A Bayesian approach to solve proton stopping powers from noisy multi‐energy CT data

Abstract

The Bayesian ETD is shown to be more robust against noise than similar methods and a promising approach to extract SPR from noisy DECT data. In the advent of commercially available multi-energy CT or photon-counting CT scanners, the Bayesian ETD is expected to allow extracting more information and improve the precision of proton therapy beyond DECT.

Theoretical and experimental analysis of photon counting detector CT for proton stopping power prediction

“…The standard deviation of the image noise distribution for each energy bin depended on the number of energy bins, , as in Lalonde et al …”

“…have recently proposed new SPR methods for PCD‐CT. Based on computer simulations or multiple regular SECT scans, all three studies found that the root‐mean‐square error (RMSE) for SPR estimation decreased when the number of energy bins increased …”

Experimental validation of two dual‐energy CT methods for proton therapy using heterogeneous tissue samples

“…The SPR values are calculated from elemental compositions using the Bragg additivity rule as well as the Bethe–Bloch formula. In this study, we investigate an adaptation of this method especially made for SPR prediction from noisy multienergy CT data …”

“…For the method of Lalonde et al., we use the measured CT numbers of the phantom to calibrate the Z ‐space coefficients, as proposed in Ref. , eq.…”

“…Alternative methods parameterize the I ‐value or the SPR directly, while other groups focus on calibrating pseudo‐monoenergetic images derived from DECT to predict the SPR of tissues . Another class of formalisms aims at extracting elemental compositions as inputs for Monte Carlo simulations . In a previous study, we performed a theoretical study on the potential of DECT to reduce proton range uncertainties .…”