気管支患者における光子計数およびエネルギー積分型コンピューテッドトモグラフィーによる気道定量化

Accurate airway measurement is critical for bronchitis quantification with computed tomography (CT), yet optimal protocols and the added value of photon-counting CT (PCCT) over energy-integrating CT (EICT) for reducing bias remain unclear. We quantified biomarker accuracy across modalities and protocols and assessed strategies to reduce bias.

A virtual imaging trial with 20 bronchitis anthropomorphic models was scanned using a validated simulator for two systems (EICT: SOMATOM Flash; PCCT: NAEOTOM Alpha) at 6.3 and 12.6 mGy. Reconstructions varied algorithm, kernel sharpness, slice thickness, and pixel size. Pi10 (square-root wall thickness at 10-mm perimeter) and WA% (wall-area percentage) were compared against ground-truth airway dimensions obtained from the 0.1-mm-precision anatomical models prior to CT simulation. External validation used clinical PCCT ( $n=22$ ) and EICT ( $n=80$ ).

Simulated airway dimensions agreed with pathological references ( $R=0.89-0.93$ ). PCCT had lower errors than EICT across segmented generations ( $p<0.05$ ). Under optimal parameters, PCCT improved Pi10 and WA% accuracy by 26.3% and 64.9%. Across the tested PCCT and EICT imaging protocols, improvements were associated with sharper kernels (25.8% Pi10, 33.0% WA%), thinner slices (23.9% Pi10, 49.8% WA%), smaller pixels (17.0% Pi10, 23.1% WA%), and higher dose ( $\le 3.9\%$ ). Clinically, PCCT achieved higher maximum airway generation ( $8.8\pm 0.5$ versus $6.0\pm 1.1$ ) and lower variability, mirroring trends in virtual results.

PCCT improves the accuracy and consistency of airway biomarker quantification relative to EICT, particularly with optimized protocols. The validated virtual platform enables modality-bias assessment and protocol optimization for accurate, reproducible bronchitis measurements.