Volumetric motion quantification by 3D tissue phase mapped CMR.

Background: The objective of this study was the quantification of myocardial motion from 3D tissue phase mapped (TPM) CMR. Recent work on myocardial motion quantification by TPM has been focussed on multi-slice 2D acquisitions thus excluding motion information from large regions of the left ventricle. Volumetric motion assessment appears an important next step towards the understanding of the volumetric myocardial motion and hence may further improve diagnosis and treatments in patients with myocardial motion abnormalities.

Combination of tagging and tissue phase mapping to accelerate myocardial motion measurements in three directions.

Object: Until now, a three-directional velocity field has mostly been obtained by velocity encoding in three directions, which is very time-consuming and hence not usually used in clinical routine. We show the feasibility of combining in-plane tagging with through-plane tissue phase mapping (TPM) to encode a three-directional velocity field at 3 T with reduced overall acquisition time.

Materials And Methods: Assessment of a three-directional velocity field was performed for 10 healthy volunteers.

An integrated platform for image-guided cardiac resynchronization therapy.

Cardiac resynchronization therapy (CRT) is an effective procedure for patients with heart failure but 30% of patients do not respond. This may be due to sub-optimal placement of the left ventricular (LV) lead. It is hypothesized that the use of cardiac anatomy, myocardial scar distribution and dyssynchrony information, derived from cardiac magnetic resonance imaging (MRI), may improve outcome by guiding the physician for optimal LV lead positioning.

Synthetic echocardiographic image sequences for cardiac inverse electro-kinematic learning.

In this paper, we propose to create a rich database of synthetic time series of 3D echocardiography (US) images using simulations of a cardiac electromechanical model, in order to study the relationship between electrical disorders and kinematic patterns visible in medical images. From a real 4D sequence, a software pipeline is applied to create several synthetic sequences by combining various steps including motion tracking and segmentation. We use here this synthetic database to train a machine learning algorithm which estimates the depolarization times of each cardiac segment from invariant kinematic descriptors such as local displacements or strains.

Acceleration of tissue phase mapping with sensitivity encoding at 3T.

Background: The objective of this study was to investigate the impact of sensitivity encoding on the quantitative assessment of cardiac motion in black blood cine tissue phase mapping (TPM) sequences. Up to now whole volume coverage of the heart is still limited by the long acquisition times. Therefore, a significant increase in imaging speed without deterioration of quantitative motion information is indispensable.

SAR reduced black-blood cine TPM for increased temporal resolution at 3T.

Object: The objective was to improve the temporal resolution in black-blood CINE tissue phase mapping sequences at high field MR systems. The temporal resolution is limited due to SAR constraints causing idle times into the sequence. The aim was to avoid these idle times and therefore providing an increased number of heart phases.

Acceleration of tissue phase mapping by k-t BLAST: a detailed analysis of the influence of k-t-BLAST for the quantification of myocardial motion at 3T.

Background: The assessment of myocardial motion with tissue phase mapping (TPM) provides high spatiotemporal resolution and quantitative motion information in three directions. Today, whole volume coverage of the heart by TPM encoding at high spatial and temporal resolution is limited by long data acquisition times. Therefore, a significant increase in imaging speed without deterioration of the quantitative motion information is required.

Active-contour-based image segmentation using machine learning techniques.

We introduce a non-linear shape prior for the deformable model framework that we learn from a set of shape samples using recent manifold learning techniques. We model a category of shapes as a finite dimensional manifold which we approximate using Diffusion maps. Our method computes a Delaunay triangulation of the reduced space, considered as Euclidean, and uses the resulting space partition to identify the closest neighbors of any given shape based on its Nyström extension.