RESUMO
PURPOSE: The purpose of this study was to evaluate the achievable radiation dose reduction of an ultra-high resolution computed tomography (UHR-CT) scanner using deep learning reconstruction (DLR) while maintaining temporal bone image quality equal to or better than high-resolution CT (HR-CT). MATERIALS AND METHODS: UHR-CT acquisitions were performed with variable tube voltages and currents at eight different dose levels (volumic CT dose index [CTDIvol] range: 4.6-79 mGy), 10242 matrix, and 0.25 mm slice thickness and reconstructed using DLR and hybrid iterative reconstruction (HIR) algorithms. HR-CT images were acquired using a standard protocol (120 kV/220 mAs; CTDI vol, 54.2 mGy, 5122 matrix, and 0.5 mm slice thickness). Two radiologists rated the image quality of seven structures using a five point confidence scale on six cadaveric temporal bone CTs. A global image quality score was obtained for each CT protocol by summing the image quality scores of all structures. RESULTS: With DLR, UHR-CT at 120 kV/220 mAs (CTDIvol, 50.9 mGy) and 140 kV/220 mAs (CTDIvol, 79 mGy) received the highest global image quality scores (4.88 ± 0.32 [standard deviation (SD)] [range: 4-5] and 4.85 ± 0.35 [range: 4-5], respectively; P = 0.31), while HR-CT at 120 kV/220 mAs and UHR-CT at 120 kV/20 mAs received the lowest (i.e., 3.14 ± 0.75 [SD] [range: 2-5] and 2.97 ± 0.86 [SD] [range: 1-5], respectively; P = 0.14). All the DLR protocols had better image quality scores than HR-CT with HIR. CONCLUSION: UHR-CT with DLR can be performed with up to a tenfold reduction in radiation dose compared to HR-CT with HIR while maintaining or improving image quality.
RESUMO
PURPOSE: 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. MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.