Abstract: Systems and methods augmenting ultrasound simulations using photon counting computed tomography (CT) data in order to create simulated ultrasound data with fine tissue detail. A virtual volume is generated from acoustic properties derived from the photon counting CT data. Simulated ultrasound data is generated by placing a simulated transducer at various positions in the virtual volume.

Abstract: Systems and methods for left atrial appendage closure device specification from two dimensional (2D) Intracardiac Echo imaging (ICE). In an imaging procedure, 2D ICE is used to acquire images of the left atrium of a patient. A model of the left atrium is generated from the images of the left atrium. The model of the left atrium is used to validate subsequent imaging of the left atrial appendage of the patient. Anatomical measurements from the validated subsequent imaging may be used to select or specify a left atrial appendage closure device in order to, for example, treat atrial fibrillation patients.

Abstract: Systems and methods for generating an updated segmentation of an initial segmentation are provided. An initial segmentation of an anatomical object from an input medical image is received. User input modifying the initial segmentation is received. An updated segmentation of the anatomical object in the input medical image is generated using a machine learning based network based on at least one of the initial segmentation and the user input. The updated segmentation is output.

Abstract: Systems and methods for three dimensional reconstruction from two dimensional imaging data. An neural implicit shape function model is used to regress a shape of an organ from two dimensional sparse imaging data. Contours of the organ are generated from the two dimensional sparse imaging data. The contours are used to train the neural implicit shape function model to regress the shape of the organ.

Abstract: Systems and methods augmenting ultrasound simulations using photon counting computed tomography (CT) data in order to create simulated ultrasound data with fine tissue detail. A virtual volume is generated from acoustic properties derived from the photon counting CT data. Simulated ultrasound data is generated by placing a simulated transducer at various positions in the virtual volume.

Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Abstract: A medical knowledge base in a digital, clinical system is upgraded. A storage with a knowledge base, being a SNOMED knowledge base, is provided in a web ontology format. Procedural data, representing clinical procedures for evaluation of a patient's health state, is received. The received procedural data is mapped in a set of SNOMED expressions. The SNOMED expressions are converted into statements in the web ontology format. The SNOMED knowledge base is upgraded with the received procedural data by adding the statements in the SNOMED knowledge base for providing a processable file with an upgraded version of the SNOMED knowledge base.

Abstract: The present disclosure relates to a magnetic tire protection system incorporating one or a plurality of magnets arranged internally within a tire such that the magnetic field produced by each respective magnet repels magnetic objects within the road or operating environment away from the tire. The magnetic tire protection system may incorporate electromagnets with corresponding power sources to selectively produce the magnetic field produced by each respective electromagnet when it near a surface on which a tire is operating.

Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Abstract: A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest.

Abstract: Systems and methods for generating an updated segmentation of an initial segmentation are provided. An initial segmentation of an anatomical object from an input medical image is received. User input modifying the initial segmentation is received. An updated segmentation of the anatomical object in the input medical image is generated using a machine learning based network based on at least one of the initial segmentation and the user input. The updated segmentation is output.

Abstract: A medical knowledge base in a digital, clinical system is upgraded. A storage with a knowledge base, being a SNOMED knowledge base, is provided in a web ontology format. Procedural data, representing clinical procedures for evaluation of a patient's health state, is received. The received procedural data is mapped in a set of SNOMED expressions. The SNOMED expressions are converted into statements in the web ontology format. The SNOMED knowledge base is upgraded with the received procedural data by adding the statements in the SNOMED knowledge base for providing a processable file with an upgraded version of the SNOMED knowledge base.

Abstract: A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Abstract: A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest.

Abstract: Described herein, in a preferred embodiment, is a leonardite-based polyurethane resin binder that may be used, among other applications, as a binder in combination with foundry aggregate, e.g., sand, for molding or casting metal parts. The binders described herein comprise a humic substance, preferably leonardite, first mixed with a foundry aggregate and then the humic-aggregate mixture is combined with a polymerizable polyol, an isocyanate, and a polymerization catalyst to make a polyurethane resin binder in situ in a foundry aggregate, such as sand. The lignite is added as a solid to the foundry aggregate, and improves the binder performance of the lignite-containing part of the binder components.

Abstract: Described herein, in a preferred embodiment, is a leonardite-based polyurethane resin binder that may be used, among other applications, as a binder in combination with foundry aggregate, e.g., sand, for molding or casting metal parts. The binders described herein comprise a humic substance, preferably leonardite, first mixed with a foundry aggregate and then the humic-aggregate mixture is combined with a polymerizable polyol, an isocyanate, and a polymerization catalyst to make a polyurethane resin binder in situ in a foundry aggregate, such as sand. The lignite is added as a solid to the foundry aggregate, and improves the binder performance of the lignite-containing part of the binder components.

Abstract: Described herein, in a preferred embodiment, is a leonardite-based polyurethane resin binder that may be used, among other applications, as a binder in combination with foundry aggregate, e.g., sand, for molding or casting metal parts. The binders described herein comprise a humic substance, preferably leonardite, first mixed with a foundry aggregate and then the humic-aggregate mixture is combined with a polymerizable polyol, an isocyanate, and a polymerization catalyst to make a polyurethane resin binder in situ in a foundry aggregate, such as sand. The lignite is added as a solid to the foundry aggregate, and improves the binder performance of the lignite-containing part of the binder components.

Abstract: Various embodiments of a computer-implemented method, system and computer program product monitor the performance of a program component executing in a virtual machine. Breakpoints associated with position indicators within the program component are set. In response to reaching one of the breakpoints, an amount of time consumed between the breakpoint and a previous breakpoint is determined. The amount of time associated with the position indicators is accumulated. The amount of time is associated with a position indicator that is associated with the previous breakpoint.

Abstract: Various embodiments of a computer-implemented method, system and computer program product determine degradation of an application program. At least one execution state of the application program, executing in a virtual machine, is sampled. A number of occurrences of the at least one execution state are counted to provide at least one count. The at least one execution state and at least one count, respectively, are stored.