PET Signal Reconstruction Through Segment-Specific Model Selection
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Solution Overview
Problem
Existing signal reconstruction methods in PET rely on intuitive selection of fitting models based on scatter plots, leading to inaccurate reconstruction results due to difficulty in measuring scatter point dispersion, affecting precision.
Innovation Solution
Segment the signal based on characteristic information, apply multiple fitting models to signal segments, and determine a final fitting model by comparing fitted parameter values with measurement values to select the most accurate model for each segment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple fitting models are applied to signal segments, then the accuracy of signal reconstruction is improved, but the device complexity increases
Solution Approach 1:
The signal is divided into multiple signal segments based on characteristic information such as amplitude, rising time, and falling time. Different fitting models are applied to different segments, allowing the system to capture the varying characteristics of the signal across different phases while maintaining manageable complexity through localized model application.
Solution Approach 2:
The system dynamically selects the most appropriate fitting model for each signal segment by comparing fitted parameter values with measurement values. This dynamic selection process adapts to the specific characteristics of each segment, optimizing accuracy without requiring a single complex model for the entire signal.
2Measurement precision
If multiple fitting models are applied to signal segments, then the precision of signal reconstruction is improved, but the computation time increases
Solution Approach 1:
By segmenting the signal and applying fitting models only to specific segments rather than the entire signal, the computation time is reduced while maintaining precision where it matters most. The segmentation allows focused computational resources on critical signal portions.
Solution Approach 2:
The system applies multiple fitting models only where necessary - specifically to signal segments where characteristic information indicates variability or importance - rather than uniformly across the entire signal. This partial application of multiple models reduces overall computation time while preserving reconstruction precision in critical areas.
Data Source
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AI summary
The disclosure discloses methods for determining fitting model for a signal, reconstructing a signal and devices thereof. The determination method may comprise the following steps: segmenting a sampled signal based on collected characteristic information of the signal to obtain a plurality of signal segments; fitting sampling points in the plurality of signal segments using a plurality of fitting models; acquiring fitted values for a parameter of interest in each signal segment based on fitting results; comparing each of the fitted values for the parameter of interest under each of the fitting models with an acquired measurement value for the parameter of interest, and determining a final fitting model for reconstructing the signal among the plurality of the fitting models based on comparison results. In the technical solution provided in the disclosure, the accuracy of the signal reconstruction result and precision of the signal reconstruction may be improved.