  4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive
measurement technique capable of quantifying blood flow across the
cardiovascular system. While practical use is limited by spatial resolution and
image noise, incorporation of trained super-resolution (SR) networks has
potential to enhance image quality post-scan. However, these efforts have
predominantly been restricted to narrowly defined cardiovascular domains, with
limited exploration of how SR performance extends across the cardiovascular
system; a task aggravated by contrasting hemodynamic conditions apparent across
the cardiovasculature. The aim of our study was to explore the generalizability
of SR 4D Flow MRI using a combination of heterogeneous training sets and
dedicated ensemble learning. With synthetic training data generated across
three disparate domains (cardiac, aortic, cerebrovascular), varying
convolutional base and ensemble learners were evaluated as a function of domain
and architecture, quantifying performance on both in-silico and acquired
in-vivo data from the same three domains. Results show that both bagging and
stacking ensembling enhance SR performance across domains, accurately
predicting high-resolution velocities from low-resolution input data in-silico.
Likewise, optimized networks successfully recover native resolution velocities
from downsampled in-vivo data, as well as show qualitative potential in
generating denoised SR-images from clinical level input data. In conclusion,
our work presents a viable approach for generalized SR 4D Flow MRI, with
ensemble learning extending utility across various clinical areas of interest.
