Proton Therapy Plan Optimization for Shorter Irradiation Time
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Solution Overview
Problem
Conventional proton therapy systems do not optimize treatment plans for delivery system machine-specific limitations, leading to inefficient irradiation times and increased probability of interlock occurrences, while existing systems focus primarily on dose volume constraints and plan robustness without considering temporal behavior.
Innovation Solution
A computer-implemented method and system that perform time-based optimization during treatment planning by accounting for beam characteristics and machine-specific parameters, such as beam energies, spot positions, and spot lateral spread, to create efficient and clinically acceptable treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional proton therapy systems optimize treatment plans for dose volume constraints and plan robustness, then plan quality is improved, but irradiation time increases and system reliability decreases
Solution Approach 1:
The optimization engine modifies machine-specific parameters (beam energies, spot positions, spot lateral spread) to find optimal treatment delivery configurations that minimize irradiation time while maintaining plan quality constraints, directly resolving the contradiction between speed and reliability
Solution Approach 2:
The system incorporates feedback loops where the optimization engine continuously evaluates treatment plan results against machine capabilities and adjusts parameters iteratively to achieve optimal balance between irradiation time and system reliability
2Reliability
If treatment plans are optimized for dose volume constraints without considering machine-specific parameters, then plan quality is maintained, but interlock occurrences increase
Solution Approach 1:
The system optimizes machine-specific parameters (beam energies, spot positions, spot lateral spread) to ensure treatment plans are compatible with delivery system capabilities, reducing interlock occurrences while maintaining efficient treatment delivery
Solution Approach 2:
The optimization engine performs preliminary optimization of treatment plans before delivery by accounting for machine-specific limitations, preventing interlock occurrences during actual treatment execution and ensuring smooth delivery
3Productivity
If beam parameters are optimized for shortest irradiation time, then treatment speed is improved, but plan quality may deteriorate
Solution Approach 1:
The optimization engine systematically varies beam parameters (energies, spot positions, lateral spread) to identify the optimal configuration that achieves the shortest irradiation time while maintaining acceptable plan quality standards
Solution Approach 2:
The system dynamically adjusts beam parameters based on real-time optimization calculations, allowing flexible modification of treatment delivery parameters to balance speed and quality requirements for each specific treatment case
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
Reduces irradiation times, minimizes interlock occurrences, and improves system reliability by optimizing treatment plans for machine capabilities, allowing for shorter treatment delivery times and reduced patient discomfort.
Implementation Method 1
The protons in the beam are accelerated (via a generated electric field)
Implementation Method 2
the beam of accelerated protons is subsequently 'extracted' and magnetically directed through a series of interconnecting tubes
Implementation Method 3
By superposition of several proton beams of different energies, a Bragg peak can be spread out to cover target volumes using a uniform, prescribed dose
Data Source
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Figure 2
Figure 3A
AI summary
A computer implemented method (800) of determining a treatment plan for a proton radiation therapy system, said method comprising: based on the proton radiation therapy system, accessing (804) machine configuration information including a plurality of machine parameters; iteratively adjusting (806) the plurality of machine parameters to generate one or more candidate treatment plans; simulate (808) the plurality of generated candidate treatment plans to determine a respective treatment time and respective plan quality associated with each of the treatment plans; select (810) a candidate treatment plan that yields an acceptable plan quality and a shortest possible treatment time.