Abstract: The present application is directed to a system and method for creating a precision three dimensional biomap and/or digital twin representation. A representative method includes receiving, by a computing system, medical imaging data for a patient and organizing, by the computing system, the medical imaging data into hierarchical virtual space data. The method further includes creating, by the computing system, a three dimensional biomap of the patient based on the hierarchical virtual space data.

Abstract: The present disclosure is directed to a system and method for augmenting physician expert decision performance and improving healthcare system organization-level decision performance utilizing a joint data space selected from complex patient health data for advanced analytics.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A system and method for creating and using a medical imaging bioinformatics annotated (“MIBA”) master file is disclosed. Creating the MIBA master file includes receiving image data, performing a first registration on the image data for obtaining in-slice registered data, and performing a second registration for registering the in-slice registered data to a three-dimensional (3D) model for obtaining source data. Creating also includes extracting voxel data from the source data and storing the voxel data in a MIBA database, receiving selection of a volume of interest, and extracting a portion of the voxel data corresponding to the volume of interest. The MIBA master file is created from the portion of the voxel data, which is stored in the MIBA database. The MIBA system receives a query, extracts data from the MIBA master file in response to the query, and presents the extracted data on an output interface.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A system and method for creating and using a medical imaging bioinformatics annotated (“MIBA”) master file is disclosed. Creating the MIBA master file includes receiving image data, performing a first registration on the image data for obtaining in-slice registered data, and performing a second registration for registering the in-slice registered data to a three-dimensional (3D) model for obtaining source data. Creating also includes extracting voxel data from the source data and storing the voxel data in a MIBA database, receiving selection of a volume of interest, and extracting a portion of the voxel data corresponding to the volume of interest. The MIBA master file is created from the portion of the voxel data, which is stored in the MIBA database. The MIBA system receives a query, extracts data from the MIBA master file in response to the query, and presents the extracted data on an output interface.

Abstract: A method for determining MRI biomarkers for in vivo issue includes the steps of obtaining raw data concerning the in vivo tissue from a MRI machine; processing the raw data to obtain parameter maps; when applicable, registering images such that the exact same tissue at serial points can be analyzed; applying a grid over a region of interest to create sub-regions of interest (SROIs); inserting parameter measures for each SROI into a spreadsheet program to create a large 3D data matrix; applying standard big-data analytics including data mining and statistics of matrix measures to find patterns of measurement values or measure changes (which may include established biomarkers). A medical imaging software program is used to obtain the parameter maps from the raw data and place multiple grids over the SROIs. 3D matrix measures may be data mined and analyzed using standard big-data analytics.

Abstract: A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A system and method for creating and using a medical imaging bioinformatics annotated (“MIBA”) master file is disclosed. Creating the MIBA master file includes receiving image data, performing a first registration on the image data for obtaining in-slice registered data, and performing a second registration for registering the in-slice registered data to a three-dimensional (3D) model for obtaining source data. Creating also includes extracting voxel data from the source data and storing the voxel data in a MIBA database, receiving selection of a volume of interest, and extracting a portion of the voxel data corresponding to the volume of interest. The MIBA master file is created from the portion of the voxel data, which is stored in the MIBA database. The MIBA system receives a query, extracts data from the MIBA master file in response to the query, and presents the extracted data on an output interface.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A method includes obtaining image data, selecting image datasets from the image data, creating three-dimensional (3D) matrices based on the selected image dataset, refining the 3D matrices, applying one or more matrix operations to the refined 3D matrices, selecting corresponding matrix columns from the 3D matrices, applying big data convolution algorithm to the selected corresponding matrix columns to create a two-dimensional (2D) matrix, and applying a reconstruction algorithm to create a super-resolution biomarker map image.

Abstract: A method of using a moving window to form a probability map is disclosed. According to one embodiment, a method may include obtaining measures of imaging parameters for stops of a moving window on an image. Probabilities of an event associated with the stops of the moving window are obtained, for example by matching the measures of the imaging parameters to a classifier.

Abstract: A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

Abstract: A method for determining MRI biomarkers for in vivo issue includes the steps of obtaining raw data concerning the in vivo tissue from a MRI machine; processing the raw data to obtain parameter maps; when applicable, registering images such that the exact same tissue at serial points can be analyzed; applying a grid over a region of interest to create sub-regions of interest (SROIs); inserting parameter measures for each SROI into a spreadsheet program to create a large 3D data matrix; applying standard big-data analytics including data mining and statistics of matrix measures to find patterns of measurement values or measure changes (which may include established biomarkers). A medical imaging software program is used to obtain the parameter maps from the raw data and place multiple grids over the SROIs. 3D matrix measures may be data mined and analyzed using standard big-data analytics.