6 results about "Patient exposure" patented technology

A method includes positioning a patient in a first orientation relative to a radiation source. The method further includes measuring one or more locations of a patient's thorax using a 3D imaging technique. The method further includes, while measuring the one or more locations of the patient's thorax using the 3D imaging technique: generating a model of the patient's thorax using the one or more locations of the patient's thorax; updating the model of the patient's thorax as the patient breathes; and exposing the patient to a radiation dose using the radiation source, wherein the dose is determined according to the model of the patient's thorax.

Provided herein is technology relating to medical imaging and radiation therapy and particularly, but not exclusively, to devices, methods, and systems for positioning a patient with respect to a radiation source to image the patient and / or to treat the patient by exposing the patient to a radiation beam produced by the radiation source.

This invention relates to a system for controlling patient exposure generated from an X-ray device using a machine learning model. The system includes one or more processors configured to: receive spatial sensor data from an imaging device; receive one or more exposure parameters from the X-ray device; provide the spatial sensor data and exposure settings to the machine learning model; and, based on analysis of the received spatial sensor data, obtain a prediction of the optimal exposure settings for a given X-ray imaging environment from the machine learning model. The invention also relates to an X-ray system including the above-described system for controlling exposure, and a method for controlling patient exposure generated by an X-ray device.

Systems and methods for generating synthetic computed tomography (CT) images, such as generating synthetic time-resolved four-dimensional CT or single-photon emission CT images of the human respiratory system, are described. Generating a synthetic CT image includes processing at least one output from a physics-based lung simulation model in combination with at least one generative learning model. The output from the physics-based lung simulation model provides additional information that improves clinical decision-making without additional patient exposure to radiation-intensive imaging procedures.

The warm-keeping quilt comprises a quilt cover and a quilt core, the upper end face of the quilt cover and the upper end face of the quilt core are jointly provided with a visible opening, a covering curtain is fixedly installed on the inner wall of the visible opening, the inner walls of the two sides of the visible opening are connected with the outer walls of the two sides of the covering curtain through sealing zippers, first edge covering cloth is sewn on the upper end face of the quilt cover, and a second edge covering cloth is sewn on the lower end face of the quilt cover. A plurality of first female buckles are fixedly installed on the inner wall of the first edge covering cloth, and first male buckles matched with the first female buckles are fixedly installed on the upper end face of the cover curtain. Through the arrangement of the cover curtain, the sealing zipper, the first mother buckle, the first son buckle and other components, the warm-keeping quilt can be locally and conveniently opened and closed, medical staff do not need to lift the quilt integrally during examination, only need to separate the first mother buckle and pull the sealing zipper, the exposure area of a patient is greatly reduced, the body temperature can be maintained, cold can be prevented, privacy can be protected, and the medical staff can conveniently use the warm-keeping quilt. The awkwardness of the patient is reduced, the comfort and the adaptability are improved, and the practicability is high.

Systems and methods for generating synthetic computed tomography (CT) images, such as generating synthetic time-resolved four-dimensional CT or single-photon emission CT images of the human respiratory system, are described. Generating a synthetic CT image includes processing at least one output from a physics-based lung simulation model in combination with at least one generative learning model. The output from the physics-based lung simulation model provides additional information that improves clinical decision-making without additional patient exposure to radiation-intensive imaging procedures.