18188377. AUTOMATED PARAMETER SELECTION FOR PET SYSTEM simplified abstract (GE Precision Healthcare LLC)
Contents
AUTOMATED PARAMETER SELECTION FOR PET SYSTEM
Organization Name
Inventor(s)
Abolfazl Mehranian of Oxford (GB)
Scott Wollenweber of Waukesha WI (US)
Kuan-Hao Su of Waukesha WI (US)
Robert John Johnsen of Waukesha WI (US)
Floribertus P Heukensfeldt Jansen of Ballston Lake NY (US)
AUTOMATED PARAMETER SELECTION FOR PET SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18188377 titled 'AUTOMATED PARAMETER SELECTION FOR PET SYSTEM
The current disclosure presents systems and methods for enhancing the quality of medical images through a recommendation system that suggests appropriate parameter settings to users of medical imaging systems. In one example, a hybrid recommendation system for recommending parameter settings for acquiring and/or reconstructing images using a Positron Emission Tomography (PET) system includes a first model trained to predict a parameter setting based on a preliminary reconstructed image, and a second model trained to customize the predicted parameter setting based on user preferences.
- The patent application introduces a hybrid recommendation system for optimizing parameter settings in medical imaging.
- The system includes two models - one predicts parameter settings based on reconstructed images, and the other customizes these settings based on user preferences.
- The innovation aims to improve the quality and efficiency of medical image acquisition and reconstruction processes.
- By tailoring parameter settings to user preferences, the system enhances the overall user experience and image quality.
- This technology has the potential to revolutionize the field of medical imaging by streamlining processes and improving outcomes.
Potential Applications: This technology can be applied in various medical imaging settings such as PET systems, MRI machines, CT scanners, and more. It can be utilized in hospitals, clinics, research facilities, and other healthcare institutions to enhance the quality of diagnostic imaging.
Problems Solved: Addresses the challenge of optimizing parameter settings in medical imaging systems. Improves the accuracy and efficiency of image acquisition and reconstruction processes. Enhances user experience by customizing settings based on individual preferences.
Benefits: Enhanced image quality and diagnostic accuracy. Improved efficiency in medical imaging procedures. Customized user experience leading to higher satisfaction. Streamlined processes for healthcare professionals.
Commercial Applications: Title: "Optimizing Parameter Settings in Medical Imaging: A Game-Changer for Healthcare" This technology can be commercialized by partnering with medical device manufacturers to integrate the recommendation system into their imaging equipment. It can also be marketed to healthcare facilities as a way to improve patient care and streamline imaging processes, potentially increasing the demand for such systems in the market.
Questions about the technology: 1. How does the hybrid recommendation system in this patent application differ from traditional methods of setting parameters in medical imaging? The hybrid recommendation system combines predictive modeling with user customization to optimize parameter settings, offering a more tailored approach compared to standard methods.
2. What are the potential implications of this technology on the field of medical imaging and patient care? This technology has the potential to revolutionize medical imaging by improving image quality, diagnostic accuracy, and overall user experience, ultimately leading to better patient outcomes.
Original Abstract Submitted
The current disclosure provides systems and methods for increasing a quality of medical images via a recommendation system that recommends appropriate parameter settings to a user of a medical imaging system. In one example, a hybrid recommendation system for recommending a parameter setting for acquiring and/or reconstructing an image via a Positron Emission Tomography (PET) system comprises a first model trained to predict a parameter setting based on a preliminary reconstructed image; and a second model trained to customize the predicted parameter setting based on a preference of a user of the hybrid recommendation system.
