Radiation Treatment Planning with Interpolable Spot Dose Distributions
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Solution Overview
Problem
Existing radiation therapy treatment plans face challenges in optimizing spot placements and dose distributions, particularly in proton beam therapy, due to the complexity of varying spot positions during optimization, which complicates the process and increases computational demands.
Innovation Solution
A method involving normalization of spot dose distributions and recursive refinement of spot arrangements in a hierarchical data structure, such as an octree, to ensure similarity criteria are met, allowing for efficient storage and interpolation of dose distributions, thereby reducing computational load and storage requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If spot positions are allowed to vary during optimization, then a sharper penumbra and more uniform dose distribution could be achieved, but the optimization process becomes significantly more complicated
Solution Approach 1:
The patent segments the continuous spot position optimization problem into discrete spot placement decisions on a grid. By dividing the target volume into a systematic grid pattern, the optimization is simplified to selecting which grid positions receive spots, rather than continuously varying positions. This segmentation maintains the ability to achieve sharp penumbra and uniform dose distribution while reducing optimization complexity.
Solution Approach 2:
The patent changes the parameter space from continuous spot coordinates to discrete grid cell selections. By transforming the optimization variables from continuous positional parameters to discrete presence/absence decisions at grid locations, the problem becomes computationally more tractable while still achieving the desired dose distribution quality through appropriate grid resolution and spot weighting.
2Device complexity
If a fixed pattern of spot placements is used, then the optimization process is simpler, but the ability to achieve sharp penumbra and uniform dose distribution is limited
Solution Approach 1:
The patent introduces dynamics by allowing the spot pattern to adapt to the specific target geometry and dose requirements. Rather than using a completely fixed universal pattern, the system dynamically selects which grid positions receive spots based on the target volume shape, dose constraints, and optimization criteria, enabling sharp penumbra and uniform distribution while keeping the underlying grid structure for computational simplicity.
Solution Approach 2:
The patent applies local quality by allowing different regions of the target volume to have different spot densities and patterns. The grid-based approach enables local adaptation where spots are placed more densely in regions requiring sharper penumbra or more uniform dose, while using coarser spacing in less critical areas, thus achieving high precision where needed without uniformly increasing overall complexity.
3Manufacturing precision
If more spots are used to improve dose uniformity and penumbra, then treatment quality improves, but treatment time increases
Solution Approach 1:
The patent applies partial action by using a grid-based selection approach where not all grid positions receive spots. Instead of uniformly distributing spots throughout the volume, the optimization selectively activates only those grid positions necessary to achieve the prescribed dose uniformity and penumbra requirements, thus reducing the total spot count and treatment time while maintaining dose quality.
Solution Approach 2:
The patent changes parameters by optimizing spot weights and selection based on grid position rather than increasing spot density uniformly. By adjusting the presence and weighting of spots at different grid locations, the system achieves dose uniformity and sharp penumbra through intelligent distribution rather than sheer number, reducing treatment time while maintaining quality.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables faster and more efficient optimization of radiation therapy plans by reducing memory requirements and computational complexity, while ensuring uniform dose distribution and minimizing exposure to healthy tissue.
Implementation Method 1
A proton beam reaches a depth in tissue that depends on the energy of the beam, and releases most of its energy (delivers most of its dose) at that depth. The region of a depth-dose curve where most of the energy is released is referred to as the Bragg peak of the beam.
Data Source
AI summary
An initial, relatively coarse arrangement of spots in a target volume and a respective dose distribution per spot are accessed from memory or determined. If the dose distributions of neighboring spots do not satisfy a similarity criterion, then a new set of spots with finer spacing is determined for the regions that include dissimilar spots (e.g., spots are added between the dissimilar spots), and spot dose distributions are determined for the new spot arrangement. The process is repeated until the similarity criterion is satisfied for all or a threshold number of spots or until a minimum spot spacing is reached. The final arrangement of spots and dose distributions for the spots can be stored. During subsequent optimization of a treatment plan based on the final arrangement of spots, a dose distribution for a point that is between the spots can be determined by interpolating the dose distributions of nearby spots.