Case Western Reserve University

Debra McGivney

Mark A Griswold

Dan Ma

Rasim Boyacioglu

Vikas Gulani

Chaitra Badve

Mingrui Yang

Samuel Frankel

Ananya Panda

A method for determining an undersampling error for a magnetic resonance fingerprinting (MRF) pulse sequence includes retrieving a plurality of sets of spatial response functions. Each set of spatial response functions is associated with a tissue type in a reference image and is based on a tissue mask of the reference image for each tissue type. A signal evolution for each tissue type may be generated based on, for example, the MRF pulse sequence, An undersampled image may be generated for each tissue type using the set of spatial response functions and the signal evolutions for the tissue type. At least one quantitative parameter may be determined by comparing an undersampled image series created from the undersampled images to an MRF dictionary. An undersampling error for the MRF pulse sequence may be generated by comparing a quantitative map (or maps) for the quantitative parameter (or parameters) and the reference image.

System and method for determining undersampling errors for a magnetic resonance fingerprinting pulse sequence

A system and method is provided for improved magnetic resonance fingerprinting (MRF) data dictionary matching using an MRF dictionary having entries with an inner product storing tissue properties.

System and method for improved magnetic resonance fingerprinting using inner product space

A method for characterizing a tissue in a subject using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a tissue in a subject using a magnetic resonance imaging (MRI) system, comparing the MRF data to a MRF dictionary to identify quantitative values of at least one tissue property for the MRF data, generating a quantitative map based on the quantitative values of the at least one tissue property, identifying at least one region of interest on the quantitative map, determining at least one texture feature of the at least one region of interest of the quantitative map, characterizing the tissue in the at least one region of interest based on the at least one texture feature and generating a report indicating the characterization of the tissue based in the at least one texture feature.

System and method for texture analysis in magnetic resonance fingerprinting (MRF)

A method for magnetic resonance fingerprinting (MRF), including accessing MRF data and a dictionary of signal evolutions. A plurality of regions-of-interest (ROIs) are selected in the MRF data. A first series of tissue parameter estimates is generated from the MRF data in the ROIs using the dictionary and a multicomponent Bayesian framework. From the first series of tissue parameter estimates, probability distributions are computed for different tissue types. The method further includes creating a reduced dictionary by removing entries from the dictionary having tissue parameter values not contained within the computed probability distributions. A second series of tissue parameter estimates is generated from the MRF data using the reduced dictionary and a multicomponent Bayesian framework. The method also includes generating a tissue probability map for each different tissue type from the second series of tissue parameter estimates.

System and method for visualization and segmentation of tissue using a bayesian estimation of multicomponent relaxation values in magnetic resonance fingerprinting

Systems and methods are provided for Magnetic Resonance Fingerprinting (MRF) using Independent Component Analysis (ICA) to distinguish between different tissue types. In some configurations, an MRF acquisition may be performed to be sensitive to a selected tissue property or parameter, and tissues may be grouped into separate independent components based on their time courses which may be based on the underlying tissue property.

Blind source separation in magnetic resonance fingerprinting

A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.

System and method for low rank approximation of high resolution MRF through dictionary fitting

Apparatus, methods, and other embodiments associated with 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 varying parameters may include flip angle, echo time, RF amplitude, and other parameters. 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 resonant species in the object as a result of comparing acquired signals to reference signals. The reference signals may be stored in a dictionary. Singular value decomposition may be applied to the dictionary and the acquired signals before comparing the acquired signals to the reference signals.

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Bay Village, OH, United States of America

Debra McGivney