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.

Abstract: A system and method are provided for designing RF pulses which have improved magnetization profiles. By utilizing an optimal control approach as an alternative to, or in combination with, non-iterative approximations, RF pulses generated by the system and method described herein will exhibit less deviation from that of “ideal” Bloch solutions. Consequently, the magnetization profiles produced by the RF pulses generated by the system and method described herein will be closer to the desired profiles. In addition, limitations of non-iterative approximations, such as maximum tip angle limits and linearity constraints, can be avoided.

Abstract: Variable-density (VD), sequentially-interleaved sampling of k-space coupled with the acquisition of reference frames of data is carried out to improve spatiotemporal resolution, image quality, and signal-to-noise ratio (SNR) of dynamic images. In one example, ktSENSE is implemented with a non-static regularization image, such as that provided by RIGR or similar technique, to acquire and reconstruct dynamic images. The integration of ktSENSE and RIGR, for example, provides dynamic images with higher spatiotemporal resolution and lower image artifacts compared to dynamic images acquired and reconstructed using ktSENSE alone.

Abstract: A system and method are provided for designing RF pulses for multi-channel and/or multi-dimensional spatially-selective applications using a linear approximation. Embodiments of the system and method may use a generalized linear-class large tip angle approximation to design RF pulses for multi-channel and parallel transmission. Further, some of these approximations allow for the design of arbitrarily large flip angles, irregularly-shaped flip angle profiles, or arbitrary initial magnetization values. Embodiments of the system and method may also provide for the design of k-space trajectories which aid in maintaining assumptions of the various linear class approximations.

Abstract: A system and method are provided for designing RF pulses which have improved magnetization profiles. By utilizing an optimal control approach as an alternative to, or in combination with, non-iterative approximations, RF pulses generated by the system and method described herein will exhibit less deviation from that of “ideal” Bloch solutions. Consequently, the magnetization profiles produced by the RF pulses generated by the system and method described herein will be closer to the desired profiles. In addition, limitations of non-iterative approximations, such as maximum tip angle limits and linearity constraints, can be avoided.

Abstract: Generalized series-based image reconstruction as used in dynamic imaging for high-speed imaging with limited k-space coverage for each time frame. Further, in acquiring low resolution data for a plurality of image frames, a full k-space data set is generated for each time frame with the measured low-resolution data and high spatial frequency data generated by the GS model constructed based on the high-resolution image(s). The algorithms of the invention have computational complexity of O(N log N) and arc capable of producing high-resolution dynamic images with a small number of Fourier transform samples.

Abstract: Generalized series-based image reconstruction as used in dynamic imaging for high-speed imaging with limited k-space coverage for each time frame. Further, in acquiring low resolution data for a plurality of image frames, a full k-space data set is generated for each time frame with the measured low-resolution data and high spatial frequency data generated by the GS model constructed based on the high-resolution image(s). The algorithms of the invention have computational complexity of O(N log N) and are capable of producing high-resolution dynamic images with a small number of Fourier transform samples.

Abstract: Described here, is a method for obtaining high-resolution snap-shot images of moving objects in MR imaging applications through the elimination of ghosting and other image artifacts by estimating motion frequency data, estimating amplitude data for the motion frequency data, interpolating the motion frequency data and the amplitude data to generate snap-shot data frames, and generating snapshot images of each data frame.

Abstract: An imaging apparatus and method which provides for reconstruction of images from sampled data with improved resolution, improved signal-to-noise, optimal edge detection, and pattern recognition. The apparatus and method are characterized by the use of a priori information (constraints) and a high pass filter on the data to obtain superresolution reconstruction of image data. An object function is approximated by a series of known model functions such as box car and polynomial functions. Solution of the object function depends on a finite number of unknowns whereupon the model parameters can be perfectly resolved to obtain infinite or superresolution. The apparatus and method have particular application in nuclear magnetic resonance imaging as well as other imaging modalities.