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: Automated processing and radiomic analysis of magnetic resonance imaging (“MRI”), such as multi-contrast MR images, and magnetic resonance fingerprinting (“MRF”) data, such as quantitative parameter maps, are integrated into a single workflow.

Abstract: Radiomic analysis of multiparametric magnetic resonance imaging (“MRI”) and magnetic resonance fingerprinting (“MRF”) data enhances delineation and mapping of tumor regions. Radiomic features are extracted from MRI and MRF tumor images. Distinct tumor regions, including but not limited to necrotic core, enhancing tumor, and peritumoral white matter, are segmented and mapped. Whole tumor as well as tumor region characteristics are evaluated. Tumors can also be differentiated and classified by pathology, grading, staging, and so on.

Abstract: 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.

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 prostate cancer tissue from normal peripheral zone tissue based on quantitative data acquired using NMR fingerprinting in combination with apparent diffusion co-efficient (ADC) values or perfusion values acquired using DWI-MRI or DCE-MRI.

Abstract: 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 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 NMR signal evolution may be assigned to a cluster based on the characterization of the resonant species. Cluster overlay maps may be produced simultaneously based, at least in part, on the clustering. The clusters may be associated with different tissue types.

Abstract: 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.

Abstract: 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 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 NMR signal evolution may be assigned to a cluster based on the characterization of the resonant species. Cluster overlay maps may be produced simultaneously based, at least in part, on the clustering. The clusters may be associated with different tissue types.

Abstract: 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 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 NMR signal evolution may be assigned to a cluster based on the characterization of the resonant species. Cluster overlay maps may be produced simultaneously based, at least in part, on the clustering. The clusters may be associated with different tissue types.

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 prostate cancer tissue from normal peripheral zone tissue based on quantitative data acquired using NMR fingerprinting in combination with apparent diffusion co-efficient (ADC) values or perfusion values acquired using DWI-MRI or DCE-MRI.

Abstract: 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 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 NMR signal evolution may be assigned to a cluster based on the characterization of the resonant species. Cluster overlay maps may be produced simultaneously based, at least in part, on the clustering. The clusters may be associated with different tissue types.