Multi-Leaf Collimator Sequencing for Faster Radiation Plan Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The optimization of radiation treatment plans for multi-leaf collimators is time-consuming and complex, relying on fast but difficult-to-adjust algorithms, which complicates the process of generating optimal leaf sequences while accommodating physical restrictions and speed limits.
Innovation Solution
A control circuit with a memory storing a fluence map and deep learning models, specifically neural network models trained via supervised or reinforcement learning, iteratively optimizes radiation treatment plans by generating a leaf sequence for a multi-leaf collimator, speeding up the optimization process and making it more adjustable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional optimization algorithms are used for leaf sequencing, then the treatment plan can be optimized, but the process becomes time-consuming and complex
Solution Approach 1:
The system pre-calculates and stores optimal leaf sequences in a lookup table during an offline training phase using reinforcement learning. During actual treatment planning, the pre-computed sequences are directly retrieved and applied, eliminating the need for time-consuming real-time optimization calculations while maintaining high-quality treatment plan optimization.
Solution Approach 2:
The invention creates a simplified computational model (neural network) that copies the essential decision-making patterns of complex optimization algorithms. The neural network learns optimal leaf sequencing strategies from extensive training data and reproduces these patterns during inference, providing fast approximations that balance optimization quality with computational speed.
2Productivity
If fast leaf-sequencing algorithms are used, then the optimization process speeds up, but the algorithms become complex and difficult to adjust on a case-by-case basis
Solution Approach 1:
The system employs a dynamic approach where the neural network model can be retrained or fine-tuned for different treatment scenarios, patient anatomies, or clinical requirements. This allows the algorithm to adapt to case-by-case variations without requiring complex manual adjustments, as the model learns optimal strategies for different situations during training and automatically applies the appropriate behavior during inference.
3Manufacturing precision
If complex optimization iterations are performed to optimize each leaf pair position, then the treatment plan quality improves, but the computational burden increases significantly
Solution Approach 1:
The invention replaces the traditional mechanical iterative optimization process with a neural network-based system. Instead of performing repeated mathematical calculations and simulations to optimize leaf positions, the pre-trained neural network directly predicts optimal positions based on the treatment plan parameters, substituting complex computational mechanics with a learned predictive model that achieves similar or better accuracy with significantly reduced computational burden.
Data Source
AI summary
A memory has a fluence map that corresponds to a particular patient stored therein. This memory also has at least one deep learning model stored therein trained to deduce a leaf sequence for a multi-leaf collimator from a fluence map. A control circuit operably coupled to that memory iteratively optimizes a radiation treatment plan to administer therapeutic radiation to that patient by, at least in part, generating a leaf sequence as a function of the at least one deep learning model and the fluence map that corresponds to the patient.