David Marlevi

I am a research lead in quantitative cardiovascular imaging, developing
data-driven image analysis tools to tackle urgent clinical challenges across
the heart, aorta, and brain.
I am a biomedical engineer with a focus on translational cardiovascular
imaging. Specifically, I am intrigued by the translation of image-based
engineering into clinical practice, using data-driven utilities to enhance
diagnosis, improve prognosis, and provide fundamental mecanistic
understanding of cardiovascular disease.
After graduating from the joint doctoral program in Medical Technology from
the Royal Institute of Technology (KTH) and KI with a thesis entitled
"amp

Non-invasive estimation of cardiovascular pressure gradients:
Regional quantification of cardiovascular pressure gradients is critical for
diagnosis, treatment planning, and risk prediction of many cardiovascular
disease. Still, for a large number of conditions, non-invasive assessment is
obstructed by inherent method limitations, and a wide range of clinical
instances exist where regional pressure behaviour remains unexplored. To
tackle this, we have recently deployed a combination
of physics-informed image analysis (invoking fundamental fluid mechanical
description of blood flow) and full-field flow imaging (4D Flow MRI) to allow
for arbitrary probing of pressure gradients across previously inaccessible
compartments. Here, we seek to extend these utilities to further understand
early hemodynamic changes indicative of later physiological impairement,
including validation, implementation, and clincial utility across spatial
(large / small vessels), temporal (fast / slow flows) and flow (laminar /
turbulent) scales.