Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images.

Abstract

Objective: Contrast agents in CT scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition. Approach: A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition (ETD) technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρ_e), particle stopping power (SPR), and photon energy absorption coefficient (EAC) Main results: The proposed method yields accurate voxelwise estimations for ρ_e, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% for ρ_e, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bone. Significance: The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.