Comparison of two deep learning image reconstruction algorithms in chest CT images: A task-based image quality assessment on phantom data.

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Phantoms, Imaging Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Tomography, X-Ray Computed
Deep learning image reconstruction Multidetector computed tomography Task-based image quality assessment

Journal

Diagnostic and interventional imaging
ISSN: 2211-5684
Titre abrégé: Diagn Interv Imaging
Pays: France
ID NLM: 101568499

Informations de publication

Date de publication:
Jan 2022
Historique:
received: 05 07 2021
revised: 02 08 2021
accepted: 04 08 2021
pubmed: 9 9 2021
medline: 11 1 2022
entrez: 8 9 2021
Statut: ppublish

Résumé

The purpose of this study was to compare the effect of two deep learning image reconstruction (DLR) algorithms in chest computed tomography (CT) with different clinical indications. Acquisitions on image quality and anthropomorphic phantoms were performed at six dose levels (CTDI For the L-DLR/M-DLR levels, the noise magnitude was lower with TrueFidelity DLR algorithms reduce the image-noise and improve lesion detectability. Their operations and properties impacted both noise-texture and spatial resolution.

Identifiants

DOI: 10.1016/j.diii.2021.08.001 PMID: 34493475
pubmed: 34493475
pii: S2211-5684(21)00174-1
doi: 10.1016/j.diii.2021.08.001
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

21-30

Informations de copyright

Copyright © 2021 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.

Auteurs

Joël Greffier (J)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France. Electronic address: joel.greffier@chu-nimes.fr.

Julien Frandon (J)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Salim Si-Mohamed (S)

Department of Radiology, Hospices Civils de Lyon, 69500 Lyon, France.

Djamel Dabli (D)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Aymeric Hamard (A)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Asmaa Belaouni (A)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Philippe Akessoul (P)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

Francis Besse (F)

Department of Radiology Centre Cardiologique Nord, 93200 Saint Denis, France.

Boris Guiu (B)

Department of Radiology Saint-Eloi University Hospital, 34295 Montpellier, France.

Jean-Paul Beregi (JP)

Department of Medical Imaging, CHU Nimes, Univ Montpellier, Medical Imaging Group Nimes, EA 2992, 30029 Nîmes, France.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Comparison of two deep learning image...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Réseaux neuronaux (ordinateur) › Apprentissage profond › Comparison of two deep learning image...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Comparison of two deep learning image...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › Comparison of two deep learning image...
questionsmedicales.fr › Équipement et fournitures › Fantômes en imagerie › Comparison of two deep learning image...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Hygiène radiologique › Dose de rayonnement › Comparison of two deep learning image...
questionsmedicales.fr › Sciences de l'information › Informatique › Informatique médicale › Applications de l'informatique médicale › Prise de décision assistée par ordinateur › Diagnostic assisté par ordinateur › Interprétation d'images assistée par ordinateur › Interprétation d'images radiographiques assistée par ordinateur › Comparison of two deep learning image...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Tomographie à rayons X › Tomodensitométrie › Comparison of two deep learning image...