Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays

Cesar F. Caiafa, Olaf Sporns, Andrew Saykin, Franco Pestilli

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both dMRI data and connectome resolution, and can become very large when applied to modern data. In this paper, we introduce a method to encode dMRI signals and large connectomes, i.e., those that range from hundreds of thousands to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFESD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.

Original languageEnglish (US)
Pages (from-to)4341-4352
Number of pages12
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - Jan 1 2017
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: Dec 4 2017Dec 9 2017

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Magnetic resonance imaging
Tensors
Decomposition
Convex optimization
Brain
Magnetic Resonance Imaging

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

Cite this

Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays. / Caiafa, Cesar F.; Sporns, Olaf; Saykin, Andrew; Pestilli, Franco.

In: Advances in Neural Information Processing Systems, Vol. 2017-December, 01.01.2017, p. 4341-4352.

Research output: Contribution to journalConference article

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