title: Statistically-driven 3D fiber reconstruction and denoising from multi-slice cardiac DTI using a Markov random field model.
abstract: 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.
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain. Electronic address: karim.lekadir@upf.edu.
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK.
authors.forename: Matthias
authors.initials: M
authors.lastname: Lange
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Veronika A
authors.initials: VA
authors.lastname: Zimmer
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
authors.forename: Corné
authors.initials: C
authors.lastname: Hoogendoorn
authors.affiliation: Center for Computational Imaging & Simulation Technologies in Biomedicine, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK. Electronic address: a.frangi@sheffield.ac.uk.
authors.forename: Alejandro F
authors.initials: AF
authors.lastname: Frangi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000379:methods*; D008390:Markov Chains; D015233:Models, Statistical; D009206:Myocardium / Q000166:cytology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D059629:Signal-To-Noise Ratio
keywords: Cardiac fibers; Diffusion tensor imaging; Fiber denoising; Fiber prediction; Markov random fields