Keywords: Flow, Data Processing, Image reconstruction
Motivation: Scan times for clinical 4D Flow MRI are still around 5-10 mins, which is too long for ideal integration in clinical practice.
Goal(s): To investigate the possibility to reduce scan time by reconstructing three-directional velocity without the acquisition of reference scan in cardiac applications.
Approach: Training a conditional generative adversarial network (cGAN) to estimate the reference scan from the three-velocity encoded scan segments.
Results: Correlation coefficients for the pulmonary and systemic flow volumes and for maximum velocities were higher than 0.99, showing an excellent agreement between the cGAN-enabled referenceless 4D Flow MRI and conventional 4D flow MRI, thus potentially reducing scan time.
Impact: By reconstructing the three-directional velocity without the reference scan it is possible reduce scan time of 4D Flow MRI, alleviating costs and patient discomfort, which especially is important for elderly and impaired subjects.
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