Segmentation of PhaseContrast MR Images for Aortic Pulse Wave Velocity Measurements Aortic stiffness is an important diagnostic and prognostic parameter for many diseases, and is estimated by measuring the Pulse Wave Velocity (PWV) from Cardiac Magnetic Resonance (CMR) images. However, this process requires combinations of multiple sequences, which makes the acquisition long and processing tedious. We propose a method for aorta segmentation and centerline extraction from parasagittal PhaseContrast (PC) CMR images. The method uses the order of appearance of the blood flow in PC images to track the aortic centerline from the seed start position to the seed end position of the aorta. The only required user interaction involves selection of 2 input seed points for the start and end position of the aorta. We validate our results against the ground truth manually extracted centerlines from parasagittal PC images and anatomical MR images. The resulting measurement values of both centerline length and PWV show high accuracy and low variability, which allows for use in clinical setting. The main advantage of our method is that it requires only velocity encoded PC image, while being able to process images encoded only in one direction.

3D right ventricular endocardium segmentation in cardiac magnetic resonance images by using a new intermodality statistical shape modelling method Abstract   Objective  Statistical shape modelling (SSM) has established as a powerful method for segmenting the left ventricle in cardiac magnetic resonance (CMR) images However, applying them to segment the right ventricle (RV) is not straightforward because of the complex structure of this chamber. Our aim was to develop a new intermodality SSMbased approach to detect the RV endocardium in CMR data.    Methods  Realtime transthoracic 3D echocardiographic (3DE) images of 219 retrospective patients were used to populate a large database containing 4347 3D RV surfaces and train a model. The initial position, orientation and scale of the model in the CMR stack were semiautomatically derived. The detection process consisted in iteratively deforming the model to match endocardial borders in each CMR plane until convergence was reached. Clinical values obtained with the presented SSM method were compared with goldstandard (GS) corresponding parameters.    Results  CMR images of 50 patients with different pathologies were used to test the proposed segmentation method. Average processing time was 2xe2x80xafmin (including manual initialization) per patient. High correlations (r2xe2x80xafu003exe2x80xaf0.76) and not significant bias (BlandAltman analysis) were observed when evaluating clinical parameters. Quantitative analysis showed high values of Dice coefficient (0.87xe2x80xafxc2xb1xe2x80xaf0.03), acceptable Hausdorff distance (9.35xe2x80xafxc2xb1xe2x80xaf1.51xe2x80xafmm) and small pointtosurface distance (1.91xe2x80xafxc2xb1xe2x80xaf0.26xe2x80xafmm).    Conclusion  A novel SSMbased approach to segment the RV endocardium in CMR scans by using a model trained on 3DEderived RV endocardial surfaces, was proposed. This intermodality technique proved to be rapid when segmenting the RV endocardium with an accurate anatomical delineation, in particular in apical and basal regions.