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

对比度(视觉) 还原(数学) 对抗制 生成语法 计算机科学 生成对抗网络 人工智能 数学 深度学习 几何学
作者
Johannes Haubold,Gregor Jošt,Jens Theysohn,Johannes Ludwig,Yan Li,Jens Kleesiek,Benedikt M. Schaarschmidt,Michael Forsting,Felix Nensa,Hubertus Pietsch,René Hosch
出处
期刊:Investigative Radiology [Lippincott Williams & Wilkins]
卷期号:58 (6): 396-404 被引量:5
标识
DOI:10.1097/rli.0000000000000947
摘要

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.

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