Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model

• We present a Markov random fields approach for cardiac fiber reconstruction and denoising from a set of sparse 2D DTI images.
• Statistical constraints are encoded to relate the missing fibers in the inter-slice gaps in cardiac DTI and the known fibers on the input 2D slices.
• A consistency term is incorporated to the MRF model to ensure that the obtained 3D fibrous structure is continuous and physiologically meaningfully.
• The validation shows accuracy and robustness, as well as potential for cardiac modeling.
• To the best of our knowledge, the proposed technique is the first statistically-driven approach for 3D fiber reconstruction.

The construction of subject-specific dense and realistic 3D meshes of the myocardial fibers is an important pre-requisite for the simulation of cardiac electrophysiology and mechanics. Current diffusion tensor imaging (DTI) techniques, however, provide only a sparse sampling of the 3D cardiac anatomy based on a limited number of 2D image slices. Moreover, heart motion affects the diffusion measurements, thus resulting in a significant amount of noisy fibers. This paper presents a Markov random field (MRF) approach for dense reconstruction of 3D cardiac fiber orientations from sparse DTI 2D slices. In the proposed MRF model, statistical constraints are used to relate the missing and the known fibers, while a consistency term is encoded to ensure that the obtained 3D meshes are locally continuous. The validation of the method using both synthetic and real DTI datasets demonstrates robust fiber reconstruction and denoising, as well as physiologically meaningful estimations of cardiac electrical activation.

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