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. This 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 a peak velocity to saturation cut-off velocity ratio of at least 5:3.

Accuracy of blood flow assessment in cerebral arteries with 4D flow MRI: Evaluation with three segmentation methods

“…With an oversized ROI the results are affected by partial volume effects, Gibbs ringing, and eddy currents . The error in flow quantification should, however, be smaller and less vessel area‐dependent with an oversized ROI compared with an undersized one . Therefore, although subjective variability is always present in manual segmentations, we believe that our approach minimized the impact of unwanted subjectivity on the result.…”

“… A region of interest (ROI) covering the vessel was manually outlined in the image, considering both the magnitude and the phase image. The ROI size was kept constant through all timeframes, with a strategy to segment with a slightly oversized ROI that included all pixels that had a flow signal during any of the timeframes. For each artery, flow rates from the 2D PCMRI were determined as the mean flow over the 32 timeframes.…”

Jugular Anomalies in Multiple Sclerosis Are Associated with Increased Collateral Venous Flow

“…Flow rates (mL/s) were calculated based on integrated flow velocities within the vessel lumen. Velocities which exceeded the 50 cm/s were unwrapped by SPIN software using a robust automatic unwrapping algorithm that compares pixel-wise phase values in x, y, and z directions and ensures that only pixels that are aliased are unwrapped 15 .…”

Validation of a Hemodynamic Model for the Study of the Cerebral Venous Outflow System Using MR Imaging and Echo-Color Doppler Data

“…A maximum velocity encoding of 50 cm/s was used, and phase unwrapping was performed when the flow velocity exceeded this value. Vessel boundaries were delineated automatically by using a full width at half maximum region-growing threshold method 23,24 with manual modification applied when appropriate. Signal Processing In NMR software (SPIN; MR Imaging Institute for Biomedical Research, Detroit, Michigan) 25 was used to quantify blood flows and to evaluate the presence and dimensions of IJV stenosis.…”