PET Image Reconstruction Using Event Segmentation for Scattering Correction
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Solution Overview
Problem
PET devices with long axial lengths face challenges in data processing due to differences in random and scattering event data composition, rendering traditional correction techniques unsuitable.
Innovation Solution
A system and method that select and process PET data based on position information of coincident events, reconstruct preliminary images, determine correction data for scattering and random events, and reconstruct target images using a processor and storage medium, incorporating anatomical information from CT or MRI data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional PET data processing techniques are used, then processing is simple and fast, but image reconstruction accuracy deteriorates due to unsuitable correction for scattering and random events in long axial length devices
Solution Approach 1:
The patent segments the PET data into different categories based on position information: true coincident events, scattering events, and random events. This segmentation allows for targeted correction of each event type, improving image reconstruction accuracy while managing complexity through systematic classification of the data
Solution Approach 2:
The patent performs preliminary classification and correction of scattering and random events before final image reconstruction. By pre-processing the PET data to identify and correct these unwanted events beforehand, the system achieves better reconstruction accuracy without overwhelming complexity during the main reconstruction process
2Measurement precision
If all PET data is processed uniformly, then processing is simple, but image quality deteriorates due to mixed composition of true, scattering, and random events
Solution Approach 1:
The patent divides the PET data into distinct segments based on position information criteria, separating true coincident events from scattering and random events. This segmentation enables selective processing where each event type receives appropriate correction, improving image quality while optimizing processing time by not applying complex corrections uniformly to all data
Solution Approach 2:
The patent applies different processing quality levels to different portions of the PET data. True coincident events receive standard processing, while scattering and random events receive specialized correction based on their specific characteristics. This local quality approach improves overall image accuracy without uniformly increasing processing time for all data
Data Source
AI summary
The present disclosure relates to systems and methods for reconstructing a PET image. The systems may execute the methods to acquire PET data of a subject. The PET data may include position information of a plurality of coincident events. The plurality of coincident events may include scattering events and random events. The systems may execute the methods to select a portion of the PET data from the PET data based on the position information. The systems may execute the methods to reconstruct a first preliminary image of the subject based on the selected portion of the PET data, and project the first preliminary image. The systems may execute the methods to may determine, based on the PET data and the projection of the first preliminary image, preliminary correction data relating to the scattering events and the random events.