Tracking of cyclic motion with phase-contrast cine MR velocity data

Area of Science:

• Medical Imaging
• Biophysics
• Computational Science

Background:

• Phase-contrast magnetic resonance (MR) imaging is a powerful tool for assessing velocity.
• Accurate computation of object trajectories from velocity data is crucial in various scientific fields.
• Challenges include measurement noise and eddy current-induced velocity offsets.

Purpose of the Study:

• To develop and validate a method for computing object trajectories using phase-contrast MR imaging velocity data.
• To analyze the impact of measurement noise and velocity offsets on trajectory estimation.
• To improve the precision and accuracy of trajectory computation for periodic motions.

Main Methods:

• A recursive estimation method is employed, starting from a known location at a specific time point.
• For periodic motion, trajectories are computed by integrating velocity data in both forward and backward temporal directions.
• A linear combination of forward and backward trajectories is used to minimize velocity offsets and enhance precision.

Main Results:

• The method's limitations due to measurement noise were found to be acceptable under representative acquisition parameters and signal-to-noise ratios.
• In a phantom study with reciprocal rotation, the computed trajectories closely matched the true trajectories, with agreement within 3.3%.
• Sample trajectory estimates of human myocardial regions demonstrated encouraging results.

Conclusions:

• The presented method provides an effective approach for computing object trajectories from phase-contrast MR imaging velocity data.
• The technique demonstrates robustness against measurement noise and offers improved precision for periodic motions.
• The promising results in phantom and myocardial tissue suggest potential clinical and research applications for this trajectory computation method.