Investigating Cardiac Motion Patterns Using Synthetic High-Resolution 3D Cardiovascular Magnetic Resonance Images and Statistical Shape Analysis

Abstract

Diagnosis of ventricular dysfunction in congenital heart disease is more and more based on medical imaging, which allows investigation of abnormal cardiac morphology and correlated abnormal function. Although analysis of 2D images represents the clinical standard, novel tools performing automatic processing of 3D images are becoming available, providing more detailed and comprehensive information than simple 2D morphometry. Among these, statistical shape analysis (SSA) allows a consistent and quantitative description of a population of complex shapes, as a way to detect novel biomarkers, ultimately improving diagnosis and pathology understanding. The aim of this study is to describe the implementation of a SSA method for the investigation of 3D left ventricular shape and motion patterns and to test it on a small sample of 4 congenital repaired aortic stenosis patients and 4 age-matched healthy volunteers to demonstrate its potential. The advantage of this method is the capability of analyzing subject-specific motion patterns separately from the individual morphology, visually and quantitatively, as a way to identify functional abnormalities related to both dynamics and shape. Specifically, we combined 3D, high-resolution whole heart data with 2D, temporal information provided by cine cardiovascular magnetic resonance images, and we used an SSA approach to analyze 3D motion per se. Preliminary results of this pilot study showed that using this method, some differences in end-diastolic and end-systolic ventricular shapes could be captured, but it was not possible to clearly separate the two cohorts based on shape information alone. However, further analyses on ventricular motion allowed to qualitatively identify differences between the two populations. Moreover, by describing shape and motion with a small number of principal components, this method offers a fully automated process to obtain visually intuitive and numerical information on cardiac shape and motion, which could be, once validated on a larger sample size, easily integrated into the clinical workflow. To conclude, in this preliminary work, we have implemented state-of-the-art automatic segmentation and SSA methods, and we have shown how they could improve our understanding of ventricular kinetics by visually and potentially quantitatively highlighting aspects that are usually not picked up by traditional approaches.

Computational Modeling of Right Ventricular Motion and Intracardiac Flow in Repaired Tetralogy of Fallot

“…A similar method has been previously demonstrated by Biffi et al on the left ventricle in aortic stenosis patients. 7 The approach relied on an image registration technique based on a control-point instance of the Large Deformation Diffeomorphic Geometric Mapping (LDDMM) implemented into the open-source software Deformetrica. 11 Using Deformetrica, the motion of the surface mesh from the 3D model throughout the cardiac cycle was guided by endocardial data contour points in both short-axis and longaxis trajectories.…”

Abnormal Diastolic Hemodynamic Forces: A Link Between Right Ventricular Wall Motion, Intracardiac Flow, and Pulmonary Regurgitation in Repaired Tetralogy of Fallot

“…Two separate sets of mappings were computed: one derived from the short-axis cine and the other from long-axis cine (i.e., three-chamber and four-chamber planes); the final transformation was then obtained by linearly superimposing the two mapping fields. The computed diffeomorphisms were applied to the segmented 3D end-diastolic model of the RV, to obtain 30 triangulated surface meshes with the same connectivity properties and with vertices correspondence ( 19 ). Supplementary Video 1 demonstrates an example of the RV motion reconstruction.…”

A Spatiotemporal Volumetric Interpolation Network for 4D Dynamic Medical Image

“…Since most biological movements have a relatively fixed motion pattern, especially in cardiac motion [7], we present a regression-based module to model the relationship between cardiac motion of the cardiac cycle and time phase (as shown in Fig. 4).…”