Abstract: A method to culling parts of a 3D reconstruction volume is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data with low usage of computational resources and storage spaces. The method includes culling parts of the 3D reconstruction volume against a depth image. The depth image has a plurality of pixels, each of which represents a distance to a surface in a scene. In some embodiments, the method includes culling parts of the 3D reconstruction volume against a frustum. The frustum is derived from a field of view of an image sensor, from which image data to create the 3D reconstruction is obtained.

Abstract: A method of operating a computing system to generate a model of an environment represented by a mesh is provided. The method allows to update 3D meshes to client applications in real time with low latency to support on the fly environment changes. The method provides 3D meshes adaptive to different levels of simplification requested by various client applications. The method provides local update, for example, updating the mesh parts that are changed since last update. The method also provides 3D meshes with planarized surfaces to support robust physics simulations. The method includes segmenting a 3D mesh into mesh blocks. The method also includes performing a multi-stage simplification on selected mesh blocks. The multi-stage simplification includes a pre-simplification operation, a planarization operation, and a post-simplification operation.

Abstract: A method to culling parts of a 3D reconstruction volume is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data with low usage of computational resources and storage spaces. The method includes culling parts of the 3D reconstruction volume against a depth image. The depth image has a plurality of pixels, each of which represents a distance to a surface in a scene. In some embodiments, the method includes culling parts of the 3D reconstruction volume against a frustum. The frustum is derived from a field of view of an image sensor, from which image data to create the 3D reconstruction is obtained.

Abstract: A method to efficiently update and manage outputs of real time or offline 3D reconstruction and scanning in a mobile device having limited resource and connection to the Internet is provided. The method makes available to a wide variety of mobile XR applications fresh, accurate and comprehensive 3D reconstruction data, in either single user applications or multi-user applications sharing and updating the same 3D reconstruction data. The method includes a block-based 3D data representation that allows local update and maintains neighbor consistency at the same time, and a multi-layer caching mechanism that retrieves, prefetches, and stores 3D data efficiently for XR applications.

Abstract: An augmented reality/mixed reality system that provides a more immersive user experience. That experience is provided with increased speed of update for occlusion data by using depth sensor data augmented with lower-level reconstruction data. When operating in real-time dynamic environments, changes in the physical world can be reflected quickly in the occlusion data. Occlusion rendering using live depth data augmented with lower-level 3D reconstruction data, such as a raycast point cloud, can greatly reduce the latency for visual occlusion processing. Generating occlusion data in this way may provide faster operation of an XR system using less computing resources and enabling the system to be packaged in a battery operated wearable device.

Abstract: A method of assessing stenosis severity for a patient includes generating a three dimensional model of a lesion specific vessel tree of the patient. A plurality of inlet and outlet positions are identified in the lesion tree. A total flow rate from the vessel tree model is estimated. A processor and task specific software are utilized to perform computational fluid dynamic simulation on the vessel tree. A flow rate and apparent flow resistance for each of the outlets is then determined. At least one ideal model is generated. A computational fluid dynamic simulation is performed on the at least one ideal model. A level of stenosis severity is determined for each of the outlets.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: A method of assessing stenosis severity for a patient includes generating a three dimensional model of a lesion specific vessel tree of the patient. A plurality of inlet and outlet positions are identified in the lesion tree. A total flow rate from the vessel tree model is estimated. A processor and task specific software are utilized to perform computational fluid dynamic simulation on the vessel tree. A flow rate and apparent flow resistance for each of the outlets is then determined. At least one ideal model is generated. A computational fluid dynamic simulation is performed on the at least one ideal model. A level of stenosis severity is determined for each of the outlets.

Abstract: A method of assessing stenosis severity for a patient includes obtaining patient information relevant to assessing severity of a stenosis, including anatomical imaging data of the patient. Based on the anatomical imaging data, the existence of any lesions of concerns may be identified. A three dimensional image can be generated of any irregular shaped lesion of concern and a surrounding area from the patient anatomical imaging data. A plurality of comparative two dimensional lesion specific models may be created that have conditions that correspond to the three dimensional model. The comparative two dimensional models may represent vessels having regular shaped lesions with each of the comparative two dimensional models represents a different stenosis severity. The three dimensional model can then be mapped to one of the plurality of comparative two dimensional models. After this mapping, a diagnosis of whether the patient has coronary artery disease may be made.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: A method of assessing stenosis severity for a patient includes obtaining patient information relevant to assessing severity of a stenosis, including anatomical imaging data of the patient. Based on the anatomical imaging data, the existence of any lesions of concerns may be identified. A three dimensional image can be generated of any irregular shaped lesion of concern and a surrounding area from the patient anatomical imaging data. A plurality of comparative two dimensional lesion specific models may be created that have conditions that correspond to the three dimensional model. The comparative two dimensional models may represent vessels having regular shaped lesions with each of the comparative two dimensional models represents a different stenosis severity. The three dimensional model can then be mapped to one of the plurality of comparative two dimensional models. After this mapping, a diagnosis of whether the patient has coronary artery disease may be made.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.

Abstract: Systems and methods are disclosed for assessing the quality of medical images of at least a portion of a patient's anatomy, using a computer system. One method includes receiving one or more images of at least a portion of the patient's anatomy; determining, using a processor of the computer system, one or more image properties of the received images; performing, using a processor of the computer system, anatomic localization or modeling of at least a portion of the patient's anatomy based on the received images; obtaining an identification of one or more image characteristics associated with an anatomic feature of the patient's anatomy based on the anatomic localization or modeling; and calculating, using a processor of the computer system, an image quality score based on the one or more image properties and the one or more image characteristics.