An inhomogeneous most likely path formalism for proton computed tomography

TL;DR

This paper introduces an inhomogeneous most likely path formalism for proton CT that improves path estimation accuracy in heterogeneous tissues by accounting for material composition, with potential benefits in clinical imaging accuracy.

Contribution

The authors develop a new MLP formalism based on scattering moments in inhomogeneous media, enhancing proton path prediction accuracy over traditional water-based models.

Findings

Predicted proton paths within 1.0 mm accuracy in simulations.

Improved accuracy by up to 17% over conventional models.

Reduced computation time by half with the MLP-Spline-Hybrid method.

Abstract

Purpose: Multiple Coulomb scattering (MCS) poses a challenge in proton CT (pCT) image reconstruction. The assumption of straight paths is replaced with Bayesian models of the most likely path (MLP). Current MLP-based pCT reconstruction approaches assume a water scattering environment. We propose an MLP formalism based on accurate determination of scattering moments in inhomogeneous media. Methods: Scattering power relative to water (RScP) was calculated for a range of human tissues and investigated against relative stopping power (RStP). Monte Carlo simulation was used to compare the new inhomogeneous MLP formalism to the water approach in a slab geometry and a human head phantom. An MLP-Spline-Hybrid method was investigated for improved computational efficiency. Results: A piecewise-linear correlation between RStP and RScP was shown, which may assist in iterative pCT reconstruction. The inhomogeneous formalism predicted Monte Carlo proton paths through a water cube with thick bone inserts to within 1.0 mm for beams ranging from 210 to 230 MeV incident energy. Improvement in accuracy over the conventional MLP ranged from 5% for a 230 MeV beam to 17% for 210 MeV. There was no noticeable gain in accuracy when predicting 200 MeV proton paths through a clinically relevant human head phantom. The MLP-Spline-Hybrid method reduced computation time by half while suffering negligible loss of accuracy. Conclusions: We have presented an MLP formalism that accounts for material composition. In most clinical cases a water scattering environment can be assumed, however in certain cases of significant heterogeneity the proposed algorithm may improve proton path estimation.