R-fMRI reconstruction from k–t undersampled data using a subject-invariant dictionary model and VB-EM with nested minorization

• Published in: Medical image analysis Vol. 65; p. 101752
• Main Authors: Kulkarni, Prachi H., Merchant, S.N., Awate, Suyash P.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2020; Elsevier BV
• Subjects: Bayesian analysis; Brain mapping; Cerebral cortex; Cortex (temporal); Dictionaries; Dictionary prior; Expectation maximization; Functional magnetic resonance imaging; Inference; Invariants; Magnetic resonance imaging; Nested minorization; Neuroimaging; R-fMRI; Reconstruction; Robust; Sparse; Spatial discrimination; Spatial regularization; Spatial resolution; Subject-invariant; Temporal resolution; Undersample; Variational Bayesian factorization; Reconstruction; Subject-invariant; Variational Bayesian factorization; Spatial regularization; Robust; Nested minorization; Dictionary prior; Expectation maximization; Sparse; R-fMRI; Undersample
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2020.101752

•Faster R-fMRI imaging allows higher spatial resolution and more reliable functional connectivity analysis.•We propose a novel undersampling scheme in k-space and time (k-t) both to provide the necessary speedup.•We propose a novel dictionary-based model on the signal, which is robust, spatially regularized, and subject invariant by leveraging an equivalence-class structure on the dictionary.•We propose a novel Bayesian inference framework based on variational Bayesian expectation maximization with nested minorization (VB-EM-NM), which allows us to estimate per-voxel uncertainty in the reconstruction.•Empirical evaluation of (i) R-fMRI reconstructions from simulated data and (ii) functional-network estimates from reconstructions of brain R-fMRI demonstrate that our framework improves over the state of the art, and, additionally, enables significantly higher spatial resolution. [Display omitted] Higher spatial resolution in resting-state functional magnetic resonance imaging (R-fMRI) can give reliable information about the functional networks in the cerebral cortex. Typical methods can achieve higher spatial or temporal resolution by speeding up scans using either (i) complex pulse-sequence designs or (ii) k-space undersampling coupled with priors on the signal. We propose to undersample the R-fMRI acquisition in k-space and time to speedup scans in order to improve spatial resolution. We propose a novel model-based R-fMRI reconstruction framework using a robust, subject-invariant, spatially regularized dictionary prior on the signal. Furthermore, we propose a novel inference framework based on variational Bayesian expectation maximization with nested minorization (VB-EM-NM). Our inference framework allows us to provide an estimate of uncertainty of the reconstruction, unlike typical reconstruction methods. Empirical evaluation of (i) simulated R-fMRI reconstruction and (ii) functional-network estimates from brain R-fMRI reconstructions demonstrate that our framework improves over the state of the art, and, additionally, enables significantly higher spatial resolution.