Robust model-based quantification of global ventricular torsion from spatially sparse three-dimensional time series data by orthogonal distance regression: evaluation in a canine animal model under different pacing regimes

Background: Quantification of global ventricular rotational deformation, expressed as twist or torsion, and its dynamic changes is important in understanding the pathophysiology of heart disease and its therapy. Various techniques, such as sonomicrometry, allow tracking of specific sites within the myocardium. Quantification of twist from such data requires a longitudinal reference axis of rotation. Current methods require specific positioning and numbers of myocardial markers and assumptions about temporal positional evolution that may be violated during dyssynchronous contraction.

Methods: We present a new method to assess myocardial twist that makes minimal fully explicit assumptions while removing extraneous assumptions, by performing a least squares orthogonal distance regression of all position data on an ellipsoidal ventricular model. Rotational deformation is quantified in terms of the ellipsoid's internal coordinate system, allowing intuitive visualization.

Results: We tested this method on a set of sparse, noisy sonomicrometric crystal data in dogs under different pacing regimes to model dyssynchrony and cardiac resynchronization. We found that this method yielded robust and plausible data. This technique is also fully automated while identifying when data may be insufficient for reliable quantification of rotational deformation.

Conclusion: This approach may allow future analysis of myocardial contraction with less tracking sites and relaxed positioning requirements while identifying situations where data are insufficient for reliable quantification of rotational deformation.