Ultra-High-Resolution CT of the Head and Neck with Deep Learning Reconstruction—Assessment of Image Quality and Radiation Exposure and Intraindividual Comparison with Normal-Resolution CT

Abstract

Objectives: To assess the benefits of ultra-high-resolution CT (UHR-CT) with deep learning–based image reconstruction engine (AiCE) regarding image quality and radiation dose and intraindividually compare it to normal-resolution CT (NR-CT). Methods: Forty consecutive patients with head and neck UHR-CT with AiCE for diagnosed head and neck malignancies and available prior NR-CT of a different scanner were retrospectively evaluated. Two readers evaluated subjective image quality using a 5-point Likert scale regarding image noise, image sharpness, artifacts, diagnostic acceptability, and assessability of various anatomic regions. For reproducibility, inter-reader agreement was analyzed. Furthermore, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and slope of the gray-value transition between different tissues were calculated. Radiation dose was evaluated by comparing CTDIvol, DLP, and mean effective dose values. Results: UHR-CT with AiCE reconstruction led to significant improvement in subjective (image noise and diagnostic acceptability: p < 0.000; ICC ≥ 0.91) and objective image quality (SNR: p < 0.000; CNR: p < 0.025) at significantly lower radiation doses (NR-CT 2.03 ± 0.14 mSv; UHR-CT 1.45 ± 0.11 mSv; p < 0.0001) compared to NR-CT. Conclusions: Compared to NR-CT, UHR-CT combined with AiCE provides superior image quality at a markedly lower radiation dose. With improved soft tissue assessment and potentially improved tumor detection, UHR-CT may add further value to the role of CT in the assessment of head and neck pathologies.