Abstract: Exemplary embodiments may provide for the switching of glucose prediction models responsive to certain conditions. For example, glucose prediction models may be switched responsive to a detected crashing glucose level condition. Exemplary embodiments also may dynamically customize parameters, such as coefficient values, of the glucose prediction model to a user. The exemplary embodiments may customize the glucose prediction model based on the history of glucose levels of the user and the history of insulin deliveries to the user. The exemplary embodiments may determine a set of parameters that provides an improved fit of the parameters to the history of glucose levels and insulin deliveries of the user. The improved fit parameters may be used to adapt the parameter set to the most recent run.

Abstract: The exemplary embodiments may dynamically adjust an Insulin on Board (JOB) profile for a user of a medicament delivery system to customize the IOB profile to the user based on recent glucose level history and insulin deliveries rather than use the conventional static IOB profiles that are based on population averages. The exemplary embodiments may calculate a Duration of Insulin Action (DIA) for a user from the customized insulin decay curves. The exemplary embodiments may generate and send notifications to the user to help reduce the DIA of the user and/or to keep the DIA of the user from rising. The exemplary embodiments may aim to not overly constrain insulin delivery due to the contribution of insulin boluses to JOB.

Abstract: A computer-implemented method for generating a motion corrected image is provided. The method includes receiving listmode data collected by an imaging system; producing two or more histo-image frames or two or more histo-projection frames based on the listmode data; providing the two or more histo-image frames or two or more histo-projection frames to an Artificial Intelligence (AI) system; receiving two or more AI reconstructed images from the AI system based on the two or more histo-image frames or the two or more histo-projection frames; and generating a motion estimation in reconstructed images by using a motion free AI reconstructed image frame as a reference frame.

Abstract: An image reconstruction system generates a motion estimation using images that have been reconstructed using AI processing. The system receives listmode data collected by an imaging system and produces two or more histo-images based on the listmode data. The system provides the two or more histo-images to an AI system and receives two or more AI reconstructed images back from the AI system based on the two or more histo-images. The system generates a motion estimation based on the two or more AI reconstructed images.

Abstract: Systems and methods for reconstructing medical images are disclosed. Measurement data, such as sinogram data, is received from an image scanning system. A plurality of masks are applied to corresponding portions of the measurement data to generate a plurality of masked measurement data portions. In some examples, the measurement data is encoded before the plurality of masks are applied. A neural network including a plurality of fully connected layers is applied to the plurality of masked measurement data portions to generate a plurality of image patches. The plurality of image patches are then combined to generate an initial image. In some examples, refinement and scaling operations are applied to the initial image and corresponding attenuation maps to generate a final image. In some examples, the final image is stored in a database. In some examples, the final image is displayed for diagnosis.

Abstract: A computer-implemented method for generating a motion corrected image is provided. The method includes receiving listmode data collected by an imaging system; producing two or more histo-image frames or two or more histo-projection frames based on the listmode data; providing the two or more histo-image frames or two or more histo-projection frames to an Artificial Intelligence (AI) system; receiving two or more AI reconstructed images from the AI system based on the two or more histo-image frames or the two or more histo-projection frames; and generating a motion estimation in reconstructed images by using a motion free AI reconstructed image frame as a reference frame.

Abstract: An image reconstruction system generates a motion estimation using images that have been reconstructed using AI processing. The system receives listmode data collected by an imaging system and produces two or more histo-images based on the listmode data. The system provides the two or more histo-images to an AI system and receives two or more AI reconstructed images back from the AI system based on the two or more histo-images. The system generates a motion estimation based on the two or more AI reconstructed images.

Abstract: An image reconstruction system generates de-noised, attenuation corrected, and scatter corrected images using AI processing. The system receives a low-dose PET image and applies a machine learning algorithm via a convolutional neural network to the low-dose PET image to generate an output image. The output image includes correction for scatter and attenuation associated with the image being low-dose. The system provides the output image to a computing device comprising a user interface.

Abstract: A system and method include execution of a first scan to acquire a first PET dataset, back-projection of the first PET dataset to generate a first histo-image, input of the first histo-image to a trained neural network, and reception of a first output image from the trained neural network.

Abstract: A system and method include execution of a first scan to acquire a first PET dataset, back-projection of the first PET dataset to generate a first histo-image, input of the first histo-image to a trained neural network, and reception of a first output image from the trained neural network.

Abstract: Systems and methods for reconstructing medical images are disclosed. Measurement data, such as sinogram data, is received from an image scanning system. A plurality of masks are applied to corresponding portions of the measurement data to generate a plurality of masked measurement data portions. In some examples, the measurement data is encoded before the plurality of masks are applied. A neural network including a plurality of fully connected layers is applied to the plurality of masked measurement data portions to generate a plurality of image patches. The plurality of image patches are then combined to generate an initial image. In some examples, refinement and scaling operations are applied to the initial image and corresponding attenuation maps to generate a final image. In some examples, the final image is stored in a database. In some examples, the final image is displayed for diagnosis.

Abstract: A method for collecting and maintaining oil at elevated pressures eliminates the undesirable discharge of volatile components to the atmosphere. At least two tanks are interconnected by a valved liquid inlet manifold and a valved gas outlet. While one tank is being filled with pressurized petroleum, the second tank is in valved vapor communication with the first tank and both tanks are isolated from atmosphere. This configuration permits the filling and storage of the first tank at pressures above atmospheric. When the first tank is full to capacity, the inlet manifold directs liquid into the second tank. Before unloading the first tank to a tank truck or pipeline, any excess vapor pressure is vented to the atmosphere. The process is repeated by introducing the second tank to an emptied first tank.