Purpose: To develop a subspace learning method for the recently proposed subspace-based MRSI approach known as SPICE, and achieve ultrafast 1H-MRSI of the brain. Theory and Methods: A novel strategy is formulated to learn a low-dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning “empirical” distributions of molecule-specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics-based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition-specific subspace for spatiospectral encoding and processing. High-resolution MRSI acquisitions combining ultrashort-TE/short-TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method. Results: The accuracy of the learned subspace and the capability of the proposed method in producing high-resolution 3D 1H metabolite maps and high-quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing ((Formula presented.) map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters. Conclusions: The proposed method enables ultrafast 1H-MRSI of the brain using a learned subspace, eliminating the need of acquiring subject-dependent navigator data (known as (Formula presented.)) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high-resolution MRSI.
- MR spectroscopic imaging
- no water suppression
- rapid spatiospectral encoding
- subspace learning
- union-of-subspaces model
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging