Mathematical and computational modeling of the cardiovascular system is increasingly
providing non‐invasive alternatives to traditional invasive clinical procedures. Moreover, it …

Uni-dimensional Doppler echocardiography data provide the mainstay of quantative
assessment of aortic stenosis, with the transvalvular pressure drop a key indicator of …

Purpose Hemodynamic alterations are indicative of cerebrovascular disease. However, the
narrow and tortuous cerebrovasculature complicates image‐based assessment, especially …

We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with
ground truth flow data to support training of inference methods. Turbulent blood flow is …

Intracardiac blood flow is driven by differences in relative pressure, and assessing these is
critical in understanding cardiac disease. Non-invasive image-based methods exist to …

Purpose To estimate relative transvalvular pressure gradient (TVPG) noninvasively from 4D
flow MRI. Methods A novel deep learning–based approach is proposed to estimate pressure …

Abstract For 4D Flow MRI of mean and turbulent flow a compromise between spatiotemporal
undersampling and velocity encodings needs to be found. Assuming a fixed scan time …

Patient‐specific image‐based computational fluid dynamics (CFD) is widely adopted in the
cardiovascular research community to study hemodynamics, and will become increasingly …

Objective Arterial stenosis is a significant cardiovascular disease requiring accurate
estimation of the pressure gradients for determining hemodynamic significance. In this …

While the clinical gold standard for pressure difference measurements is invasive
catheterization, 4D Flow MRI is a promising tool for enabling a non-invasive quantification …