Abstract: Apparatus, methods, and other embodiments associated with NMR fingerprinting are described. One example NMR apparatus includes an NMR logic configured to repetitively and variably sample a (k, t, E) space associated with an object to acquire a set of NMR signals. Members of the set of NMR signals are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The varying parameters may include flip angle, echo time, RF amplitude, and other parameters. The NMR apparatus may also include a signal logic configured to produce an NMR signal evolution from the NMR signals, a matching logic configured to compare a signal evolution to a known, simulated or predicted signal evolution, and a characterization logic configured to characterize a resonant species in the object as a result of the signal evolution comparisons.

Abstract: Systems, methods, and other embodiments associated with removing motion artifacts from MR images are described. One example method includes controlling an MRI apparatus to acquire a fully sampled, centric-ordered, non-interleaved, data set from an object to be imaged and controlling a Generalized Auto-Calibrating Partially Parallel Acquisition (GRAPPA) logic to produce a GRAPPA duplicate of a single partition through the data set. The method also includes computing, from the GRAPPA duplicate, a GRAPPA navigator for a phase encoding (PE) line in the single partition and computing an error between the PE line in the single partition and a corresponding PE line in the GRAPPA duplicate using the GRAPPA navigator. The method also includes selectively replacing data in the PE line in the single partition with replacement data upon determining that the error exceeds a threshold. The method may include reconstructing an MR image based, at least in part, on the single partition.

Abstract: Example apparatus and methods control a magnetic resonance imaging (MRI) apparatus to acquire, from an object to be imaged, throughout a period of time, a partitioned non-Cartesian fully-sampled calibration data set. Different groups of lines in the calibration data set are acquired at different points in time under different gradient encoding conditions that yield phase encoding in the direction perpendicular to the non-Cartesian encoded plane. The MRI apparatus is controlled to acquire an under-sampled non-Cartesian data set from the object to be imaged and to reconstruct an image from the under-sampled data set based, at least in part, on a through-time GRAPPA calibration. A GRAPPA weight set can be computed from data in different groups of lines in the calibration data set because different groups of lines can be treated as unique calibration time frames due to phase encoding produced by the different gradient encoding conditions.

Abstract: Apparatus, methods, and other embodiments associated with magnetic resonance (MR) trajectory correcting using GRAPPA operator gridding (GROG) are described. One example method includes identifying an on angle or regular portion of a projection in an MR trajectory and then computing base GROG weights for that portion. The example method includes identifying a shift direction and a shift amount for the projection. The shift direction is configured to shift the projection towards a desired point in k-space and the shift amount is configured to shift the projection by a desired amount in the shift direction. With a shift direction and amount available, the example method corrects for a gradient delay by manipulating the MR source signal data using the shift direction and the shift amount. In one embodiment, a gradient delay can be determined and used to calibrate an MRI apparatus.

Abstract: Apparatus, methods, and other embodiments associated with multi-scale orthogonal matching pursuit (OMP) for magnetic resonance imaging (MRI) relaxometry are described. One example method includes controlling a nuclear magnetic resonance (NMR) apparatus to cause selected nuclei in an item to resonate by applying radio frequency (RF) energy to the item and then acquiring multiple series of magnetic resonance (MR) images of the item, the series of MR images having different scales. The example method includes controlling the NMR apparatus to produce a combined signal evolution from a first signal evolution associated with a first series of MR images and a second signal evolution associated with a second series of MR images and to characterize relaxation of the selected nuclei in the item as a function of an OMP that compares the combined signal evolution to a set of combined comparative signal evolutions.

Abstract: Example systems and methods control a parallel magnetic resonance imaging (pMRI) apparatus to acquire non-Cartesian (e.g., spiral) calibration data sets throughout time. Example systems and methods also control the pMRI apparatus to acquire an under-sampled non-Cartesian data set from the object to be imaged. Example systems and methods then control the pMRI apparatus to reconstruct an image of the object to be imaged from the under-sampled non-Cartesian data set. The reconstruction depends, at least in part, on a through-time non-Cartesian GRAPPA calibration where a value for a point missing from k-space in the under-sampled non-Cartesian data set is computed using a GRAPPA weight set calibrated and applied for the missing point. The GRAPPA weight set is computed from data in the non-Cartesian calibration data sets.

Abstract: Apparatus, methods, and other embodiments associated with combined correlation parameter estimation are described. One example method includes accessing data associated with a magnetic resonance (MR) signal produced by relaxation of nuclei in an item that has experienced nuclear magnetic resonance (NMR) excitation. The MR signal is a function of two or more NMR parameters. The example method also includes accessing data associated with a set of comparative signal evolutions and computing a value for an NMR parameter based on a combined correlation of the data associated with the MR signal to the data associated with the set of comparative signal evolutions. The combined correlation will depend on at least two correlations between the data associated with the MR signal and two different members of the set of comparative signal evolutions.

Abstract: Apparatus, methods, and other embodiments associated with multi-scale orthogonal matching pursuit (OMP) for magnetic resonance imaging (MRI) relaxometry are described. One example method includes controlling a nuclear magnetic resonance (NMR) apparatus to cause selected nuclei in an item to resonate by applying radio frequency (RF) energy to the item and then acquiring multiple series of magnetic resonance (MR) images of the item, the series of MR images having different scales. The example method includes controlling the NMR apparatus to produce a combined signal evolution from a first signal evolution associated with a first series of MR images and a second signal evolution associated with a second series of MR images and to characterize relaxation of the selected nuclei in the item as a function of an OMP that compares the combined signal evolution to a set of combined comparative signal evolutions.

Abstract: Apparatus, methods, and other embodiments associated with NMR fingerprinting are described. One example NMR apparatus includes an NMR logic configured to repetitively and variably sample a (k, t, E) space associated with an object to acquire a set of NMR signals. Members of the set of NMR signals are associated with different points in the (k, t, E) space. Sampling is performed with t and/or E varying in a non-constant way. The varying parameters may include flip angle, echo time, RF amplitude, and other parameters. The NMR apparatus may also include a signal logic configured to produce an NMR signal evolution from the NMR signals, a matching logic configured to compare a signal evolution to a known, simulated or predicted signal evolution, and a characterization logic configured to characterize a resonant species in the object as a result of the signal evolution comparisons.

Abstract: Systems, methods, and other embodiments associated with removing motion artifacts from MR images are described. One example method includes controlling an MRI apparatus to acquire a fully sampled, centric-ordered, non-interleaved, data set from an object to be imaged and controlling a Generalized Auto-Calibrating Partially Parallel Acquisition (GRAPPA) logic to produce a GRAPPA duplicate of a single partition through the data set. The method also includes computing, from the GRAPPA duplicate, a GRAPPA navigator for a phase encoding (PE) line in the single partition and computing an error between the PE line in the single partition and a corresponding PE line in the GRAPPA duplicate using the GRAPPA navigator. The method also includes selectively replacing data in the PE line in the single partition with replacement data upon determining that the error exceeds a threshold. The method may include reconstructing an MR image based, at least in part, on the single partition.

Abstract: Example systems and methods control a parallel magnetic resonance imaging (pMRI) apparatus to acquire non-Cartesian (e.g., spiral) calibration data sets throughout time. Example systems and methods also control the pMRI apparatus to acquire an under-sampled non-Cartesian data set from the object to be imaged. Example systems and methods then control the pMRI apparatus to reconstruct an image of the object to be imaged from the under-sampled non-Cartesian data set. The reconstruction depends, at least in part, on a through-time non-Cartesian GRAPPA calibration where a value for a point missing from k-space in the under-sampled non-Cartesian data set is computed using a GRAPPA weight set calibrated and applied for the missing point. The GRAPPA weight set is computed from data in the non-Cartesian calibration data sets.

Abstract: Example systems and methods control a parallel magnetic resonance imaging (pMRI) apparatus to acquire radial calibration data sets throughout time. Example systems and methods also control the pMRI apparatus to acquire an under-sampled radial data set from the object to be imaged. Example systems and methods then control the pMRI apparatus to reconstruct an image of the object to be imaged from the under-sampled radial data set. The reconstruction depends, at least in part, on a through-time radial GRAPPA calibration where a value for a point missing from k-space in the under-sampled radial data set is computed using a GRAPPA weight set calibrated and applied for the missing point. The GRAPPA weight set is computed from data in the radial calibration data sets.

Abstract: Systems, methods, apparatus, and other embodiments associated with reducing imaging acquisition time are described. One example method includes accessing an under-sampled data set and a library of previously acquired data sets. The method includes producing an approximation of the under-sampled data set by transforming data stored in the library. The method includes producing a sparsified data set from the approximation and the under-sampled data set and then reconstructing the sparsified data set into a sparse image using a reconstruction technique configured to reconstruct sparse data. The method includes producing a fully-sampled approximation of the under-sampled data set and producing a final reconstructed image from the sparse image and the fully sampled approximation.