Abstract: 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.

Abstract: The present application provides an automated system and method for improving and generating annotated magnetic resonance (MRI) images based on magnetic resonance fingerprinting (MRF) data. The present disclosure also provides an automated method for generating the automated system for improving annotated MRI images. In some aspects, the method comprises accessing MRF data, MRI data, and images annotated with bulk-pixel labels that identify a tissue class for a group of patients. The annotated images can be used to train a machine learning system based on MRF data. The system can be trained to assign pixel labels to pixels outside the bulk-pixel labels to create an automated system. The automated system provided may be used to determine disease states using MRF data and generate machine-annotated images that include labels that indicate a tissue class. In this way, the present disclosure provides an automated system and method for improving incomplete annotations.

Abstract: A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.

Abstract: 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.

Abstract: A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.

Abstract: 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.

Abstract: 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.