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

被引:7
作者
Haubold, Johannes [1 ]
Jost, Gregor [3 ]
Theysohn, Jens Matthias [1 ]
Ludwig, Johannes Maximilian [1 ]
Li, Yan [1 ]
Kleesiek, Jens [2 ]
Schaarschmidt, Benedikt Michael [1 ]
Forsting, Michael [1 ]
Nensa, Felix [1 ,4 ]
Pietsch, Hubertus [3 ]
Hosch, Rene [1 ]
机构
[1] Univ Hosp Essen, Dept Diagnost & Intervent Radiol & Neuroradiol, Essen, Germany
[2] Univ Hosp Essen, Inst Artificial Intelligence Med, Essen, Germany
[3] Bayer AG, MR & CT Contrast Media Res, Berlin, Germany
[4] Univ Hosp Essen, Dept Diagnost & Intervent Radiol & Neuroradiol, Hufelandstr 55, D-45147 Essen, Germany
关键词
animals; contrast media; deep learning; swine; magnetic resonance imaging; MAGNETIC-RESONANCE ANGIOGRAPHY; IMAGE QUALITY; GADOLINIUM; LIVER; ENHANCEMENT; CT;
D O I
10.1097/RLI.0000000000000947
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
ObjectivesThe 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.MethodsWith 20 healthy Gottingen 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.ResultsThe 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.ConclusionsIn this feasibility study in healthy Gottingen 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.
引用
收藏
页码:396 / 404
页数:9
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