Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme

TL;DR

This paper introduces a novel self-navigated spiral manifold regularization scheme for prospectively accelerated dynamic speech MRI at 3 Tesla, achieving high spatial and temporal resolution with improved image quality over existing methods.

Contribution

The study presents a self-navigated variable density spiral approach with manifold regularization, enabling improved prospective dynamic speech MRI without explicit motion binning.

Findings

Achieved 2.4mm spatial resolution and 17.4ms temporal resolution for single-slice imaging.

Demonstrated superior image quality with reduced blurring compared to other reconstruction constraints.

Implicit motion binning was validated through Laplacian matrix analysis.

Abstract

This work proposes a self-navigated variable density spiral(VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3T. Short readout 1.3ms spirals were used to minimize off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract. The manifold model leveraged similarities between frames sharing similar speech postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian matrix of the manifold. Reconstruction was posed as a SENSE-based non-local soft weighted temporal regularization scheme. Our approach was compared against view-sharing, low-rank, finite difference, extra-dimension-based sparsity reconstruction constraints. Under-sampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges were performed in a retrospectively under-sampled data. For prospective under-sampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring were performed by three voice research experts. Region of interest analysis at articulator boundaries were performed to assess articulatory motion. Our scheme provided improved reconstruction over the others. With prospective under-sampling, a spatial resolution of 2.4mm2/pixel and a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for 3-slice imaging were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Our method demonstrated superior image quality scores in reducing spatial and temporal blurring. While it exhibited faint alias artifacts similar to temporal finite-difference, it provided statistically significant improvements over remaining constraints.