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Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning
Author(s) -
MontaltTordera Javier,
Quail Michael,
Steeden Jennifer A,
Muthurangu Vivek
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27573
Subject(s) - medicine , mcnemar's test , magnetic resonance angiography , wilcoxon signed rank test , nuclear medicine , radiology , contrast (vision) , prospective cohort study , image quality , retrospective cohort study , angiography , confidence interval , magnetic resonance imaging , mathematics , artificial intelligence , mann–whitney u test , computer science , statistics , image (mathematics)
Background Contrast‐enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium‐based contrast agents (GBCAs) carry a risk of dose‐related adverse effects. Purpose To develop a deep learning method to reduce GBCA dose by 80%. Study Type Retrospective and prospective. Population A total of 1157 retrospective and 40 prospective congenital heart disease patients for training/validation and testing, respectively. Field Strength/Sequence A 1.5 T, T1 ‐weighted three‐dimensional ( 3D) gradient echo. Assessment A neural network was trained to enhance low‐dose (LD) 3D MRA using retrospective synthetic data and tested with prospective LD data. Image quality for LD (LD‐MRA), enhanced LD (ELD‐MRA), and high‐dose (HD‐MRA) was assessed in terms of signal‐to‐noise ratio (SNR), contrast‐to‐noise ratio (CNR), and a quantitative measure of edge sharpness and scored for perceptual sharpness and contrast on a 1–5 scale. Diagnostic confidence was assessed on a 1–3 scale. LD‐ and ELD‐MRA were assessed against HD‐MRA for sensitivity/specificity and agreement of vessel diameter measurements (aorta and pulmonary arteries). Statistical Tests SNR, CNR, edge sharpness, and vessel diameters were compared between LD‐, ELD‐, and HD‐MRA using one‐way repeated measures analysis of variance with post‐hoc t ‐tests. Perceptual quality and diagnostic confidence were compared using Friedman's test with post‐hoc Wilcoxon signed‐rank tests. Sensitivity/specificity was compared using McNemar's test. Agreement of vessel diameters was assessed using Bland–Altman analysis. Results SNR, CNR, edge sharpness, perceptual sharpness, and perceptual contrast were lower ( P < 0.05) for LD‐MRA compared to ELD‐MRA and HD‐MRA. SNR, CNR, edge sharpness, and perceptual contrast were comparable between ELD and HD‐MRA, but perceptual sharpness was significantly lower. Sensitivity/specificity was 0.824/0.921 for LD‐MRA and 0.882/0.960 for ELD‐MRA. Diagnostic confidence was 2.72, 2.85, and 2.92 for LD, ELD, and HD‐MRA, respectively ( P LD‐ELD , P LD‐HD < 0.05). Vessel diameter measurements were comparable, with biases of 0.238 (LD‐MRA) and 0.278 mm (ELD‐MRA). Data Conclusion Deep learning can improve contrast in LD cardiovascular MRA. Level of Evidence Level 2 Technical Efficacy Stage 2