Partial Deformation Maps for Motion-Affected Radiation Dose Reconstruction
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Solution Overview
Problem
Current radiation treatment systems face challenges in accurately delivering doses to moving tumors and anatomical structures due to limitations in reconstructing motion-affected treatment doses, particularly in sites affected by respiration, where traditional transformation models are inaccurate and complex.
Innovation Solution
The development of partial deformation maps and multi-patient transformation models using principal component analysis (PCA) and machine learning techniques, such as auto-encoder neural networks, to generate credible non-rigid transformations from limited 2-D projections, enabling real-time adaptation and dose reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional transformation models are used for motion-affected dose reconstruction, then device complexity is reduced, but manufacturing precision (dose accuracy) deteriorates
Solution Approach 1:
The patent segments the transformation model into multiple components: a linear transformation component and a non-linear deformation component. This segmentation allows each component to be optimized independently, improving overall dose accuracy while managing complexity through modular processing of motion effects
Solution Approach 2:
The patent transitions from 2-D projection images to 3-D deformation maps by introducing a spatial dimension transformation. This dimensional upgrade enables more accurate representation of tissue deformation in three-dimensional space, significantly improving dose reconstruction accuracy for moving targets
2Measurement precision
If full 3-D deformation maps are reconstructed from limited 2-D projections, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediate statistical model that bridges limited 2-D projection data and full 3-D deformation maps. This intermediary uses principal component analysis to create a simplified representation of deformation patterns, enabling accurate 3-D reconstruction without requiring complex direct inversion of the limited projection data
Solution Approach 2:
The patent transforms the reconstruction problem by changing parameters from direct spatial coordinates to statistical parameters through principal component analysis. This parameter transformation reduces the complexity of reconstructing 3-D deformation maps from limited 2-D projections by working in a reduced-dimensional statistical space
3Productivity
If real-time adaptation is implemented for moving targets, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent performs preliminary calculations of deformation patterns and transformation models during the treatment planning phase. These pre-computed models are then applied in real-time during treatment delivery, enabling fast adaptation without requiring complex real-time computations, thus improving productivity while managing system complexity
Data Source
AI summary
A method including applying a first target-subject-specific model associated with a target subject to a treatment planning image to generate a transformed treatment planning image corresponding to a first position of a plurality of positions. The method also includes modifying one or more hyper-parameters of the first target-subject-specific model to generate a second target-subject-specific model corresponding to a second position of the plurality of positions. The method further includes controlling a radiation treatment delivery device based on the second target-subject-specific model to deliver a radiation treatment to the target subject.