Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.

Journal: European radiology

Published Date: Jul 27, 2022

Abstract

OBJECTIVES: To evaluate a novel deep learning image reconstruction (DLIR) technique for dual-energy CT (DECT) derived virtual monoenergetic (VM) images compared to adaptive statistical iterative reconstruction (ASIR-V) in low kiloelectron volt (keV) images.

Authors

Jack Junchi Xu

Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark. jack.junchi.xu@regionh.dk.
Lars Lönn

Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
Esben Budtz-Jørgensen

Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Kristoffer L Hansen

Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.
Peter S Ulriksen

Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, 2100, Copenhagen, Denmark.

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Liver Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (35895120)

Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.

Abstract

Authors

Keywords

External Resources

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