Heart wall myofibers are arranged in minimal surfaces to optimize organ function.

Heart wall myofibers wind as helices around the ventricles, strengthening them in a manner analogous to the reinforcement of concrete cylindrical columns by spiral steel cables [Richart FE, et al. (1929) Univ of Illinois, Eng Exp Stn Bull 190]. A multitude of such fibers, arranged smoothly and regularly, contract and relax as an integrated functional unit as the heart beats. To orchestrate this motion, fiber tangling must be avoided and pumping should be efficient. Current models of myofiber orientation across the heart wall suggest groupings into sheets or bands, but the precise geometry of bundles of myofibers is unknown. Here we show that this arrangement takes the form of a special minimal surface, the generalized helicoid [Blair DE, Vanstone JR (1978) Minimal Submanifolds and Geodesics 13-16], closing the gap between individual myofibers and their collective wall structure. The model holds across species, with a smooth variation in its three curvature parameters within the myocardial wall providing tight fits to diffusion magnetic resonance images from the rat, the dog, and the human. Mathematically it explains how myofibers are bundled in the heart wall while economizing fiber length and optimizing ventricular ejection volume as they contract. The generalized helicoid provides a unique foundation for analyzing the fibrous composite of the heart wall and should therefore find applications in heart tissue engineering and in the study of heart muscle diseases.

Maurer-Cartan Forms for Fields on Surfaces: Application to Heart Fiber Geometry.

We study the space of first order models of smooth frame fields using the method of moving frames. By exploiting the Maurer-Cartan matrix of connection forms we develop geometrical embeddings for frame fields which lie on spherical, ellipsoidal and generalized helicoid surfaces. We design methods for optimizing connection forms in local neighborhoods and apply these to a statistical analysis of heart fiber geometry, using diffusion magnetic resonance imaging. This application of moving frames corroborates and extends recent characterizations of muscle fiber orientation in the heart wall, but also provides for a rich geometrical interpretation. In particular, we can now obtain direct local measurements of the variation of the helix and transverse angles, of fiber fanning and twisting, and of the curvatures of the heart wall in which these fibers lie.

Moving Frames for Heart Fiber Reconstruction.

The method of moving frames provides powerful geometrical tools for the analysis of smoothly varying frame fields. However, in the face of missing measurements, a reconstruction problem arises, one that is largely unexplored for 3D frame fields. Here we consider the particular example of reconstructing impaired cardiac diffusion magnetic resonance imaging (dMRI) data. We combine moving frame analysis with a diffusion inpainting scheme that incorporates rule-based priors. In contrast to previous reconstruction methods, this new approach uses comprehensive differential descriptors for cardiac fibers, and is able to fully recover their orientation. We demonstrate the superior performance of this approach in terms of error of fit when compared to alternate methods. We anticipate that these tools could find application in clinical settings, where damaged heart tissue needs to be replaced or repaired, and for generating dense fiber volumes in electromechanical modelling of the heart.

Moving frames for heart fiber geometry.

Elongated cardiac muscle cells named cardiomyocytes are densely packed in an intercellular collagen matrix and are aligned to helical segments in a manner which facilitates pumping via alternate contraction and relaxation. Characterizing the geometrical variation of their groupings as cardiac fibers is central to our understanding of normal heart function. Motivated by a recent abstraction by Savadjiev et al. of heart wall fibers into generalized helicoid minimal surfaces, this paper develops an extension based on differential forms. The key idea is to use Maurer-Cartan's method of moving frames to study the rotations of a frame field attached to the local fiber direction. This approach provides a new set of parameters that are complimentary to those of Savadjiev et al. and offers a framework for developing new models of the cardiac fiber architecture. This framework is used to compute the generalized helicoid parameters directly, without the need to formulate an optimization problem. The framework admits a straightforward numerical implementation that provides statistical measurements consistent with those previously reported. Using Diffusion MRI we demonstrate that one such specialization, the homeoid, constrains fibers to lie locally within ellipsoidal shells and yields improved fits in the rat, the dog and the human to those obtained using generalized helicoids.

Cardiac fiber inpainting using Cartan forms.

Recent progress in diffusion imaging has lead to in-vivo acquisitions of fiber orientation data in the beating heart. Current methods are however limited in resolution to a few short-axis slices. For this particular application and others where the diffusion volume is subsampled, partial or even damaged, the reconstruction of a complete volume can be challenging. To address this problem, we present two complementary methods for fiber reconstruction from sparse orientation measurements, both of which derive from second-order properties related to fiber curvature as described by Maurer-Cartan connection forms. The first is an extrinsic partial volume reconstruction method based on principal component analysis of the connection forms and is best put to use when dealing with highly damaged or sparse data. The second is an intrinsic method based on curvilinear interpolation of the connection forms on ellipsoidal shells and is advantageous when more slice data becomes available. Using a database of 8 cardiac rat diffusion tensor images we demonstrate that both methods are able to reconstruct complete volumes to good accuracy and lead to low reconstruction errors.