Automatic 4D Reconstruction of Patient-Specific Cardiac Mesh with 1-to-1 Vertex Correspondence from Segmented Contours Lines

Abstract

We propose an automatic algorithm for the reconstruction of patient-specific cardiac mesh models with 1-to-1 vertex correspondence. In this framework, a series of 3D meshes depicting the endocardial surface of the heart at each time step is constructed, based on a set of border delineated magnetic resonance imaging (MRI) data of the whole cardiac cycle. The key contribution in this work involves a novel reconstruction technique to generate a 4D (i.e., spatial–temporal) model of the heart with 1-to-1 vertex mapping throughout the time frames. The reconstructed 3D model from the first time step is used as a base template model and then deformed to fit the segmented contours from the subsequent time steps. A method to determine a tree-based connectivity relationship is proposed to ensure robust mapping during mesh deformation. The novel feature is the ability to handle intra- and inter-frame 2D topology changes of the contours, which manifests as a series of merging and splitting of contours when the images are viewed either in a spatial or temporal sequence. Our algorithm has been tested on five acquisitions of cardiac MRI and can successfully reconstruct the full 4D heart model in around 30 minutes per subject. The generated 4D heart model conforms very well with the input segmented contours and the mesh element shape is of reasonably good quality. The work is important in the support of downstream computational simulation activities.

Variational Reconstruction of Left Cardiac Structure from CMR Images

“…Second, the reconstruction of left cardiac structure will facilitate rapid automated numerical characterization of point heart surface curvedness and thereby point function spatial-temporal fluctuations of curvedness reflect local heart muscle contraction, allowing comprehensive annotation of regional and global cardiac left ventricular structure and function [ 36 – 39 ] and aorto-ventricular matching before and after heart surgery [ 40 ]. Last, this method will contribute a sizeable contemporary CMR imaging atlas of left heart morphology and function in normal subjects and diverse diseased hearts in the near future [ 41 ]. These models from CMR images will also be used to validate LV three dimensional echocardiography measurements (i.e., LV volumes and ejection fraction) (3D-echo).…”

Numerical Modeling of Intraventricular Flow during Diastole after Implantation of BMHV

“…Thirdly, this study focused only on the simulation of BMHV at the mitral position without experimental validation, however, a number of numerical studies of mechanical heart valve at the aortic position using CFD solver FLUENT have been validated based on the comparisons between numerical and experimental results of angular leaflet motion and planar flow field [ 6 , 8 , 29 ]. Lastly, the ventricle was idealized as a truncated spheroid, however, in the future, the current framework could be applied to the studies of patient-specific heart model reconstructed from magnetic resonance imaging [ 52 , 53 ].…”

Surface Mesh Reconstruction from Cardiac MRI Contours

“…Delaunay triangulation is an explicit method that relies on finding a unique triangulation for all points given a set of points [16]. Lim et al [19] use Delaunay triangulations in order to reconstruct a cardiac mesh given a set of parallel SAX contours. By computing a connectivity tree between the set of contours, they project adjacent contours onto a 2D plane, where a Delaunay triangulation is computed between the contours, before projecting them back to 3D.…”

“…This mesh can be computed using various approaches, such as the convex hull of the input data, or by fitting a precomputed mesh to the data such as the meshes found in [8,9,19]. In our case, M 0 is computed as a tubular mesh connecting the contours, as explained below.…”