Deep Learning Engine for Adaptive Radiotherapy Planning
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Solution Overview
Problem
Conventional radiotherapy treatment planning is time and labor intensive, requiring skilled professionals to manually delineate structures and generate treatment plans, which can lead to variations and uncertainties in target volume definition and radiation dose distribution.
Innovation Solution
The use of a deep learning engine with multiple processing pathways to automate radiotherapy treatment planning and adaptive radiotherapy treatment planning, processing medical image data at different resolution levels to generate feature data and output data for treatment planning, including structure data, dose data, and treatment delivery data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual treatment planning is used, then treatment plan accuracy can be maintained through expert judgment, but the process is time and labor intensive
Solution Approach 1:
The system enables self-service automated treatment planning where the computer system automatically generates treatment plans using machine learning models trained on historical data, eliminating the need for manual expert intervention while maintaining plan quality through algorithmic optimization
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on extensive datasets of treatment plans and outcomes before actual treatment planning, so that when a new patient is processed, the model is already prepared to generate accurate treatment plans rapidly without requiring real-time expert analysis
2Manufacturing precision
If manual structure delineation is performed, then anatomical accuracy can be achieved through expert visualization, but variations and uncertainties occur between different planners
Solution Approach 1:
The system performs automated structure delineation using machine learning models that independently identify and segment anatomical structures from medical images, eliminating inter-planner variability by replacing manual expert visualization with consistent algorithmic processing that applies the same criteria to all cases
3Ease of manufacture
If conventional treatment planning methods are used, then treatment plan generation can be performed with existing tools, but the process requires significant labor and time resources
Solution Approach 1:
The system replaces the mechanical process of manual treatment planning with an automated computational system using machine learning models and computer algorithms, substituting human expert labor with electronic processing that can rapidly generate multiple treatment plan options without physical manipulation or manual calculation
Solution Approach 2:
The automated system performs treatment plan generation independently without requiring manual intervention, automatically processing patient data, selecting appropriate treatment parameters, and generating optimized treatment plans through self-contained computational workflows
Data Source
AI summary
Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality and planning image data associated with a second imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, processing, using the deep learning engine, the treatment image data and the planning image data to generate output data for updating the treatment plan.