Mapping cardiac surface mechanics with structured light imaging

Abstract

Cardiovascular disease often manifests as a combination of pathological electrical and structural heart remodeling. The relationship between mechanics and electrophysiology is crucial to our understanding of mechanisms of cardiac arrhythmias and the treatment of cardiac disease. While several technologies exist for describing whole heart electrophysiology, studies of cardiac mechanics are often limited to rhythmic patterns or small sections of tissue. Here, we present a comprehensive system based on ultrafast three-dimensional (3-D) structured light imaging to map surface dynamics of whole heart cardiac motion. Additionally, we introduce a novel nonrigid motion-tracking algorithm based on an isometry-maximizing optimization framework that forms correspondences between consecutive 3-D frames without the use of any fiducial markers. By combining our 3-D imaging system with nonrigid surface registration, we are able to measure cardiac surface mechanics at unprecedented spatial and temporal resolution. In conclusion, we demonstrate accurate cardiac deformation at over 200,000 surface points of a rabbit heart recorded at 200 frames/s and validate our results on highly contrasting heart motions during normal sinus rhythm, ventricular pacing, and ventricular fibrillation.

Fig. 1.

Setup of structured light imaging system. A digital light processing (DLP) projector sequentially projects 10 binary, fringe patterns onto an object. Binary fringe patterns are defocused to produce sinusoidal fringes. A computer-controlled complementary metal oxide semiconductor (CMOS) camera simultaneously captures fringe images (I1–I10). Sinusoidal fringe images I1–I3 are combined to produce a wrapped phase map of the object. Fringe images I4–I10 are then used to unwrap phase discontinuities to produce an unwrapped phase map that can be mapped to depth with a phase-to-height conversion algorithm. Each 3-dimensional (3-D) surface is then wrapped with a black and white (B/W) image of the object to capture both 3-D geometry and texture.

Fig. 2.

Markerless motion tracking algorithm. For a given input sequence of 3-D scans (blue), a template (T₀) is created for the first frame of the scan (S₀) to remove noise along edges and fill holes in the 3-D surface. During surface registration, template surfaces (red) are matched to subsequent input scans using a pairwise nonrigid registration optimization technique. Once optimization is complete, a new template is produced that aligns with the input scan. This process is repeated sequentially through all frames of interest.

Fig. 3.

Validation of markerless motion tracking algorithm during sinus rhythm (SR). A: anterior surface of the right ventricle (RV) labeled with 10 points (7 white and 3 bicolored). B: 3-D trajectories of bicolored points. For each bicolored point, 3 trajectories are plotted based on the 3 tracking methods employed: manual tracking (black), texture correlation (brown, orange, blue), and surface matching (gray, purple, green). Colored dots at the centroid of each trajectory represent Euclidian error for each motion-tracking algorithm. C: absolute error in X, Y, and Z directions and total error for each bicolored point over 2 cardiac cycles.

Fig. 4.

Validation of markerless motion tracking algorithm for n = 3 hearts for SR, 200 ms cycle length (CL) ventricular pacing, and 143 ms ventricular pacing. Left: comparison of mean error for markerless surface matching (white) to traditional texture correlation (black). Right: comparison of mean error for markerless surface matching during SR, 200 ms CL ventricular pacing, and 143 ms ventricular pacing. Error bars represent standard deviation of the mean. Significance between groups is calculated with the Mann-Whitney U-test. P < 0.05 considered significant; NS, not significant.

Fig. 5.

Analysis of motion in the beating rabbit heart for SR (A), apical pacing (CL = 200 ms; B), and ventricular fibrillation (C). For each case, sequences of 6 normalized displacement maps are displayed. Location of sequence frames is indicated by green bars on a simultaneously recorded ECG (gray). Displacement energy (blue), a measure of total tissue displacement, is overlaid on each respective ECG.