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, that we call the shape prior manifold. 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.Our contribution lies in three aspects. First, we propose a solution to the pre-image problem and define the projection of a shape onto the manifold. Based on closest neighbors for the Diffusion distance, we then describe a variational framework for manifold denoising. Finally, we introduce a shape prior term for the deformable framework through a non-linear energy term designed to attract a shape towards the manifold at given constant embedding. Results on shapes of cars and ventricule nuclei are presented and demonstrate the potentials of our method.

BackgroundThe 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. For this purpose, the k-t BLAST acceleration technique was combined with TPM black-blood functional imaging of the heart. Different k-t factors were evaluated with respect to their impact on the quantitative assessment of cardiac motion.ResultsIt is demonstrated that a k-t BLAST factor of two can be used with a marginal, but statistically significant deterioration of the quantitative motion data. Further increasing the k-t acceleration causes substantial alteration of the peak velocities and the motion pattern, but the temporal behavior of the contraction is well maintained up to an acceleration factor of six.ConclusionsThe application of k-t BLAST for the acceleration of TPM appears feasible. A reduction of the acquisition time of almost 45% could be achieved without substantial loss of quantitative motion information.

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. Whole heart MR data can be processed to yield detailed anatomical models including the coronary veins. Cine MR data can be used to measure the motion of the LV to determine which regions are late-activating. Finally, delayed Gadolinium enhancement imaging can be used to detect regions of scarring. This paper presents a complete platform for the guidance of CRT using pre-procedural MR data combined with live x-ray fluoroscopy. The platform was used for 21 patients undergoing CRT in a standard catheterization laboratory. The patients underwent cardiac MRI prior to their procedure. For each patient, a MRI-derived cardiac model, showing the LV lead targets, was registered to x-ray fluoroscopy using multiple views of a catheter looped in the right atrium. Registration was maintained throughout the procedure by a combination of C-arm/x-ray table tracking and respiratory motion compensation. Validation of the registration between the three-dimensional (3D) roadmap and the 2D x-ray images was performed using balloon occlusion coronary venograms. A 2D registration error of 1.2 ± 0.7 mm was achieved. In addition, a novel navigation technique was developed, called Cardiac Unfold, where an entire cardiac chamber is unfolded from 3D to 2D along with all relevant anatomical and functional information and coupled to real-time device detection. This allowed more intuitive navigation as the entire 3D scene was displayed simultaneously on a 2D plot. The accuracy of the unfold navigation was assessed off-line using 13 patient data sets by computing the registration error of the LV pacing lead electrodes which was found to be 2.2 ± 0.9 mm. Furthermore, the use of Unfold Navigation was demonstrated in real-time for four clinical cases.

Abstract. 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. We derive a non-linear shape prior term designed to attract a shape towards the shape prior manifold at given constant embedding. Results on shapes of ventricle nuclei demonstrate the potential of our method for segmentation tasks.