Neural Network Correcting Detector Deviations in Radiation Measurements
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Solution Overview
Problem
Current methods for correcting detector-specific deviations in radiation measurements during radiotherapy are cumbersome and time-consuming, requiring conventional reference measurements to achieve accurate results.
Innovation Solution
An artificial neural network is trained with synthetic data pairs that include a first datum describing an ideal beam profile and a second datum affected by detector-specific transformations, allowing the network to correct detected beam profiles for detector-specific influences, potentially eliminating the need for reference measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reference measurements are performed to correct detector-specific deviations, then measurement accuracy is improved, but time consumption and process complexity increase
Solution Approach 1:
The neural network is trained in advance with synthetic data pairs that model the detector-specific transformation. This preliminary training allows the network to learn the correction mapping before actual measurements are performed, eliminating the need for time-consuming reference measurements during the measurement process itself.
Solution Approach 2:
Instead of performing actual reference measurements with physical detectors, the invention creates synthetic copies of measurement data through simulated detector responses. These synthetic data pairs replicate the detector-specific deviations without requiring physical reference measurements, thus saving time while maintaining correction accuracy.
2Measurement precision
If conventional reference measurements are performed to correct detector-specific deviations, then measurement accuracy is improved, but device complexity and operational effort increase
Solution Approach 1:
The invention replaces the mechanical process of performing physical reference measurements with a computational neural network system. The neural network automatically performs the correction based on learned patterns from synthetic data, eliminating the need for manual reference measurement procedures and reducing operational complexity.
Solution Approach 2:
The neural network is designed to autonomously correct detected beam profiles without requiring manual intervention or complex reference measurement setups. The system self-corrects by applying the learned transformation from the synthetic training data, simplifying the operational process.
3Productivity
If synthetic data pairs are used to train the neural network, then correction speed and efficiency are improved, but data generation complexity increases
Solution Approach 1:
The synthetic data generation process varies key parameters such as beam profiles, detector positions, and transformation characteristics to create diverse training examples. By systematically changing these parameters, the method generates comprehensive training data without requiring complex physical measurement setups for each scenario.
Data Source
AI summary
A method for correcting a result (5), detected using a detector (2), of a radiation-physics process pertaining to a radiation-source (6) by an artificial neural network (4) is provided. The artificial neural network (4) was initially trained with synthetic data pairs (8), and the synthetic data pairs (8) include a first, in particular synthetic, datum (9) and a second, in particular synthetic, datum (10). The data (9, 10) of a data pair (8) differ by a detector-specific transformation (11) that is uniform for all data pairs (8). A detection arrangement as well as a phantom for use with such a detection arrangement are also provided.