This paper presents methods to build histo-anatomically detailed individualized cardiac models. The models are based on high-resolution three-dimensional anatomical and/or diffusion tensor magnetic resonance images, combined with serial histological sectioning data, and are used to investigate individualized cardiac function. The current state of the art is reviewed, and its limitations are discussed. We assess the challenges associated with the generation of histo-anatomically representative individualized in silico models of the heart. The entire processing pipeline including image acquisition, image processing, mesh generation, model set-up and execution of computer simulations, and the underlying methods are described. The multifaceted challenges associated with these goals are highlighted, suitable solutions are proposed, and an important application of developed highresolution structure-function models in elucidating the effect of individual structural heterogeneity upon wavefront dynamics is demonstrated.

Cardiac computational models of electrical conduction, mechanical activation, hemodynamics and metabolism require detailed information about the structural arrangement of functionally heterogeneous cardiac cell types. However, current state-of-the-art models lack anatomically accurate cell type localization, which limits their utility.Histological sections combine unique resolution with discrimination of tissues and anatomical structures, but they suffer from alignment and deformation problems. On the other hand, MRI datasets preserve the correct geometry, but provide less micro structural detail. This paper presents a method for aligning MRI and histological datasets to obtain a highly detailed, geometrically correct anatomical description of the heart. An iterative process is used to correct the various 2D and 3D, rigid and non-rigid transforms, introduced in the histology preparation and acquisition. Validation is performed by calculating distances between anatomical landmarks in both datasets, and by quantifying tissue overlap. Results illustrate the suitability of the proposed algorithm to produce detailed, accurate cardiac models.

Abstract. Cardiac histo-anatomical structure is a key determinant in all aspects of cardiac function. While some characteristics of micro-and macrostructure can be quantified using non-invasive imaging methods, histology is still the modality that provides the best combination of resolution and identification of cellular/sub-cellular substrate identities. The main limitation of histology is that it does not provide inherently consistent three-dimensional (3D) volume representations. This paper presents methods developed within our group to reconstruct 3D histological datasets. It includes the use of high-resolution MRI and block-face images to provide supporting volumetric datasets to guide spatial reintegration of 2D histological section data, and presents recent developments in sample preparation, data acquisition, and image processing.