Contrast Agent Dose Reduction in MRI Utilizing a Generative Adversarial Network in an Exploratory Animal Study.


Journal

Investigative radiology
ISSN: 1536-0210
Titre abrégé: Invest Radiol
Pays: United States
ID NLM: 0045377

Informations de publication

Date de publication:
01 06 2023
Historique:
medline: 8 5 2023
pubmed: 3 2 2023
entrez: 2 2 2023
Statut: ppublish

Résumé

The aim of this study is to use virtual contrast enhancement to reduce the amount of hepatobiliary gadolinium-based contrast agent in magnetic resonance imaging with generative adversarial networks (GANs) in a large animal model. With 20 healthy Göttingen minipigs, a total of 120 magnetic resonance imaging examinations were performed on 6 different occasions, 50% with reduced (low-dose; 0.005 mmol/kg, gadoxetate) and 50% standard dose (normal-dose; 0.025 mmol/kg). These included arterial, portal venous, venous, and hepatobiliary contrast phases (20 minutes, 30 minutes). Because of incomplete examinations, one animal had to be excluded. Randomly, 3 of 19 animals were selected and withheld for validation (18 examinations). Subsequently, a GAN was trained for image-to-image conversion from low-dose to normal-dose (virtual normal-dose) with the remaining 16 animals (96 examinations). For validation, vascular and parenchymal contrast-to-noise ratio (CNR) was calculated using region of interest measurements of the abdominal aorta, inferior vena cava, portal vein, hepatic parenchyma, and autochthonous back muscles. In parallel, a visual Turing test was performed by presenting the normal-dose and virtual normal-dose data to 3 consultant radiologists, blinded for the type of examination. They had to decide whether they would consider both data sets as consistent in findings and which images were from the normal-dose study. The pooled dynamic phase vascular and parenchymal CNR increased significantly from low-dose to virtual normal-dose (pooled vascular: P < 0.0001, pooled parenchymal: P = 0.0002) and was found to be not significantly different between virtual normal-dose and normal-dose examinations (vascular CNR [mean ± SD]: low-dose 17.6 ± 6.0, virtual normal-dose 41.8 ± 9.7, and normal-dose 48.4 ± 12.2; parenchymal CNR [mean ± SD]: low-dose 20.2 ± 5.9, virtual normal-dose 28.3 ± 6.9, and normal-dose 29.5 ± 7.2). The pooled parenchymal CNR of the hepatobiliary contrast phases revealed a significant increase from the low-dose (22.8 ± 6.2) to the virtual normal-dose (33.2 ± 6.1; P < 0.0001) and normal-dose sequence (37.0 ± 9.1; P < 0.0001). In addition, there was no significant difference between the virtual normal-dose and normal-dose sequence. In the visual Turing test, on the median, the consultant radiologist reported that the sequences of the normal-dose and virtual normal-dose are consistent in findings in 100% of the examinations. Moreover, the consultants were able to identify the normal-dose series as such in a median 54.5% of the cases. In this feasibility study in healthy Göttingen minipigs, it could be shown that GAN-based virtual contrast enhancement can be used to recreate the image impression of normal-dose imaging in terms of CNR and subjective image similarity in both dynamic and hepatobiliary contrast phases from low-dose data with an 80% reduction in gadolinium-based contrast agent dose. Before clinical implementation, further studies with pathologies are needed to validate whether pathologies are correctly represented by the network.

Identifiants

pubmed: 36728299
doi: 10.1097/RLI.0000000000000947
pii: 00004424-202306000-00004
doi:

Substances chimiques

Contrast Media 0
Gadolinium AU0V1LM3JT

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

396-404

Informations de copyright

Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of interest and sources of funding: The study was conducted in collaboration with Bayer AG. H.P. and G.J. are employees of Bayer AG. The Clinician Scientist Program of the Clinician Scientist Academy of the University Hospital Essen provided J.H. with financial support, funded by the German Research Foundation (FU 356/12-1). The German Research Foundation had no influence on the study design, data collection, data interpretation, data analysis, or report writing. All study results were available to the corresponding author, who also had final responsibility for the decision to publish the study. The authors state that they have no other competing interests.

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Auteurs

Gregor Jost (G)

MR and CT Contrast Media Research, Bayer AG, Berlin, Germany.

Jens Matthias Theysohn (JM)

From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen.

Johannes Maximilian Ludwig (JM)

From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen.

Yan Li (Y)

From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen.

Jens Kleesiek (J)

Institute of Artificial Intelligence in Medicine, University Hospital Essen, Essen.

Benedikt Michael Schaarschmidt (BM)

From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen.

Michael Forsting (M)

From the Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen.

Hubertus Pietsch (H)

MR and CT Contrast Media Research, Bayer AG, Berlin, Germany.

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Classifications MeSH