Standardized assessment of vascular reconstruction kernels in photon-counting CT angiographies of the leg using a continuous extracorporeal perfusion model.

This study evaluated the influence of different vascular reconstruction kernels on the image quality of CT angiographies of the lower extremity runoff using a 1st-generation photon-counting-detector CT (PCD-CT) compared with dose-matched examinations on a 3rd-generation energy-integrating-detector CT (EID-CT). Inducing continuous extracorporeal perfusion in a human cadaveric model, we performed CT angiographies of eight upper leg arterial runoffs with radiation dose-equivalent 120 kVp acquisition protocols (CTDI 5 mGy). Reconstructions were executed with different vascular kernels, matching the individual modulation transfer functions between scanners. Signal-to-noise-ratios (SNR) and contrast-to-noise-ratios (CNR) were computed to assess objective image quality. Six radiologists evaluated image quality subjectively using a forced-choice pairwise comparison tool. Interrater agreement was determined by calculating Kendall's concordance coefficient (W). The intraluminal attenuation of PCD-CT images was significantly higher than of EID-CT (414.7 ± 27.3 HU vs. 329.3 ± 24.5 HU; p < 0.001). Using comparable kernels, image noise with PCD-CT was significantly lower than with EID-CT (p ≤ 0.044). Correspondingly, SNR and CNR were approximately twofold higher for PCD-CT (p < 0.001). Increasing the spatial frequency for PCD-CT reconstructions by one level resulted in similar metrics compared to EID-CT (CNR; EID-CT Bv49: 21.7 ± 3.7 versus PCD-CT Bv60: 21.4 ± 3.5). Overall image quality of PCD-CTA achieved ratings superior to EID-CTA irrespective of the used reconstruction kernels (best: PCD-CT Bv60; worst: EID-CT Bv40; p < 0.001). Interrater agreement was good (W = 0.78). Concluding, PCD-CT offers superior intraluminal attenuation, SNR, and CNR compared to EID-CT in angiographies of the upper leg arterial runoff. Combined with improved subjective image quality, PCD-CT facilitates the use of sharper convolution kernels and ultimately bears the potential of improved vascular structure assessability.