Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: In an approach for smart test data workload generation, a processor receives a plurality of expected image frames for a user interface application to be tested. The plurality of expected image frames is pre-defined and represents a series of workflows and operations of the user interface application to be expected based on a design requirement. A processor calculates a first set of hash-values for each corresponding expected image frame. A processor samples the user interface application with a frequency to a plurality of testing image frames during a test run on the user interface application. A processor calculates a second set of hash-values for each sampled testing image frame. A processor compares the first set of hash-values to the second set of hash-values. A processor verifies that the second set of hash-values matches the first set of hash-values.

Abstract: Described are techniques for predictive microservice activation. The techniques include training a machine learning model using a plurality of sequences of coordinates, where the plurality of sequences of coordinates are respectively based upon a corresponding plurality of series of vectors generated from historical usage data for an application and its associated microservices. The techniques further include inputting a new sequence of coordinates representing a series of application operations to the machine learning model. The techniques further include identifying a predicted microservice for future utilization based on an output vector generated by the machine learning model. The techniques further include activating the predicted microservice prior to the predicted microservice being called by the application.

Abstract: A method for workflow organization can be provided to manage digital conversations. Provisions for such method may be implemented in a system configured for contextually organizing digital conversations and viewing associated content. For each user, a tree of conversations which is specific to the user is constructed, while preserving context shared between users. The workflow management preferably includes provisions for appointments and attachments.

Abstract: A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Abstract: A method for workflow organization can be provided to manage digital conversations. Provisions for such method may be implemented in a system configured for contextually organizing digital conversations and viewing associated content. For each user, a tree of conversations which is specific to the user is constructed, while preserving context shared between users. The workflow management preferably includes provisions for appointments and attachments.

Abstract: The present approaches relates to the use of silicon-based energy-discriminating, photon-counting detectors, such as for use in X-ray based imaging including computed tomography. The described approaches address the resolution and classification of X-ray photons affected by Compton scatter, which may be detected as having energy levels below their proper level due to collision or deflection events.

Abstract: A device to sense degree of twist in a screwdriver and the magnitude of force applied includes a contacting portion, a deformation portion, a sensing portion, and a processor. The contacting portion receives a grip and twist pressure through the deformation portion which is tiltedly fixed to the contacting portion. The deformation portion carries the sensing portion and elastically deforms under the force and/or the torsion. The sensing portion detects a deformation of the deformation portion and generates a corresponding electrical signal. The processor receives the signal and determines the values of the twist caused and the force applied.

Abstract: The present approach relates to the use of detector elements (i.e., reference detector pixels) positioned under septa of an anti-scatter collimator. Signals detected by the reference detector pixels may be used to correct for charging-sharing events with adjacent pixels and/or to characterize or correct for focal spot misalignment either in real time or as a calibration step.

Abstract: A method is provided that includes acquiring initial PET imaging data. The method also includes acquiring CT imaging data. Further, the method includes training a deep learning model for PET image reconstruction using the initial PET imaging data and the CT imaging data.

Abstract: A device to sense degree of twist in a screwdriver and the magnitude of force applied includes a contacting portion, a deformation portion, a sensing portion, and a processor. The contacting portion receives a grip and twist pressure through the deformation portion which is tiltedly fixed to the contacting portion. The deformation portion carries the sensing portion and elastically deforms under the force and/or the torsion. The sensing portion detects a deformation of the deformation portion and generates a corresponding electrical signal. The processor receives the signal and determines the values of the twist caused and the force applied.

Abstract: A method for imaging an object to be reconstructed includes acquiring projection data corresponding to the object. Furthermore, the method includes generating a measured sinogram based on the acquired projection data and formulating a forward model, where the forward model is representative of a characteristic of the imaging system. In addition, the method includes generating an estimated sinogram based on an estimated image of the object and the forward model and formulating a statistical model based on at least one of pile-up characteristics and dead time characteristics of a detector of the imaging system. Moreover, the method includes determining an update corresponding to the estimated image based on the statistical model, the measured sinogram, and the estimated sinogram and updating the estimated image based on the determined update to generate an updated image of the object. Additionally, the method includes outputting a final image of the object.

Abstract: A method for imaging an object to be reconstructed includes acquiring projection data corresponding to the object. Furthermore, the method includes generating a measured sinogram based on the acquired projection data and formulating a forward model, where the forward model is representative of a characteristic of the imaging system. In addition, the method includes generating an estimated sinogram based on an estimated image of the object and the forward model and formulating a statistical model based on at least one of pile-up characteristics and dead time characteristics of a detector of the imaging system. Moreover, the method includes determining an update corresponding to the estimated image based on the statistical model, the measured sinogram, and the estimated sinogram and updating the estimated image based on the determined update to generate an updated image of the object. Additionally, the method includes outputting a final image of the object.

Abstract: A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Abstract: The present approach relates to scatter correction of signals acquired using radiation detectors on a pixel-by-pixel basis. In certain implementations, the systems and methods disclosed herein facilitate scatter correction for signals generated using a detector having segmented detector elements, such as may be present in an energy-resolving, photon-counting CT imaging system.

Abstract: A signal processing method is disclosed, which includes detecting a total intensity of X-rays passing through an object comprising multiple materials; obtaining at least one set of basis information of basis material information of the multiple materials and basis component information of photon-electric absorption basis component and Compton scattering basis component of the object; estimating a scatter intensity component of the detected X-rays based on the at least one set of basis information and the detected total intensity; and obtaining an intensity estimate of primary X-rays incident on a detector based on the detected total intensity and the estimated scatter intensity component. An imaging system adopting the above signal processing method is also disclosed.

Abstract: The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.

Abstract: A method is provided that includes acquiring initial PET imaging data. The method also includes acquiring CT imaging data. Further, the method includes training a deep learning model for PET image reconstruction using the initial PET imaging data and the CT imaging data.

Abstract: The present approaches relates to the use of silicon-based energy-discriminating, photon-counting detectors, such as for use in X-ray based imaging including computed tomography. The described approaches address the resolution and classification of X-ray photons affected by Compton scatter, which may be detected as having energy levels below their proper level due to collision or deflection events.