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

Fatma Boubaker; Ulysse Puel; Michael Eliezer; Gabriela Hossu; Bouchra Assabah; Karim Haioun; Alain Blum; Pedro Augusto Gondim-Teixeira; Cécile Parietti-Winkler; Romain Gillet

doi:10.1016/j.diii.2024.05.001

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

Diagn Interv Imaging. 2024 May 13:S2211-5684(24)00119-0. doi: 10.1016/j.diii.2024.05.001. Online ahead of print.

Authors

Affiliations

¹ Guilloz Imaging Department, Central Hospital, University Hospital Center of Nancy, 54000, Nancy, France.
² 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.
³ Department of Radiology, Hôpital des 15-20, 75571 Paris, France.
⁴ Université de Lorraine, INSERM, IADI, 54000, Nancy, France; Université de Lorraine, CIC, Innovation Technologique, University Hospital Center of Nancy, 54000, Nancy, France.
⁵ Department of Anatomy, University Hospital Center of Nancy, 54000, Nancy, France.
⁶ Canon Medical Systems Corporation, Kawasaki-shi, 212-0015 Kanagawa, Japan.
⁷ ENT Surgery Department, Central Hospital, University Hospital Center of Nancy, 54000 Nancy, France.
⁸ 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.

PMID: 38744577
DOI: 10.1016/j.diii.2024.05.001

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.

Keywords: Computed tomography; Deep learning; Image enhancement; Image reconstruction; Temporal bone.