Physics-enhanced velocimetry (PEV) for joint reconstruction and segmentation of noisy Flow-MRI images

Add to your list(s) Download to your calendar using vCal

• Matthew Juniper, Professor of Department of Engineering in University of Cambridge
• Monday 23 January 2023, 11:00-12:00
• JDB - Seminar Room in the Dyson building, Engineering department, Trumpington Street, Cambridge, CB2 1PZ.

If you have a question about this talk, please contact Dr Song.

We formulate and solve a generalized inverse Navier–Stokes boundary value problem for velocity field reconstruction and simultaneous boundary segmentation of noisy flow velocity images. We use a Bayesian framework that combines CFD , Gaussian processes, adjoint methods, and shape optimization in a unified and rigorous manner. With this framework, we find the velocity field and flow boundaries (i.e. the digital twin) that are most likely to have produced a given noisy image. We also calculate the posterior covariances of the unknown parameters and thereby deduce the uncertainty in the reconstructed flow. First, we verify this method on synthetic noisy images of 2-D flows. Then we apply it to experimental phase contrast magnetic resonance (PC-MRI) images of an axisymmetric flow at low (≃6) and high (>30) SNRs. We show that this method successfully reconstructs and segments the low SNR images, producing noiseless velocity fields that match the high SNR images, despite using 27 times less data. This framework also provides additional flow information, such as the pressure field and wall shear stress, accurately and with known error bounds. We demonstrate this on a synthetic 2-D representation of the flow through an aortic aneurysm to show its relevance to medical imaging.

This talk is part of the ps898's list series.

This talk is included in these lists:

• JDB - Seminar Room in the Dyson building, Engineering department, Trumpington Street, Cambridge, CB2 1PZ
• Trustworthy and Responsible Machine Learning / AI

Note that ex-directory lists are not shown.