title: 4D Flow MRI-based pressure loss estimation in stenotic flows: Evaluation using numerical simulations.
abstract: OBJECTIVE
  To assess how 4D flow MRI-based pressure and energy loss estimates correspond to net transstenotic pressure gradients (TPGnet) and their dependence on spatial resolution.
  
  
  METHODS
  Numerical velocity data of stenotic flow were obtained from computational fluid dynamics (CFD) simulations in geometries with varying stenosis degrees, poststenotic diameters and flow rates. MRI measurements were simulated at different spatial resolutions. The simplified and extended Bernoulli equations, Pressure-Poisson equation (PPE), and integration of turbulent kinetic energy (TKE) and viscous dissipation were compared against the true TPGnet .
  
  
  RESULTS
  The simplified Bernoulli equation overestimated the true TPGnet (8.74 ± 0.67 versus 6.76 ± 0.54 mmHg). The extended Bernoulli equation performed better (6.57 ± 0.53 mmHg), although errors remained at low TPGnet . TPGnet estimations using the PPE were always close to zero. Total TKE and viscous dissipation correlated strongly with TPGnet for each geometry (r(2) > 0.93) and moderately considering all geometries (r(2) = 0.756 and r(2) = 0.776, respectively). TKE estimates were accurate and minorly impacted by resolution. Viscous dissipation was overall underestimated and resolution dependent.
  
  
  CONCLUSIONS
  Several parameters overestimate or are not linearly related to TPGnet and/or depend on spatial resolution. Considering idealized axisymmetric geometries and in absence of noise, TPGnet was best estimated using the extended Bernoulli equation. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance.
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Belen
authors.initials: B
authors.lastname: Casas
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Division of Media and Information Technology, Department of Science and Technology/Swedish e-Science Research Centre (SeRC), Linköping University, Linköping, Sweden.
authors.forename: Jonas
authors.initials: J
authors.lastname: Lantz
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.
authors.forename: Petter
authors.initials: P
authors.lastname: Dyverfeldt
authors.affiliation: Division of Cardiovascular Medicine, Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.|Center for Medical Image Science and Visualization (CMIV), Linköping University, Linköping, Sweden.|Division of Media and Information Technology, Department of Science and Technology/Swedish e-Science Research Centre (SeRC), Linköping University, Linköping, Sweden.
authors.forename: Tino
authors.initials: T
authors.lastname: Ebbers
mesh_terms: D001017:Aortic Coarctation / Q000000981:diagnostic imaging*; D001021:Aortic Valve / Q000000981:diagnostic imaging; D003198:Computer Simulation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D018810:Magnetic Resonance Angiography / Q000379:methods*; D019047:Phantoms, Imaging
keywords: aortic coarctation; aortic valve disease; phase contrast magnetic resonance imaging; pressure loss