In photon-counting CT, detector energy thresholds directly affect image quality attributes such as contrast and noise. The purpose of this study was to identify optimum energy thresholds using a comprehensive virtual clinical trial platform. The virtual trial was done using a computational, anthropomorphic phantom and a photon-counting CT simulator. The phantom (adult male, 50th percentile body mass index) was chosen from the library of extended cardiac-torso (XCAT) phantoms. A vessel growth algorithm was used to model detailed hepatic arteries within the liver. Five computational lesions were inserted to the liver. The phantom was “imaged” with iodinated contrast at three contrast phases, i.e. arterial, portal venous, and delayed phases. All virtual sinograms were simulated using a validated CT simulator (DukeSim) modeling a prototype photon-counting scanner (CounT, Siemens Healthcare). The simulator included an energy- and threshold-dependent detector model accounting for X-ray and electronic effects and noise. At each contrast phase, the phantom was imaged at 35 detector energy threshold combinations with five lower-energy thresholds (20-40 keV) and seven upper-energy thresholds (50-80 keV). For each scan, two “threshold” images (photons detected beyond a threshold) and one “bin” image (photons detected between the two thresholds) were acquired. Noise magnitude, image contrast, and contrast-to-noise ratio (CNR) were measured in the aorta and the liver lesions. Optimum energy thresholds were identified as those yielding higher CNRs. From the simulations, noise magnitude was found to increase and CNR decrease with the increase in energy thresholds. Keeping the low threshold constant, the noise decreased and CNR increased with the increase in the upper energy threshold. Therefore, our results suggest that a combination of a lower threshold at 20-30 keV and upper threshold at 80 keV maximize the image quality. This study demonstrates an application of a virtual trial platform to quantify and optimize the quality of photon-counting CT.