Evaluation of Stapes Image Quality with Ultra-High-Resolution CT in Comparison with Conebeam CT and High-Resolution CT in Cadaveric Heads
Year: 2025
Type: article
Abstract: ABSTRACT Background and Purpose: The purpose of this study was to evaluate the ability of high-resolution (HR), ultra-high-resolution (UHR) with and without deep learning reconstruction (DLR), and cone-beam (CB) CT scanners to image the stapes using micro-CT as a reference. Materials and methods: 11 temporal bone specimens were imaged using all imaging modalities. Subjective image analysis was performed by grading image quality on a Likert scale, and objective image analysis was performed by taking various measurements of the stapes superstructure and footplate. Image noise and radiation dose were also recorded. Results: The global image quality scores were all worse than micro-CT (P ≤ 0.01). UHR-CT with and without DLR had the second-best global image quality scores (P > 0.99), which were both better than CB-CT (P = 0.01 for both). CB-CT had a better global image quality score than HR-CT (P = 0.01). Most of the measurements differed between HR-CT and micro-CT (P ≤ 0.02), but not between UHR-CT with and without DLR, CB-CT, and micro-CT (P > 0.06). The air noise value of UHR-CT with DLR was not different from CB-CT (P = 0.49), but HR-CT and UHR-CT without DLR exhibited higher values than UHR-CT with DLR (P ≤ 0.001). HR-CT and UHR-CT with and without DLR yielded the same effective radiation dose values of 1.23 ± 0.11 (1.13-1.35) mSv, which was four times higher than that of CB-CT (0.35 ± 0 mSv, P ≤ 0.01). Conclusion: UHR-CT with and without DLR offers comparable objective image analysis to CB-CT while providing superior subjective image quality. However, this is achieved at the cost of a higher radiation dose. Both CB-CT and UHR-CT with and without DLR are more effective than HR-CT in objective and subjective image analysis. ABBREVIATIONS: CB: Cone beam; CT: Computed tomography; DLR: Deep learning reconstruction; HR: High-resolution; UHR: Ultra-high resolution
DOI: 10.3174/ajnr.a8748
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4408836394