Dictionary+Wavelet Model With Nested-Minorized VB-EM for SMS-CAIPI R-fMRI Reconstruction

Abstract

Resting-state functional magnetic resonance imaging (R-fMRI) applications can entail a higher temporal-sampling rate that trades off spatial resolution, thereby challenging effective scientific studies. To enable higher spatial resolution, current schemes speedup per-timeframe scanning by reconstruction from simultaneous multislice (SMS) magnetic resonance imaging (MRI) with k-space undersampling (sometimes temporal undersampling), while using prior models on the signal. We propose a novel algorithmic framework to reconstruct R-fMRI (SMS with controlled aliasing) that has, both, k-space undersampling and temporal undersampling. We propose a coupled spatiotemporal sparse model, incorporating (i) a robust spatially-regularized temporal-dictionary prior and (ii) a spatiotemporal wavelet prior, which we fit efficiently using variational Bayesian expectation maximization with nested minorization (VBEMNM). We show that our framework has the potential to enable higher spatial resolution without increasing scan time in R-fMRI that has inherently weak signals and is therefore prone to large physiological fluctuations, acquisition noise, and imaging artifacts. Qualitative and quantitative evaluation on retrospectively undersampled brain R-fMRI shows that estimates of resting-state networks from our framework and the boost in temporal stability given by our framework compares favourably to existing methods for R-fMRI reconstruction.