Radiation Treatment Planning for Anatomical State Variations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current radiation therapy treatment plans are based on static 3D reference images that fail to account for inter- and intra-fractional anatomical variations due to physiological processes, leading to suboptimal dose delivery and increased exposure to healthy tissues.
Innovation Solution
A computer-implemented method generates a representation of potential anatomical states during radiation treatment sessions using image processing and machine learning models, such as recurrent neural networks and deformation vector fields, to create robust treatment plans that adapt to expected anatomical changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If treatment plans are based on static 3D reference images, then the planning process is simple and fast, but the treatment plan cannot account for inter- and intra-fractional anatomical variations leading to suboptimal dose delivery
Solution Approach 1:
The system performs preliminary actions by generating multiple synthetic 3D reference images representing different anatomical states before treatment. These pre-generated images capture potential anatomical variations, allowing the treatment plan to be optimized in advance for various scenarios rather than reacting to changes during treatment.
Solution Approach 2:
The system creates synthetic copies of the patient's anatomy in different states using machine learning models. These synthetic 3D reference images replicate actual anatomical variations without requiring multiple physical scans, enabling comprehensive coverage of possible anatomical states while maintaining computational efficiency.
2Adaptability or versatility
If multiple synthetic 3D reference images representing different anatomical states are generated, then the treatment plan can account for anatomical variations, but the computational time and resources required increase
Solution Approach 1:
The system replaces complex mechanical image acquisition processes with machine learning-based synthetic image generation. Instead of performing multiple physical CT scans or MRIs to capture different anatomical states, the system uses trained neural networks to generate synthetic images from a single input image, dramatically reducing computational time and resources.
Solution Approach 2:
The system changes the approach from acquiring multiple images with different physical parameters (multiple scans) to generating images by transforming a single image through learned parameter spaces. The machine learning model captures anatomical variations through parameter transformations rather than physical re-scanning, reducing computational burden.
3Reliability
If a single 3D reference image is used for treatment planning, then the planning process is straightforward, but it fails to capture inter-fractional and intra-fractional anatomical changes
Solution Approach 1:
The system segments the anatomical variation problem into distinct categories: inter-fractional variations (between treatment sessions) and intra-fractional variations (within a session). By segmenting these types of variations and generating synthetic images for each category separately, the system manages complexity while maintaining comprehensive coverage of anatomical changes.
Solution Approach 2:
The machine learning model acts as an intermediary between the input image and the treatment plan. It mediates by generating intermediate synthetic 3D reference images that represent different anatomical states, bridging the gap between a single input image and the need for multiple anatomical representations without requiring complex processing pipelines.
4Productivity
If treatment plans are optimized for a single anatomical state, then the optimization process is fast, but the dose distribution may not be optimal when anatomical variations occur
Solution Approach 1:
The system performs preliminary optimization by generating synthetic 3D reference images representing different anatomical states before the actual treatment. Treatment plans are then optimized in advance for these pre-generated images, allowing the optimization process to account for variations without requiring real-time recomputation during treatment delivery.
Solution Approach 2:
The system introduces dynamics by generating multiple synthetic anatomical states that capture temporal variations in anatomy. Instead of optimizing for a static single state, the system creates a dynamic representation of anatomical changes, allowing the treatment plan to adapt to varying anatomical conditions while maintaining optimization speed through pre-computation.
Data Source
AI summary
A computer-implemented method is disclosed for generating a radiation treatment plan for a patient for at least one future radiation treatment session. The method comprises obtaining one or more images of the patient. The method further comprises, generating, based on the one or more images, a representation of a plurality of potential anatomical states of the patient which may occur during the future radiation treatment session. The method further comprises, generating, based on the representation, at least one treatment plan. Such a method can reduce treatment margins and can improve the adaptive radiotherapy process by improving speed, safety and reduce the need for physician presence.