A Sparse Tensor Decomposition with Multi-Dictionary Learning Applied to Diffusion Brain Imaging

• Autores: Caiafa, César Federico; Cichocki, Andrzej; Pestilli, Franco
• Año de publicación: 2017
• Idioma: inglés
• Tipo de recurso: documento de conferencia
• Materia: Diffusion MRI; Sparse Decomposition; Tensor Decomposition; Dictionary learning
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

We use a multidimensional signal representation that integrates diffusion Magnetic Resonance Imaging (dMRI) and tractography (brain connections) using sparse tensor decomposition. The representation encodes brain connections (fibers) into a very-large, but sparse, core tensor and allows to predict dMRI measurements based on a dictionary of diffusion signals. We propose an algorithm to learn the constituent parts of the model from a dataset. The algorithm assumes a tractography model (support of core tensor) and iteratively minimizes the Frobenius norm of the error as a function of the dictionary atoms, the values of nonzero entries in the sparse core tensor and the fiber weights. We use a nonparametric dictionary learning (DL) approach to estimate signal atoms. Moreover, the algorithm is able to learn multiple dictionaries associated to different brain locations (voxels) allowing for mapping distinctive tissue types. We illustrate the algorithm through results obtained on a large in-vivo high-resolution dataset.
Fil: Caiafa, César Federico. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; Argentina. Indiana University; Estados Unidos
Fil: Cichocki, Andrzej. Labsp. Riken; Japón
Fil: Pestilli, Franco. Indiana University; Estados Unidos
Signal Processing with Adaptive Sparse Structured Representations workshop
Lisboa
Portugal
University of Lisbon