Otol Neurotol. 2025 Oct 28. doi: 10.1097/MAO.0000000000004647. Online ahead of print.
ABSTRACT
HYPOTHESIS: Photon-counting detector CT (PCD-CT) with deep learning-supported denoising can significantly reduce the radiation dose for cochlear implant (CI) planning without compromising the accuracy of cochlear duct length (CDL) measurements.
BACKGROUND: Optimal electrode placement in CI surgery requires detailed cochlear anatomy from CT scans, but reducing radiation exposure is critical. This study explores PCD-CT with denoising algorithms to lower doses while preserving diagnostic accuracy.
METHODS: Four body donors without inner ear malformations were scanned using PCD-CT at 100%, 50%, 25%, 10%, and 5% dose levels. Images were denoised with ClariAce, a deep learning algorithm, and CDL was measured using OTOPLAN software. Neurotologists compared the results to manual segmentations. Statistical analyses evaluated accuracy across dose levels, with Bland-Altman plots assessing systematic errors.
RESULTS: Automatic segmentation succeeded across all doses but showed increased failure below 50%. At 100% and 50% doses, CDL measurements closely matched the gold standard, with minor deviations (eg, -0.17 mm at 50%). Below 50%, CDL underestimation increased (-1.25 mm at 25% and -4.0 mm at 5%). Denoising improved segmentation but minimally affected CDL accuracy at low doses, where manual segmentation performed better.
CONCLUSIONS: PCD-CT enables significant dose reduction for CI planning, with reliable CDL accuracy down to 50%. Deep learning denoising enhances image quality but is less effective below 50%, necessitating manual segmentation. These findings align with ALARA principles and suggest further refinement of AI algorithms for lower-dose applicability in CI diagnostics.
PMID:41151028 | DOI:10.1097/MAO.0000000000004647
