Exploring bias in spectral CT material decomposition: a simulation-based approach

Computed tomography (CT) imaging has seen significant advancements with the introduction of spectral CT, which improves material differentiation by acquiring images at multiple energy levels. Photon-counting CT (PCCT) is an emerging technique to implement spectral CT with photon counting detectors that may discriminate detected photon energies to different energy bins. Material differentiation is achieved by decomposing the acquired data into two-material models such as brain/bone or brain/iodine. However, such decomposition is susceptible to bias due to inaccurate physical modeling. In this study, we aim to study the relationship between the material decomposition bias and the energy thresholds used in PCCT, under ideal, noiseless models.

A projection-based material decomposition model was used to directly decompose projection data. Bias simulation was performed using a Shepp-Logan phantom with brain/bone and brain/iodine as basis materials. X-ray spectra were generated using a fixed 10 keV threshold and a varying threshold sampled from 20 to 90 keV, with extra sampling points around iodine's k-edge. Virtual monoenergetic images (VMIs) at 60 keV and 140 keV were analyzed to evaluate bias for each material and material pair.

Lower energy thresholds (<40 keV) introduced a larger bias in material decomposition, with peaks observed between 30 and 40 keV, particularly around the k-edge of iodine. The bias generally decreased with increasing thresholds above 50 keV, especially for non-basis materials. This trend was consistent across brain/bone and brain/iodine bases and for both 60 and 140 keV VMIs.

Energy thresholds significantly affect the accuracy of projection-based material decomposition in PCCT. Greater differences between thresholds lead to reduced decomposition bias. Future research should incorporate non-ideal detector responses and noise, as well as explore image-domain decomposition and real phantom studies with possible translation to improve patient care.