Radiation dose reduction and image quality improvement with ultra-high resolution temporal bone CT using deep learning-based reconstruction: An anatomical study.

Autor: Boubaker F; Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France., Puel U; Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France; Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France., Eliezer M; Department of Radiology, Hôpital des 15-20, 75571 Paris, France., Hossu G; Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France., Assabah B; Department of Anatomy, University Hospital Center of Nancy, 54000, Nancy, France., Haioun K; Canon Medical Systems Corporation, Kawasaki-shi, 212-0015 Kanagawa, Japan., Blum A; Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France; Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France., Gondim-Teixeira PA; Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France; Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France., Parietti-Winkler C; ENT Surgery Department, Central Hospital, University Hospital Center of Nancy, 54000 Nancy, France., Gillet R; Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France; Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France. Electronic address: r.gillet@chru-nancy.fr.
Jazyk: angličtina
Zdroj: Diagnostic and interventional imaging [Diagn Interv Imaging] 2024 Oct; Vol. 105 (10), pp. 371-378. Date of Electronic Publication: 2024 May 13.
DOI: 10.1016/j.diii.2024.05.001
Abstrakt: 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), 1024 2 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, 512 2 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.
Competing Interests: Declaration of competing interest Two authors in this work, A.B. and P.A.G.T., are involved in a non-remunerated research contract with Canon Medical Systems. K. H. works as a CT clinical research scientist for Canon Medical Systems Corporation.
(Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)
Databáze: MEDLINE
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