zahr2020siampp.bib

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@conference{zahr2020siampp,
  abstract = {Detailed in vivo imaging of the human body using magnetic resonance imaging (MRI) holds great potential for scientific discovery and impact in health care. However, current MRI scanners and flow reconstruction techniques are limited by a fundamental trade-off between resolution, image quality, and scan time. We propose a new method of flow reconstruction that aims to overcome this limitation by integrating computational fluid dynamics (CFD), numerical optimization, and uncertainty quantification into the MRI workflow. Our approach defines an optimization problem that aims to define the boundary conditions and material properties of a high-order CFD simulation that best describes data from low-resolution (fast) MRI scans. The resulting data-certified simulation is subsequently used for flow visualization and to accurately compute clinical biomarkers. To quantify the uncertainty in the reconstruction due to noise in the measurements, we adopt a Bayesian setting and estimate the posterior distribution using implicit sampling. The optimization and sampling procedures are accelerated using adaptive projection-based reduced-order models. We demonstrate the method reconstructs both external and in vivo flows more accurately than standard 4D flow MRI techniques.},
  address = {Seattle, Washington},
  author = {Zahr, Matthew J.},
  booktitle = {SIAM Conference on Parallel Processing for Scientific Computing (PP20)},
  conftype = {conference},
  date-added = {2019-12-08 00:50:49 -0500},
  date-modified = {2019-12-08 00:53:10 -0500},
  presenter = {Zahr, Matthew J.},
  project = {dgopt},
  status = {invited},
  title = {High-resolution visualization of in vivo blood flow from low-resolution 4{D} flow {MRI} scans using computational fluid dynamics and optimization},
  year = {2/12/2020 -- 2/15/2020}
}

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