Abstract: Methods, apparatus, and storage medium for obtaining high-resolution mass spectra and chemical maps from a sample using a subspace Fourier transform mass spectrometry (FT-MS) approach are described. The method includes conducting a first set of image data corresponding to a first group of spatial positions on the sample and a second set of image data corresponding to a second group of spatial positions on the sample; conducting a decomposition process on the first set of image data to obtain a set of basis elements; performing a reconstruction process on a second set of image data to obtain a set of reconstructed image data; performing a Fourier transform on the first and second sets of image data to obtain a first and second sets of mass spectra, respectively; and obtaining a FT-MS image for the sample based on the first set of mass spectra and the second set of mass spectra.

Abstract: A new method is developed for ultrafast, high-resolution magnetic resonance spectroscopic imaging (MRSI) using learned spectral features. The method uses Free Induction Decay (FID) based ultrashort-TE and short-TR acquisition without any solvent suppression pulses to generate the desired spatiospectral encodings. The spectral features for the desired molecules are learned from specifically designed “training” data by taking into account the resonance structure of each compound generated by quantum mechanical simulations. A union-of-subspaces model that incorporates the learned spectral features is used to effectively separate the unsuppressed water/lipid signals, the metabolite signals, and the macromolecule signals. The unsuppressed water spectroscopic signals in the data can be used for various purposes, e.g., removing the need of additional auxiliary scans for calibration, and for generating high quality quantitative tissue susceptiability mapping etc.

Abstract: A new method is developed for ultrafast, high-resolution magnetic resonance spectroscopic imaging (MRSI) using learned spectral features. The method uses Free Induction Decay (FID) based ultrashort-TE and short-TR acquisition without any solvent suppression pulses to generate the desired spatiospectral encodings. The spectral features for the desired molecules are learned from specifically designed “training” data by taking into account the resonance structure of each compound generated by quantum mechanical simulations. A union-of-subspaces model that incorporates the learned spectral features is used to effectively separate the unsuppressed water/lipid signals, the metabolite signals, and the macromolecule signals. The unsuppressed water spectroscopic signals in the data can be used for various purposes, e.g., removing the need of additional auxiliary scans for calibration, and for generating high quality quantitative tissue susceptiability mapping etc.

Abstract: Various embodiments accelerate high-resolution magnetic resonance spectroscopic imaging (MRSI). Various embodiments are built on a low-dimensional subspace model exploiting the partial separability of high-dimensional MRSI signals. For two and three dimensional MRSI with one spectral dimension, various embodiments sparsely sample the corresponding (k,t)-space in two complementary data sets, one with dense temporal sampling and high signal-to-noise ratio but limited k-space coverage and the other with sparse temporal sampling but extended k-space coverage. The reconstruction is then done by estimating a set of temporal/spectral basis functions and the corresponding spatial coefficients from these two data sets. The imaging technique of various embodiments can be used for high-resolution MRSI of different nuclei.

Abstract: A new method is developed to accelerate high-resolution magnetic resonance spectroscopic imaging (MRSI). The method is built on a low-dimensional subspace model exploiting the partial separability of high-dimensional MRSI signals and uses this subspace model for data acquisition, processing, and image reconstruction. Specifically for two and three dimensional MRSI with one spectral dimension, this method sparsely samples the corresponding (k,t)-space in two complementary data sets, one with dense temporal sampling and high signal-to-noise ratio but limited k-space coverage and the other with sparse temporal sampling but extended k-space coverage. The reconstruction is then done by estimating a set of temporal/spectral basis functions and the corresponding spatial coefficients from these two data sets. The proposed subspace model can be further extended to incorporate multiple signal components for nuisance signal removal in 1H-MRSI and more generalized reconstruction methods.