The correlation of 3D DT-MRI fiber disruption with structural and mechanical degeneration in porcine myocardium.

Evaluation of structural parameters following a myocardial infarction (MI) is important to assess left ventricular function and remodeling. In this study, we assessed the capability of 3D diffusion tensor magnetic resonance imaging (DT-MRI) to assess tissue degeneration shortly after an MI using a porcine model of infarction. Two days after an induced infarction, hearts were explanted and immediately scanned by a 3T MRI scanner with a diffusion tensor imaging protocol. 3D fiber tracks and clustering models were generated from the diffusion-weighted imaging data. We found in a normal explanted heart that DT-MRI fibers showed a multilayered helical structure, with fiber architecture and fiber density reflecting the integrity of muscle fibers. For infarcted heart explants, we observed either a lack of fibers or disruption of fibers in the infarcted regions. Contours of the disrupted DT-MRI fibers were found to be consistent with the infarcted regions. Both histological and mechanical analysis of the infarcted hearts suggested DT-MRI fiber disruption correlated with altered microstructure and tissue mechanics. The ability of 3D DT-MRI to accurately distinguish viable myocardium from dead myocardium only 2 days post infarct without the use of radioisotopes or ionotropic agents makes it a promising approach to evaluate cardiac damage early post-MI.

Volume illustration of muscle from diffusion tensor images.

Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.