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. Conclusion: 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.

(1) Background: To evaluate diagnostic image quality and radiation exposure of ultra-high resolution cerebral Computed-Tomography (CT) angiography (CTA) obtained on an ultra-high resolution computed tomography scanner (UHR-CT). (2) Methods: Fifty consecutive patients with UHR-CTA were enrolled. Image reconstruction was processed with a 1024 × 1024 matrix and a slice thickness of 0.25 mm. Quantitative analyses comprising CT values, contrast–noise ratio (CNR) and signal-to-noise ratio (SNR) were performed. Subjective assessment of image quality, vessel contrast, noise, artefacts and delineation of different sized vessels were assessed by two readers on a 4-point scale. Radiation exposure was determined. (3) Results: Hounsfield values (ACI: 461.8 ± 16.8 HU; MCA: 406.1 ± 24.2 HU; BA: 412.2 ± 22.3 HU), SNR (ACI: 35.4 ± 13.1; MCA: 20.8 ± 12.4; BA: 23.7 ± 12.9) and CNR (ACI: 48.7 ± 21; MCA: 63.9 ± 26.9; BA: 48.1 ± 21.4) were remarkably high in all segments. Subjective analysis by two raters (fair agreement, k = 0.26) indicated excellent image qualities (image quality = 4; contrast = 4; noise = 3; artefacts = 4).Our analysis revealed a notably high traceability of the cerebral perforators (3 Points). Radiation exposure was at moderate dose levels (effective dose = 2.5 ± 0.6mSv). (4) Conclusions: UHR-CTA generates highly valuable image qualities that allow the depiction of vessels including cerebral perforators at acceptable dose levels. The UHR-CTA may therefore enhance the detection of small cerebral pathologies and may improve interpretability, especially in settings where high image qualities are crucial for the diagnostic accuracy.