Referenceless 4D flow cardiovascular magnetic resonance with deep learning

Background Despite its potential to improve the assessment of cardiovascular diseases, 4D Flow CMR is hampered by long scan times. 4D Flow CMR is conventionally acquired with three motion encodings and one reference encoding, as the 3-dimensional velocity data are obtained by subtracting the phase of the reference from the phase of the motion encodings. In this study, we aim to use deep learning to predict the reference encoding from the three motion encodings for cardiovascular 4D Flow. Methods A U-Net was trained with adversarial learning (U-Net ADV ) and with a velocity frequency-weighted loss function (U-Net VEL ) to predict the reference encoding from the three motion encodings obtained with a non-symmetric velocity-encoding scheme. Whole-heart 4D Flow datasets from 126 patients with different types of cardiomyopathies were retrospectively included. The models were trained on 113 patients with a 5-fold cross-validation, and tested on 13 patients. Flow volumes in the aorta and pulmonary artery, mean and maximum velocity, total and maximum turbulent kinetic energy at peak systole in the cardiac chambers and main vessels were assessed. Results 3-dimensional velocity data reconstructed with the reference encoding predicted by deep learning agreed well with the velocities obtained with the reference encoding acquired at the scanner for both models. U-Net ADV performed more consistently throughout the cardiac cycle and across the test subjects, while U-Net VEL performed better for systolic velocities. Comprehensively, the largest error for flow volumes, maximum and mean velocities was -6.031% for maximum velocities in the right ventricle for the U-Net ADV , and -6.92% for mean velocities in the right ventricle for U-Net VEL . For total turbulent kinetic energy, the highest errors were in the left ventricle (-77.17%) for the U-Net ADV , and in the right ventricle (24.96%) for the U-Net VEL , while for maximum turbulent kinetic energy were in the pulmonary artery for both models, with a value of -15.5% for U-Net ADV and 15.38% for the U-Net VEL . Conclusion Deep learning-enabled referenceless 4D Flow CMR permits velocities and flow volumes quantification comparable to conventional 4D Flow. Omitting the reference encoding reduces the amount of acquired data by 25%, thus allowing shorter scan times or improved resolution, which is valuable for utilization in the clinical routine.

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