Multi-Leaf Collimator Leaf Sequencing Using Deep Learning
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Solution Overview
Problem
Existing radiation treatment plans optimized for multi-leaf collimators are time-consuming and rely on complex, case-specific algorithms, often failing to account for physical restrictions and speed limits, leading to inefficiencies in generating optimal leaf sequences.
Innovation Solution
Employing a deep learning model, such as a neural network, to deduce a leaf sequence for a multi-leaf collimator from a fluence map, facilitating faster and more adjustable optimization of radiation treatment plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional optimization processes are used to generate leaf sequences for multi-leaf collimators, then treatment plan quality can be improved, but the process becomes unduly time-consuming and reduces productivity
Solution Approach 1:
The system pre-calculates and stores optimal leaf sequences during a training phase using traditional optimization methods. During actual treatment planning, these pre-computed sequences are retrieved and applied directly, eliminating the need to perform time-consuming optimization calculations in real-time while maintaining treatment plan quality
Solution Approach 2:
The invention creates a database of pre-computed leaf sequences that serve as templates or copies of optimized solutions. These stored sequences can be rapidly copied and applied to similar treatment scenarios without re-running the full optimization process, significantly reducing computation time while preserving treatment quality
2Manufacturing precision
If complex case-specific algorithms are used for leaf sequencing, then optimization accuracy can be improved, but device complexity and difficulty of adjustment increase
Solution Approach 1:
The system performs the complex optimization calculations automatically during an initial training phase and stores the results. The runtime system simply retrieves pre-computed sequences without requiring complex real-time calculations, making the system easier to operate and adjust while maintaining accuracy through the pre-stored optimized sequences
Solution Approach 2:
Complex algorithmic work is performed in advance during system initialization and training phases. The results are cached and reused during treatment planning, eliminating the need for complex real-time computations and reducing the operational complexity of the system
Data Source
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AI summary
A memory (102) 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 (101) 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.