinting method was developed to determine the inhomogeneity thickness and location within the phantom based on track count and energy fluence information. Two additional yield values extended this method to the general case which also determines the inhomogeneity material and the phantom thickness.Results: The graphical fingerprinting method was manually validated for two, and automatically tested for all, tissue materials using an exhaustive set of inhomogeneity geometries for 16 cm thick phantoms. Unique recognition of test phantom configurations was achieved in the large majority of cases. The method in the general case was further tested using an exhaustive set of inhomogeneity and phantom tissues and geometries where the phantom thicknesses ranged between 8 and 24 cm. Unique recognition of the test phantom configurations was achieved only for part of the phantom parameter space. The correlations between the remaining false positive recognitions were analyzed.Conclusions: The concept of 3D proton radiography for tissue inhomogeneities of simple geometries was established with the current work. In contrast to conventional 2D proton radiography, the main objective of the demonstrated 3D technique is not proton range. Rather, it is to measure the depth and thickness of an inhomogeneity located in an imaged geometry. Further work is needed to extend and apply the method to more complex geometries.