PET Image Reconstruction Using Detector Functional Status Filtering
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Solution Overview
Problem
In positron emission tomography (PET) systems, the failure of detector units can lead to inaccurate PET data and compromised image reconstruction due to the inclusion of non-functional detector units, which affects the quality of acquired PET images.
Innovation Solution
A method and system for reconstructing PET images by determining the functional status of detector units, grouping them based on their status, and generating reconstruction data using only data from functional units, ensuring accurate image reconstruction by excluding or compensating for non-functional units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from all detector units is used for PET image reconstruction, then the quantity of data increases, but image accuracy deteriorates due to inclusion of non-functional detector units
Solution Approach 1:
The patent extracts and removes data from non-functional detector units from the total PET data set. By identifying detector units that fail to meet functional criteria and excluding their data from reconstruction, the system eliminates harmful data while preserving useful data from functional units, thereby resolving the contradiction between data quantity and image accuracy
Solution Approach 2:
The patent applies different quality criteria to different detector units based on their individual functional status. Rather than uniformly accepting or rejecting all detector data, the system evaluates each detector unit's performance characteristics and selectively includes only those meeting functional thresholds, ensuring local optimization of data quality across the detector array
2Device complexity
If non-functional detector units are included in reconstruction, then device complexity is reduced, but reliability of PET images deteriorates
Solution Approach 1:
The patent performs preliminary identification and classification of detector unit functional status before the actual image reconstruction process. By pre-evaluating detector functionality and organizing data accordingly, the system prepares the data set in advance to exclude non-functional units, ensuring reliable reconstruction without adding significant complexity to the overall workflow
Solution Approach 2:
The patent introduces an intermediary classification layer between raw detector data and final image reconstruction. This intermediary step categorizes detector units by functional status and selectively routes data from functional units to the reconstruction algorithm, acting as a mediator that filters harmful data while maintaining system reliability without substantially increasing complexity
3Measurement precision
If functional status determination is performed on all detector units, then image quality improves, but processing time increases
Solution Approach 1:
The patent changes the parameters used to assess detector unit functionality, focusing on key performance metrics that can be evaluated efficiently. By selecting specific functional parameters that are quick to measure and interpret, the system determines detector status without excessive processing time while still achieving the image quality improvements necessary for accurate reconstruction
Data Source
AI summary
The disclosure relates to a system and method for reconstructing a PET image. The method may include: obtaining PET data relating to an object collected by a plurality of detector units; determining functional status of the plurality of detector units; generating reconstruction data based on the functional status of the respective detector units and the PET data; and reconstructing a PET image based on the reconstruction data.