High‐resolution ¹H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

Abstract

Purpose To develop data acquisition and image reconstruction methods to enable high-resolution 1H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation). Theory and Methods SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging (CSI)/echo-planar spectroscopic imaging (EPSI) sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed EPSI sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high-resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization. Results Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in-plane and 4 mm through-plane resolution in about 25 min. Conclusion Special data acquisition and reconstruction methods have been developed for high-resolution 1H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of 1H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE.

Fast high‐resolution brain metabolite mapping on a clinical 3T MRI by accelerated H‐FID‐MRSI and low‐rank constrained reconstruction

“…[44][45][46] Numerous previous studies presented methodology based either on specific acquisition sequences 47,48 and/or reconstruction techniques. 15,17,25,27,49 Spatial-spectral encoding and echo planar schemes have been shown to be fast and efficient F I G U R E 1 0 Effect of k-space undersampling acceleration on metabolite distribution maps for an in vivo dataset. Reconstruction was performed with rank K = 20, optimal regularization parameter (λ = 10 −3 ) and with acceleration factors = 1, 2, 3.…”

“…The full MRSI dataset contains highly correlated measurements and is often assumed to be partially separable with a limited number of components K. 9,15,19,27 This low-rank approximation for the reconstructed MRSI data ρ reads (following notation in (2)) This space-time decomposition leads to factorization of the MRSI dataset into a finite set of characteristic time series V that are spatially distributed over the measured volume according to U. Because the MRSI dataset is assumed to contain a finite number of distinct metabolite resonance frequencies that are independent of spatial location, the lowrank approximation is suitable.…”

Simultaneous metabolic and functional imaging of the brain using SPICE

“…This trade-off between increased spatial and reduced spectral encodings is enabled by the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) subspace imaging framework. 29,30,33 To further extend k-space coverage and reduce data acquisition time, (k, t)-space is sampled sparsely in variable density. More specifically, k-space is partitioned into 3 regions, as shown in Figure 2B: a central region, a middle region, and an outer region.…”

Fast data acquisition techniques in magnetic resonance spectroscopic imaging

“…147,149 A recently developed hybrid CSI/EPSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation) employs a subspace approach to acquire high-resolution spectroscopic data with good SNR. 156,157 The SPICE technique exploits the property that lowdimensional subspaces tend to contain high-dimensional spectroscopic signals. 156 Sparse sampling of the (k, t) space is achieved using a hybrid CSI/EPSI sequence that acquires two distinct datasets, D 1 and D 2 .…”