Simultaneous metabolic and functional imaging of the brain using SPICE

Abstract

Purpose To enable simultaneous high‐resolution mapping of brain function and metabolism. Methods An encoding scheme was designed for interleaved acquisition of functional MRI (fMRI) data in echo volume imaging trajectories and MR spectroscopic imaging (MRSI) data in echo‐planar spectroscopic imaging trajectories. The scheme eliminates water and lipid suppression and utilizes free induction decay signals to encode both functional and metabolic information with ultrashort TE, short TR, and sparse sampling of )(normalk,0.166667emt‐space. A subspace‐based image reconstruction method was introduced for processing both the fMRI and MRSI data. The complementary information in the fMRI and MRSI data sets was also utilized to improve image reconstruction in the presence of intrascan head motion, field drift, and tissue susceptibility changes. Results In‐vivo experimental results were obtained from healthy human subjects in resting‐state fMRI/MRSI experiments. In these experiments, the proposed method was able to simultaneously acquire metabolic and functional information from the brain in high resolution. For scans of 6.5 minutes, we achieved 3.0 × 3.0 × 1.8 mm3 spatial resolution for fMRI, 1.9 × 2.5 × 3.0 mm3 nominal spatial resolution for MRSI, and 1.9 × 1.9 × 1.8 mm3 nominal spatial resolution for quantitative susceptibility maps. Conclusion This work demonstrates the feasibility of simultaneous high‐resolution mapping of brain function and metabolism with improved spatial resolution and synergistic image reconstruction.

Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing

“…This was accomplished using the union‐of‐subspaces model as in the original SPICE method. More specifically, we represented the spatiotemporal signals of water, lipids, and metabolites, each in a very low‐dimensional subspace 22,25,30‐33 :…”

“…After the water signals were reconstructed in high‐resolution, QSM maps were calculated using an existing processing pipeline, which includes field estimation using hankel singular value decomposition, 35 background field removal by solving the Laplacian boundary value problem, 36 and solving the dipole‐inversion model incorporating anatomical spatial priors 22,37 . The spatiospectral distributions of the metabolites were reconstructed using the original SPICE processing pipeline 23‐25,30‐33 …”

Accelerated J‐resolved 1H‐MRSI with limited and sparse sampling of (‐space

“…The corresponding Nyquist sampling interval is 0.83 ms (1/1200 Hz), which severely limits the achievable readout resolution. In this work, we used a similar strategy as reported in our previous work, [34][35][36][37][38][39] to overcome this limitation. More specifically, we increased the readout size to about 60 encodings to achieve high spatial resolution; the resulting echo spacing Δt 2 was 1.2 ms, which was larger than the Nyquist interval (0.83 ms).…”

“…[28][29][30][31][32] Recently, subspace models exploiting the partial separability (PS) of high-dimensional spatiospectral signals 33 have been proposed for ultrafast MRSI using a technique known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE). [34][35][36][37][38][39] The extension of SPICE to J-resolved MRSI has also been investigated, which produced encouraging results. [40][41][42] Building on these advances, this work investigated and demonstrated the feasibility of highly accelerated J-resolved 1 H-MRSI using: (1) subspace spatiospectral models incorporating spectral prior information, (2) sparse and limited sampling of k, t 1 , t 2 -space, and (3) rapid acquisition of spatiospectral encodings in echo-planar spectroscopic imaging (EPSI) trajectories following semi-LASER excitations.…”

“…Compressed sensing further accelerates MRSI experiments by reducing the number of k‐space encodings 28‐32 . Recently, subspace models exploiting the partial separability (PS) of high‐dimensional spatiospectral signals 33 have been proposed for ultrafast MRSI using a technique known as SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) 34‐39 . The extension of SPICE to J‐resolved MRSI has also been investigated, which produced encouraging results 40‐42 …”