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Photon counting detectors (PCDs) offer promising advancements in computedtomography (CT) imaging by enabling the quantification and 3D imaging ofcontrast agents and tissue types through multi-energy projections. However, theaccuracy of these decomposition methods hinges on precise composite spectralattenuation values that one must reconstruct from spectral micro CT. Factorssuch as surface defects, local temperature, signal amplification, and impuritylevels can cause variations in detector efficiency between pixels, leading tosignificant quantitative errors. In addition, some inaccuracies such as thecharge-sharing effects in PCDs are amplified with a high Z sensor material andalso with a smaller detector pixels that are preferred for micro CT. In thiswork, we propose a comprehensive approach that combines practicalinstrumentation and measurement strategies leading to the quantitation ofmultiple materials within an object in a spectral micro CT with a photoncounting detector. Our Iterative Clustering Material Decomposition (ICMD)includes an empirical method for detector spectral response corrections,cluster analysis and multi-step iterative material decomposition. Utilizing aCdTe-1mm Medipix detector with a 55$\mu$m pitch, we demonstrate thequantitatively accurate decomposition of several materials in a phantom study,where the sample includes mixtures of material, soft material and K-edgematerials. We also show an example of biological sample imaging and separatingthree distinct types of tissue in mouse: muscle, fat and bone. Our experimentalresults show that the combination of spectral correction and high-dimensionaldata clustering enhances decomposition accuracy and reduces noise in micro CT.This ICMD allows for quantitative separation of more than three materialsincluding mixtures and also effectively separates multi-contrast agents.

Iterative Clustering Material Decomposition Aided by Empirical Spectral Correction for High-Resolution Photon-Counting Detectors in Micro-CT