title: Quantifying errors in flow measurement using phase contrast magnetic resonance imaging: comparison of several boundary detection methods.
abstract: Quantifying flow from phase-contrast MRI (PC-MRI) data requires that the vessels of interest be segmented. The estimate of the vessel area will dictate the type and magnitude of the error sources that affect the flow measurement. These sources of errors are well understood, and mathematical expressions have been derived for them in previous work. However, these expressions contain many parameters that render them difficult to use for making practical error estimates. In this work, some realistic assumptions were made that allow for the simplification of such expressions in order to make them more useful. These simplified expressions were then used to numerically simulate the effect of segmentation accuracy and provide some criteria that if met, would keep errors in flow quantification below 10% or 5%. Four different segmentation methods were used on simulated and phantom MRA data to verify the theoretical results. Numerical simulations showed that including partial volumed edge pixels in vessel segmentation provides less error than missing them. This was verified with MRA simulations, as the best performing segmentation method generally included such pixels. Further, it was found that to obtain a flow error of less than 10% (5%), the vessel should be at least 4 (5) pixels in diameter, have an SNR of at least 10:1 and have a peak velocity to saturation cut-off velocity ratio of at least 5:3.
authors.affiliation: Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA.
authors.forename: Jing
authors.initials: J
authors.lastname: Jiang
authors.affiliation: Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Magnetic Resonance Innovations, Inc., Detroit, MI, USA.
authors.forename: Paul
authors.initials: P
authors.lastname: Kokeny
authors.affiliation: Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning province, China.
authors.forename: Wang
authors.initials: W
authors.lastname: Ying
authors.affiliation: Buffalo Neuroimaging Analysis Center, State University of New York, Buffalo, NY, USA.
authors.forename: Chris
authors.initials: C
authors.lastname: Magnano
authors.affiliation: Buffalo Neuroimaging Analysis Center, State University of New York, Buffalo, NY, USA.
authors.forename: Robert
authors.initials: R
authors.lastname: Zivadinov
authors.affiliation: Department of Biomedical Engineering, Wayne State University, Detroit, MI, USA; Department of Radiology, Wayne State University, Detroit, MI, USA; Magnetic Resonance Innovations, Inc., Detroit, MI, USA; Department of Electrical and Computer Engineering, McMaster University, Hamilton, Ontario, Canada. Electronic address: nmrimaging@aol.com.
authors.forename: E
authors.initials: E
authors.lastname: Mark Haacke
mesh_terms: D000465:Algorithms; D001783:Blood Flow Velocity; D001808:Blood Vessels / Q000473:pathology; D003196:Computer Graphics; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008858:Microscopy, Phase-Contrast / Q000379:methods*; D008962:Models, Theoretical; D019047:Phantoms, Imaging; D059629:Signal-To-Noise Ratio
keywords: Flow error; Flow quantification; Magnetic resonance imaging; Vessel segmentation