A deterministic adjoint-based semi-analytical algorithm for fast response change computations in proton therapy

TL;DR

This paper introduces a fast, semi-analytical adjoint-based algorithm for accurately computing dose response changes in proton therapy, enabling efficient online adaptive treatment adjustments.

Contribution

It presents a novel deterministic method combining Fokker-Planck and Fermi-Eyges solutions with adjoint calculations for rapid dose change assessment in proton therapy.

Findings

Negligible errors for small perturbations (1.1e-6% to 3.6e-3%)

Moderate errors (3% to 17%) for large perturbations

Viable for real-time adaptive proton therapy applications

Abstract

In this paper we propose a solution to the need for a fast particle transport algorithm in Online Adaptive Proton Therapy capable of cheaply, but accurately computing the changes in patient dose metrics as a result of changes in the system parameters. We obtain the proton phase-space density through the product of the numerical solution to the one-dimensional Fokker-Planck equation and the analytical solution to the Fermi-Eyges equation. Moreover, a corresponding adjoint system was derived and solved for the adjoint flux. The proton phase-space density together with the adjoint flux and the metric (chosen as the energy deposited by the beam in a variable region of interest) allowed assessing the accuracy of our algorithm to different perturbation ranges in the system parameters and regions of interest. The algorithm achieved negligible errors ($1.1 \times 10^{-6}$ $\%$ to $3.6 \times 10^{-3}$ $\%$) for small perturbation ranges ($-40 \text{ HU to } 40 \text{ HU}$) and small to moderate errors ($3 \text{ $\%$ to } 17 \text{ $\%$}$) -- in line with the well-known limitation of adjoint approaches -- for large perturbation ranges ($-400 \text{ HU to } 400 \text{ HU}$) in the case of most clinical interest where the region of interest surrounds the Bragg peak. Given these results coupled with the capability of further improving the timing performance it can be concluded that our algorithm presents a viable solution for the specific purpose of Online Adaptive Proton Therapy.