Interleaved EPI based fMRI improved by multiplexed sensitivity encoding (MUSE) and simultaneous multi-band imaging

Abstract

Functional magnetic resonance imaging (fMRI) is a non-invasive and powerful imaging tool for detecting brain activities. The majority of fMRI studies are performed with single-shot echo-planar imaging (EPI) due to its high temporal resolution. Recent studies have demonstrated that, by increasing the spatial-resolution of fMRI, previously unidentified neuronal networks can be measured. However, it is challenging to improve the spatial resolution of conventional single-shot EPI based fMRI. Although multi-shot interleaved EPI is superior to single-shot EPI in terms of the improved spatial-resolution, reduced geometric distortions, and sharper point spread function (PSF), interleaved EPI based fMRI has two main limitations: 1) the imaging throughput is lower in interleaved EPI; 2) the magnitude and phase signal variations among EPI segments (due to physiological noise, subject motion, and B0 drift) are translated to significant in-plane aliasing artifact across the field of view (FOV). Here we report a method that integrates multiple approaches to address the technical limitations of interleaved EPI-based fMRI. Firstly, the multiplexed sensitivity-encoding (MUSE) post-processing algorithm is used to suppress in-plane aliasing artifacts resulting from time-domain signal instabilities during dynamic scans. Secondly, a simultaneous multi-band interleaved EPI pulse sequence, with a controlled aliasing scheme incorporated, is implemented to increase the imaging throughput. Thirdly, the MUSE algorithm is then generalized to accommodate fMRI data obtained with our multi-band interleaved EPI pulse sequence, suppressing both in-plane and through-plane aliasing artifacts. The blood-oxygenation-level-dependent (BOLD) signal detectability and the scan throughput can be significantly improved for interleaved EPI-based fMRI. Our human fMRI data obtained from 3 Tesla systems demonstrate the effectiveness of the developed methods. It is expected that future fMRI studies requiring high spatial-resolvability and fidelity will largely benefit from the reported techniques.

Figure 1. The simultaneous 2-band interleaved EPI pulse sequence with TE compensation of first slice, where m is an integer and ESP is the echo-spacing time of EPI echo train.

The TE compensation of the first slice is the multiples of ESP.

Figure 2. Mathematical simulation of the point spread function (PSF) for MUSE reconstruction: (a) The theoretic PSF, (b) the PSF of SENSE-produced images, and (c) the PSF of MUSE-produced images corresponding to different numbers of EPI segments.

(d) The FWHM values of PSF waveforms corresponding to different acquisition and reconstruction schemes.

Figure 3. The impact of data inconsistencies on MUSE produced image quality: (a) The gold standard of our numerical simulation.

(b) The conventional interleaved EPI in the presence of inter-segment phase variation (0.3 radians), magnitude variation (20%), translational motion (0.5 pixel) and rotational motion (1 degree). (c) The MUSE reconstruction of the same data set shown in b. (d) Artifact levels in images reconstructed with Fourier transform (i.e., conventional interleaved EPI: circles) and MUSE (triangles) in the presence of inter-segment phase variations. (e) Artifact levels in images reconstructed with Fourier transform (circles) and MUSE (triangles) in the presence of inter-segment magnitude variations. (f) Artifact levels in images reconstructed with Fourier transform (circles) and MUSE (triangles) in the presence of inter-segment position changes (at a fixed level of translation of 1 pixel and various levels of rotation). (g) Artifact levels in images reconstructed with Fourier transform (circles) and MUSE (triangles) in the presence of inter-segment signal variations simultaneously resulting from multiple sources.

Figure 4. Time-domain signal stabilities, measured by the temporal fluctuation noise (upper row) and SFNR (lower row), of images reconstructed with conventional interleaved EPI reconstruction (left), the SENSE algorithm (middle), and the MUSE algorithm (right).

Figure 5. Time course profiles of an activated voxel in the gold standard data set, 2-band 2-shot MUSE reconstructed images, and 2-band 2-shot SENSE reconstructed images.

Figure 6. Comparison of images before and after multi-band MUSE reconstruction: (a) Multi-band images reconstructed by directly applying 2D Fourier transform to a full k-space data set (obtained from two consecutive TRs).

(b) Images obtained with the multi-band MUSE reconstruction algorithm, showing 44 axial slices without in-plane and through-plane aliasing artifacts. Functional activation is displayed by color.

Figure 7. Comparison of time domain signal stability with conventional and multi-band MUSE reconstruction: (a) The temporal fluctuation noise levels in multi-band interleaved EPI data reconstructed with the conventional procedure.

(b) The temporal fluctuation noise levels in multi-band interleaved EPI data reconstructed with the MUSE algorithm.

Figure 8. Comparison of SFNR with conventional and multi-band MUSE reconstruction: (a) The SFNR values of multi-band interleaved EPI data reconstructed with the conventional procedure.

(b) The SFNR values of multi-band interleaved EPI data reconstructed with the MUSE algorithm.

Figure 9. High-resolution multi-band images (1.8×1.8×1.8 mm³) reconstructed by (a) the multi-band MUSE algorithm, and (b) the multi-band UNFOLD technique, showing 40 axial slices without in-plane and through-plane aliasing artifacts.

Functional activation is displayed on top of MUSE-reconstructed and UNFOLD-reconstructed images.

Figure 10. Functional activation maps derived from (a) 2-band 2-shot MUSE-fMRI (1.8×1.8×1.8 mm³), (b) multi-band UNFOLD fMRI data (1.8×1.8×1.8 mm³) and (c) conventional single-shot EPI based fMRI (3.75×3.75×3.8 mm³) are shown in the upper row.

The contour lines of the activation areas are shown in the lower row.