Siemens Medical Solutions Usa, Inc.

The University of Tennessee Research Foundation

Insulet Corporation

William Whiteley

Deepak Bharkhada

Vladimir Y Panin

Michael E Casey

Yibin Zheng

Jicun Hu

Chuanyu Zhou

Saeed Salavati

Mert Sevil

Rangarajan Narayanaswami

Sam Carl

Jens Gregor

The exemplary embodiments may provide a drug delivery device that receives glucose level values for a user (e.g., a diabetic patient) and based on the glucose level values, determines when the user has consumed a meal. In some embodiments, the drug delivery device may calculate an appropriate bolus dose and automatically deliver the drug bolus to the user. In some embodiments, instead of detecting the meal, the user may announce the meal, such as by activating an element on the drug delivery device or on a management device for the drug delivery device. Responsive to the meal announcement, the drug delivery device may calculate the drug bolus dose and deliver the drug bolus. In conjunction with the delivery of the drug bolus, the drug delivery device may relax one or more safety constraints for a relaxation period following the drug bolus delivery so that additional basal drug may be delivered, if needed, under relaxed constraints.

Automatically or manually initiated meal bolus delivery with subsequent automatic safety constraint relaxation

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.

Systems and methods for motion estimation in PET imaging using AI image reconstructions

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.

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.

Methods and apparatus for neural network based image reconstruction

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.

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Knoxville, TN, United States of America

William Whiteley