Deep Learning Engine for Adaptive Radiotherapy Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional radiotherapy treatment planning is time and labor intensive, requiring skilled professionals for manual segmentation and planning, and lacks efficiency in adapting to changes in patient anatomy during treatment, leading to potential radiation overdose in healthy structures.
Innovation Solution
The use of a deep learning engine with multiple processing pathways to process medical image data at different resolution levels, enabling automated segmentation, dose prediction, and adaptive treatment planning, thereby improving the efficiency and accuracy of radiotherapy treatment planning and adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual segmentation and treatment planning methods are used, then treatment planning accuracy can be maintained through expert judgment, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The deep learning model processes medical images through multiple processing pathways that segment the image data at different resolution levels, with each pathway handling specific spatial frequency information to efficiently extract relevant features for treatment planning
Solution Approach 2:
The patent replaces manual mechanical segmentation by experts with an automated deep learning system that uses neural networks to perform image analysis, structure segmentation, and treatment plan generation, dramatically reducing time while maintaining accuracy
2Reliability
If frequent treatment plan updates are performed to adapt to anatomical changes, then radiation safety improves, but the workload and time requirements increase
Solution Approach 1:
The deep learning system enables automated adaptive treatment planning that can independently process new imaging data and generate updated treatment plans without requiring manual re-segmentation, allowing frequent updates to maintain radiation safety while reducing workload
Solution Approach 2:
The system performs preliminary automated segmentation and analysis of anatomical structures from new imaging data, preparing treatment plan updates in advance before clinical review, thereby improving both safety and efficiency
3Manufacturing precision
If manual segmentation is performed to accurately identify tumor and healthy structures, then treatment precision can be achieved, but skilled professionals are required increasing labor demands
Solution Approach 1:
The patent replaces manual segmentation performed by skilled professionals with an automated deep learning system that uses convolutional neural networks to automatically identify and segment tumor and healthy structures, eliminating the need for expert manual intervention while maintaining high precision
Solution Approach 2:
The deep learning model learns from training data consisting of expert-segmented images, creating a digital copy of expert knowledge that can be applied automatically to new cases without requiring the actual experts to be present
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. 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, transforming the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality. The method may further comprise: processing, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.