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Reconstruction for proton computed tomography by tracing proton trajectories: A Monte Carlo study
Author(s) -
Li Tianfang,
Liang Zhengrong,
Singanallur Jayalakshmi V.,
Satogata Todd J.,
Williams David C.,
Schulte Reinhard W.
Publication year - 2006
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 - 10.1118/1.2171507
Subject(s) - monte carlo method , imaging phantom , proton therapy , spline (mechanical) , iterative reconstruction , algorithm , path (computing) , algebraic reconstruction technique , scattering , proton , computer science , physics , computational physics , mathematics , artificial intelligence , optics , statistics , nuclear physics , thermodynamics , programming language
Proton computed tomography (pCT) has been explored in the past decades because of its unique imaging characteristics, low radiation dose, and its possible use for treatment planning and on‐line target localization in proton therapy. However, reconstruction of pCT images is challenging because the proton path within the object to be imaged is statistically affected by multiple Coulomb scattering. In this paper, we employ GEANT4 ‐based Monte Carlo simulations of the two‐dimensional pCT reconstruction of an elliptical phantom to investigate the possible use of the algebraic reconstruction technique (ART) with three different path‐estimation methods for pCT reconstruction. The first method assumes a straight‐line path (SLP) connecting the proton entry and exit positions, the second method adapts the most‐likely path (MLP) theoretically determined for a uniform medium, and the third method employs a cubic spline path (CSP). The ART reconstructions showed progressive improvement of spatial resolution when going from the SLP [2 line pairs (lp)cm − 1] to the curved CSP and MLP path estimates ( 5 lpcm − 1 ) . The MLP‐based ART algorithm had the fastest convergence and smallest residual error of all three estimates. This work demonstrates the advantage of tracking curved proton paths in conjunction with the ART algorithm and curved path estimates.