High-Resolution Ex Vivo Microstructural MRI After Restoring Ventricular Geometry via 3D Printing

Abstract

Computational modeling of the heart requires accurately incorporating both gross anatomical detail and local microstructural information. Together, these provide the necessary data to build 3D meshes for simulation of cardiac mechanics and electrophysiology. Recent MRI advances make it possible to measure detailed heart motion in vivo, but in vivo microstructural imaging of the heart remains challenging. Consequently, the most detailed measurements of microstructural organization and microanatomical infarct details are obtained ex vivo. The objective of this work was to develop and evaluate a new method for restoring ex vivo ventricular geometry to match the in vivo configuration. This approach aids the integration of high-resolution ex vivo microstructural information with in vivo motion measurements. The method uses in vivo cine imaging to generate surface meshes, then creates a 3D printed left ventricular (LV) blood pool cast and a pericardial mold to restore the ex vivo cardiac geometry to a mid-diastasis reference configuration. The method was evaluated in healthy (N = 7) and infarcted (N = 3) swine. Dice similarity coefficients were calculated between in vivo and ex vivo images for the LV cavity (0.93 ± 0.01), right ventricle (RV) cavity (0.80 ± 0.05), and the myocardium (0.72 ± 0.04). The R ² coefficient between in vivo and ex vivo LV and RV cavity volumes were 0.95 and 0.91, respectively. These results suggest that this method adequately restores ex vivo geometry to match in vivo geometry. This approach permits a more precise incorporation of high-resolution ex vivo anatomical and microstructural data into computational models that use in vivo data for simulation of cardiac mechanics and electrophysiology.

Keywords: 3D printing; Cardiac electromechanics; Computational modeling; Magnetic resonance imaging.

Fig. 1

Workflow for 3D printing restoration of ventricular geometry. In vivo images were acquired followed by manual left ventricle (LV) and pericardial segmentation. Segmentations were then converted to surface meshes and 3D printed. The LV blood pool cast was inserted into the excised heart, the heart was placed into the pericardial mold, and the right ventricle (RV) was filled with a rubber silicone compound. Ex vivo images were acquired and rigidly registered with in vivo images.

Fig. 2

Image overlays of a mid-ventricular slice of an infarcted swine heart with a magnified view of the infarcted tissue. In vivo cine images were registered with in vivo LGE (left), ex vivo LGE (middle), and ex vivo cDTI tensor glyphs (right). The epicardial and endocardial boundaries between healthy and infarcted tissue agree well in each images. The manual segmentation (white contour) of the infarct as seen on the ex vivo LGE is superimposed on the ex vivo DTI.

Fig. 3

Scatter plots of in vivo and ex vivo LV and RV cavity volumes showing both healthy hearts (o) and infarct subjects (+). Linear regression fits and 95% confidence interval (CI) are reported.