Available online 16 February 2024

Ultra-high resolution CT is superior to conventional high-resolution CT for depicting the fine anatomy of the temporal bone.

The use of deep learning and model-based iterative reconstructions helps compensate for noise enhancement induced by ultra-high resolution CT with hybrid iterative reconstruction compared to conventional CT.

Ultra-high resolution CT with deep learning reconstruction is the best technique for imaging fine temporal bone anatomy in a phantom study and in the clinical setting.

The purpose of this study was to evaluate the ability of ultra-high-resolution computed tomography (UHR-CT) to assess stapes and chorda tympani nerve anatomy using a deep learning (DLR), a model-based, and a hybrid iterative reconstruction algorithm compared to simulated conventional CT.

CT acquisitions were performed with a Mercury 4.0 phantom. Images were acquired with a 1024 × 1024 matrix and a 0.25 mm slice thickness and reconstructed using DLR, model-based, and hybrid iterative reconstruction algorithms. To simulate conventional CT, images were also reconstructed with a 512 × 512 matrix and a 0.5 mm slice thickness. Spatial resolution, noise power spectrum, and objective high-contrast detectability were compared. Three radiologists evaluated the clinical acceptability of these algorithms by assessing the thickness and image quality of the stapes footplate and superstructure elements, as well as the image quality of the chorda tympani nerve bony and tympanic segments using a 5-point confidence scale on 13 temporal bone CT examinations reconstructed with the four algorithms.

UHR-CT provided higher spatial resolution than simulated conventional CT at the penalty of higher noise. DLR and model-based iterative reconstruction provided better noise reduction than hybrid iterative reconstruction, and DLR had the highest detectability index, regardless of the dose level. All stapedial structure thicknesses were thinner using UHR-CT by comparison with conventional simulated CT (P < 0.009). DLR showed the best visualization scores compared to the other reconstruction algorithms (P < 0.032).

UHR-CT with DLR results in less noise than UHR-CT with hybrid iterative reconstruction and significantly improves stapes and tympanic chorda tympani nerve depiction compared to simulated conventional CT and UHR-CT with iterative reconstruction.

Computed tomography

Deep learning

Image enhancement

Image reconstruction

Temporal bone

Advanced Intelligent Clear-IQ Engine

Adaptive iterative dose reduction 3-dimensional

Deep learning reconstruction

High resolution computed tomography

Hybrid iterative reconstruction

Intraclass correlation coefficient

Model-based iterative reconstruction

Simulated normal resolution

Tasked-based transfer function

Ultra-high resolution computed tomography

© 2024 The Author(s). Published by Elsevier Masson SAS on behalf of Société française de radiologie.