MRI-Guided Radiation Therapy Reoptimization With Parallel Workflow
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Solution Overview
Problem
Conventional radiation therapy planning and delivery require manual intervention by multiple clinical team members, leading to time-consuming and error-prone reoptimization of treatment plans due to patient anatomical changes between initial imaging and treatment day.
Innovation Solution
An automated system that recalls imaging parameters from memory, performs treatment-day scans, generates reoptimized radiation treatment plans, and delivers radiation based on real-time imaging, allowing for simultaneous parallel workflows among clinicians.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual intervention by multiple clinical team members is used for treatment plan reoptimization, then treatment plans can be reviewed and adjusted, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs automatic reoptimization of radiation treatment plans by processing treatment day images itself without requiring manual intervention from multiple clinical team members. The automated system retrieves initial treatment plans, processes new images, and generates updated plans independently, eliminating the time-consuming manual review process while maintaining accuracy through algorithmic optimization.
Solution Approach 2:
The patent replaces the mechanical manual process of reviewing and adjusting treatment plans by multiple clinicians with an automated computational system. The system uses image processing algorithms, dose calculation models, and optimization software to automatically generate and update treatment plans, substituting human manual operations with automated mechanical processes that are faster and more consistent.
2Productivity
If manual reoptimization processes are used, then treatment plans can be updated, but the workflow becomes sequential and less efficient
Solution Approach 1:
The automated reoptimization system is divided into distinct functional modules: image retrieval module, image processing module, dose calculation module, optimization module, and plan generation module. Each module performs a specific function independently, allowing the complex system to be managed through modular components that work together in a coordinated manner, reducing overall system complexity while improving productivity.
Solution Approach 2:
The system is designed as a universal platform that can handle multiple tasks within the radiation therapy workflow: retrieving initial treatment plans, processing treatment day images, calculating doses, optimizing parameters, and generating updated plans. This multi-functional approach consolidates what would otherwise require separate systems into a single integrated platform, improving efficiency without proportionally increasing complexity.
3Speed
If treatment day scans are performed automatically, then imaging can be done quickly, but parameters must be recalled from memory
Solution Approach 1:
The system performs preliminary action by storing initial imaging parameters in a database during the initial treatment planning phase. When treatment day arrives, the system automatically retrieves these pre-stored parameters, avoiding the need to recreate them manually. This preliminary storage action enables fast automatic scanning while maintaining parameter accuracy through direct retrieval from the original planning data.
Solution Approach 2:
The system creates a copy of the initial imaging parameters and treatment plan data that can be automatically retrieved and applied to treatment day scanning. Instead of manually re-entering or recreating parameters, the system copies the original parameter set from storage and uses it as a template for generating treatment day images, ensuring accuracy while enabling rapid execution.
Data Source
AI summary
Systems, methods, and computer software are disclosed that allow the automatic recalling of imaging parameters from computer memory for controlling an MRI system to perform treatment-day scans of a patient on a treatment couch in a radiotherapy system, prior to treatment. The treatment-day scans can be automatically initialized and the MRI system can then be controlled to perform the treatment-day scans according to the recalled imaging parameters. Reoptimized radiation treatment plan(s) can be automatically generated and predicted doses to anatomical structures of the patient based on the plan(s) can be displayed. Clinicians can be enabled to perform numerous reoptimization tasks simultaneously through parallel workflow interfaces and then a radiation therapy device can be controlled to deliver radiation according to a selected radiation treatment plan.