Montalt-Tordera, J;
Quail, M;
Steeden, JA;
Muthurangu, V;
(2021)
Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning.
Journal of Magnetic Resonance Imaging
10.1002/jmri.27573.
(In press).
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Abstract
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 (PLD-ELD , PLD-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.
| Type: | Article |
|---|---|
| Title: | Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1002/jmri.27573 |
| Publisher version: | https://doi.org/10.1002/jmri.27573 |
| Language: | English |
| Additional information: | Copyright © 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
| Keywords: | MR angiography, contrast agent, deep learning, low-dose MRA |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Childrens Cardiovascular Disease |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10123483 |
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