Deep Reinforcement Learning for Nuclear Imaging Model Adaptation
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Solution Overview
Problem
Current nuclear imaging systems face challenges in generating organ-specific models that can adapt to different clinical scenarios and user-specific requirements, often requiring long product lifecycles for model modifications.
Innovation Solution
A method utilizing deep-reinforcement learning to revise image models for nuclear imaging by receiving patient scan data, generating reconstructed images, and applying feedback to modify the image models, thereby generating refined models that better meet user-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional model generation methods are used for nuclear imaging, then models can be generated for specific organs and body regions, but the models cannot adapt to different clinical scenarios and user-specific requirements without long product lifecycles
Solution Approach 1:
The patent implements dynamic model adaptation by enabling image models to be continuously refined through deep reinforcement learning based on user feedback. The system transitions from static pre-defined models to dynamic models that automatically adjust and improve over time through iterative learning processes, allowing the same model to adapt to different clinical scenarios and user preferences without requiring complete model replacement
Solution Approach 2:
The system enables self-service model refinement where the deep reinforcement learning process automatically generates model modifications based on user feedback without requiring manual intervention for each adjustment. The learning process autonomously identifies patterns in feedback and implements optimal model revisions, reducing the need for time-consuming manual model editing and deployment cycles
2Ease of operation
If deep reinforcement learning is applied to generate model modifications, then user-specific requirements can be met, but the processing time and computational complexity increase
Solution Approach 1:
The patent implements a feedback-driven model refinement process where user feedback on reconstructed images is systematically collected and fed into the deep reinforcement learning process. This feedback loop enables the system to learn from actual user interactions and preferences, automatically adjusting model parameters to better meet user-specific requirements while maintaining a manageable complexity through focused learning objectives
Solution Approach 2:
The system achieves user-specific customization by dynamically adjusting model parameters through deep reinforcement learning rather than creating entirely new models. The learning process optimizes specific parameters such as organ contour definitions, tissue classification thresholds, and reconstruction settings based on user feedback, enabling flexible adaptation without proportionally increasing overall system complexity
Data Source
AI summary
Methods and systems of revising image models for nuclear imaging are disclosed. A system receives first patient scan data corresponding to one or more nuclear imaging scans performed on one or more individuals and generates a first reconstructed image by applying a first image model to the first patient scan data. Feedback is received regarding the first reconstructed image and the feedback is provided as an input to a deep-reinforcement learning process. The deep-reinforcement learning process is configured to generate at least one modification for the first image model based on the feedback regarding the first reconstructed image. A second image model is generated by applying the at least one modification generated by the deep-reinforcement learning process to the first image model.