Iterative clustering material decomposition aided by empirical spectral correction for photon counting detectors in micro-CT

Photon counting detectors offer promising advancements in computed tomography (CT) imaging by enabling the quantification and three-dimensional imaging of contrast agents and tissue types through simultaneous multi-energy projections from broad X-ray spectra. However, the accuracy of these decomposition methods hinges on precise composite spectral attenuation values that one must reconstruct from spectral micro-CT. Errors in such estimations could be due to effects such as beam hardening, object scatter, or detector sensor-related spectral distortions such as fluorescence. Even if accurate spectral correction is done, multi-material separation within a volume remains a challenge. Increasing the number of energy bins in material decomposition problems often comes with a significant noise penalty but with minimal decomposition benefits.