Model-Based Radiation Therapy Data Routing
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Solution Overview
Problem
Conventional radiation therapy data routing methods are inefficient and time-consuming, relying on manual user input and lacking flexibility, as they do not easily implement logic or branching, leading to rigid workflows and suboptimal resource utilization in radiation oncology systems with diverse devices having different capabilities.
Innovation Solution
An adaptable model-based method for routing radiation therapy data is introduced, where a computing device generates a model of the radiation oncology system, including nodes and relationships, to automatically select efficient data transfer paths based on processing capabilities and storage availability, transforming data formats as needed, and updating the model in real-time to optimize data transfers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual user input methods are used for data routing, then system complexity is reduced, but productivity and efficiency deteriorate due to time-consuming workflows
Solution Approach 1:
The system automatically routes data through the radiation oncology system without requiring manual user intervention. The model-based routing system self-determines optimal data transfer paths by querying system elements about their processing capabilities and storage availability, eliminating the need for manual configuration while maintaining high efficiency
Solution Approach 2:
The system continuously queries system elements about their processing capability and storage space, using this feedback information to dynamically select optimal data transfer paths. The model is updated in real-time based on system state changes, enabling adaptive routing that responds to current system conditions
2Adaptability or versatility
If rigid routing paths are used, then device complexity is reduced, but adaptability deteriorates when system resources change
Solution Approach 1:
The routing system transitions from static rigid paths to dynamic adaptive paths. The model-based approach allows the system to automatically adjust data transfer routes in real-time based on changing resource availability, processing capabilities, and storage conditions, enabling flexible workflows that respond to system state changes
Solution Approach 2:
The system changes routing parameters dynamically by querying system elements about their current processing capability and storage availability. The model updates its parameters based on real-time system state, allowing the routing logic to adapt to varying conditions without requiring complex manual reconfiguration
3Reliability
If data is transferred without format transformation, then transfer speed is improved, but reliability deteriorates due to compatibility issues between diverse devices
Solution Approach 1:
The system performs data format transformation as a preliminary action before transfer by querying destination elements about their required formats and preparing data accordingly. This advance preparation ensures compatibility is established before actual transfer, reducing delays and ensuring reliable data reception across diverse devices
Data Source
AI summary
Various methods and systems are provided for adaptably routing radiation therapy data based on a model of a radiation oncology system. In one example, a method comprises defining a model of a radiation oncology system; storing the model in a non-transitory memory of a computing device; sending a query from a source system element to a destination system element to determine a processing capability and an available storage space of the destination system element; selecting a transfer path from the source system element to the destination system element based on the model; transforming data into a format that is compatible with the destination system element; transferring transformed data to the destination system element according to the transfer path; updating the model of the radiation oncology system to reflect a real-time state of transfer; and storing an updated model in the non-transitory memory.