Probabilistic 4D blood flow tracking and uncertainty estimation

Phase-Contrast (PC) MRI utilizes signal phase shifts resulting from moving spins to measure tissue motion and blood flow. Time-resolved 4D vector fields representing the motion or flow can be derived from the acquired PC MRI images. In cardiovascular PC MRI applications, visualization techniques such as vector glyphs, streamlines, and particle traces are commonly employed for depicting the blood flow. Whereas these techniques indeed provide useful diagnostic information, uncertainty due to noise in the PC-MRI measurements is ignored, which may lend the results a false sense of precision. In this work, the statistical properties of PC MRI flow measurements are investigated and a probabilistic flow tracking method based on sequential Monte Carlo sampling is devised to calculate flow uncertainty maps. The theoretical derivations are validated using simulated data and a number of real PC MRI data sets of the aorta and carotid arteries are used to demonstrate the flow uncertainty mapping technique.

This work investigates uncertainty in 4D MRI blood flow measurements. The noise distribution of the velocity measurements is derived on the local voxel level, and propagated to the global image level using a sequential monte carlo sampling method. The result is a distribution of possible blood flow trajectories that visualizes the uncertainty associated with the measurement.Figure optionsDownload high-quality image (124 K)Download as PowerPoint slideHighlights
► The uncertainty in Phase-Contrast MRI blood flow measurements is investigated.
► We derive the probability distribution of flow vectors due to image noise.
► Probabilistic flow paths based on Monte Carlo sampling are introduced.
► Flow connectivity maps displaying the flow distribution are generated.