Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study - 28/09/24
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Highlights |
• | Ultra-high-resolution CT of the temporal bone with deep learning reconstruction can be performed with up to a tenfold reduction in radiation dose by comparison with conventional high-resolution CT while maintaining image quality. |
• | The use of deep learning with ultra-high-resolution CT at the same radiation dose as conventional high-resolution CT allows a marked increase in image quality of the middle and inner ear. |
• | The use of deep learning with ultra-high-resolution CT helps achieve more complete bony coverage of the facial nerve and better representation of the cochlear spiral osseous lamina compared to hybrid iterative reconstruction algorithms. |
Abstract |
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.
El texto completo de este artículo está disponible en PDF.Keywords : Computed tomography, Deep learning, Image enhancement, Image reconstruction, Temporal bone
Abbreviations : AiCE, CT, CTDI, DLR, HR-CT, HIR, HU, SD, UHR-CT
Esquema
Vol 105 - N° 10
P. 371-378 - octobre 2024 Regresar al número
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