Flow Field Abstraction and Vortex Detection for MR Velocity Mapping

Abstract

Abstract. The evolution of blood flow patterns (vortical flow features in particular) and their interactions with cardiovascular structure is important in the study of cardiovascular function. The winding index method is widely used for vortex detection and has shown to be very sensitive to noise. We propose a novel technique for the detection of vortical flow features that consists of three main components: flow restoration, abstraction and vortex detection. Firstly, an abstract flow field is obtained by clustering the flow field restored using our proposed restoration method. Afterwards, critical points are computed from the abstract flow field and then phase portrait theory is employed to identify vortices. The proposed technique is applied to 12 sets of MR velocity maps acquired from patients with sequential MR examination following myocardial infarction and the results show that vortical flow patterns are more readily detected compared to the winding index method.

Contractile Analysis with Kriging Based on MR Myocardial Velocity Imaging

“…The images were restored using a rigid body motion correction and a Total Variational (TV) restoration technique [8,9]. In the sequence, a specially designed black-blood RF pulse is applied every other time frame followed by the imaging pulse and a k-space viewsharing scheme is incorporated to reduce the total scan time needed, hence allowing for one reference image and three orthogonally encoded velocity images to be acquired.…”

Bayesian Motion Recovery Framework for Myocardial Phase-Contrast Velocity MRI

“…Subsequently, this approach to TV restoration was applied to MR velocity data [5] and extended through the use of the first order Lagrangian technique to derive an optimal solution to the constrained minimization problem. A significant observation of this work was that the restoration algorithm was not greatly affected by the estimated noise variance, thus significantly enhancing the practical usability of the technique as reliable noise variance estimation is difficult in practice.…”