Abstract: Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that 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 NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Abstract: Example apparatus and methods employ an artificial neural network (ANN) to automatically design magnetic resonance (MR) pulse sequences. The ANN is trained using transverse magnetization signal evolutions having arbitrary initial magnetizations. The trained up ANN may then produce an array of signal evolutions associated with a pulse sequence having user selectable pulse sequence parameters that vary in degrees of freedom associated with magnetic resonance fingerprinting (MRF). Efficient and accurate approaches are provided for predicting user controllable MR pulse sequence settings including, but not limited to, acquisition period and flip angle (FA). The acquisition period and FA may be different in different sequence blocks in the pulse sequence produced by the ANN. Predicting user controllable MR pulse sequence settings for both conventional MR and MRF facilitates achieving desired signal characteristics from a signal evolution produced in response to an automatically generated pulse sequence.

Abstract: Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that 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 NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Abstract: Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that 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 NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Abstract: Example embodiments associated with characterizing a sample using NMR fingerprinting are described. One example NMR apparatus includes an NMR logic that repetitively and variably samples a (k, t, E) space associated with an object to acquire a set of NMR signals that 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 NMR apparatus may also include a signal logic that produces an NMR signal evolution from the NMR signals and a characterization logic that characterizes a tissue in the object as a result of comparing acquired signals to reference signals. Example embodiments facilitate distinguishing diseased tissue from healthy tissue based on tissue component fractions identified using the NMR fingerprinting.

Abstract: Example apparatus and methods employ an artificial neural network (ANN) to automatically design magnetic resonance (MR) pulse sequences. The ANN is trained using transverse magnetization signal evolutions having arbitrary initial magnetizations. The trained up ANN may then produce an array of signal evolutions associated with a pulse sequence having user selectable pulse sequence parameters that vary in degrees of freedom associated with magnetic resonance fingerprinting (MRF). Efficient and accurate approaches are provided for predicting user controllable MR pulse sequence settings including, but not limited to, acquisition period and flip angle (FA). The acquisition period and FA may be different in different sequence blocks in the pulse sequence produced by the ANN. Predicting user controllable MR pulse sequence settings for both conventional MR and MRF facilitates achieving desired signal characteristics from a signal evolution produced in response to an automatically generated pulse sequence.

Abstract: Systems, methodologies, media, and other embodiments associated with improving MRI scan times and mitigating the effects of aliasing artifacts when the Nyquist data sampling threshold is not satisfied are described. One exemplary method embodiment includes producing an oscillating phase encoding gradient during a readout phase, where the gradient facilitates acquiring data from multiple phase encoding lines during a single readout phase. The exemplary method embodiment may also include reconstructing an image from the data acquired from two or more phase encoding lines.