Adjoint transport for dose in treatment trajectory optimization for external beam radiation therapy
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Solution Overview
Problem
Existing methods for determining treatment fields in external beam radiation therapy are sub-optimal due to variability in patient anatomy and clinical goals, necessitating improved methods for optimizing treatment geometries.
Innovation Solution
A method involving the use of adjoint transport to evaluate adjoint photon fluence and dose response in a delivery coordinate space, determining beam's eye view (BEV) regions and connectivity manifolds to optimize treatment trajectories and field geometries, considering multiple candidate energy modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forward transport methods are used to evaluate dose for each candidate vertex and beamlet, then dose calculation accuracy is maintained, but computational cost and time consumption increase significantly
Solution Approach 1:
The patent applies adjoint transport theory to invert the traditional dose calculation approach. Instead of forward transporting photons from each beamlet to calculate dose (forward transport), the method adjoint-transportes from ROI to source, evaluating dose response by tracing adjoint photons backward from the region of interest to the beam source. This inversion fundamentally reduces computational complexity while maintaining dose calculation accuracy, as the adjoint solution field can be reused across multiple beamlet evaluations.
2Reliability
If treatment geometries are optimized to account for patient anatomy variability and clinical goals, then treatment plan quality improves, but the complexity of determining optimal trajectories and field geometries increases
Solution Approach 1:
The patent performs preliminary adjoint transport calculations to pre-compute the adjoint solution field from each ROI before evaluating candidate vertices and beamlets. This preliminary action creates a reusable adjoint fluence map that accelerates subsequent dose evaluations across multiple treatment geometry candidates. By preparing this foundational data structure in advance, the system efficiently explores multiple trajectories and field geometries without repeating expensive transport calculations, thus managing optimization complexity while maintaining high treatment plan quality.
Data Source
AI summary
A method of trajectory optimization for radiotherapy treatment includes providing a patient model having one or more regions of interest (ROIs), defining a delivery coordinate space (DCS), for each ROI, solving an adjoint transport to obtain an adjoint solution field from the ROI, for each vertex in the DCS, evaluating an adjoint photon fluence by performing ray tracing of the adjoint solution field, evaluating a dose of the ROI using the adjoint photon fluence, for each vertex in the DCS, evaluating a respective beam's eye view (BEV) score of each pixel of a BEV plane using the doses of the one or more ROIs, determining one or more BEV regions in the BEV plane based on the BEV scores, determining a BEV region connectivity manifold based on the BEV regions, and determining one or more optimal treatment trajectories based on the BEV region connectivity manifold.


