PET Couch Position Offset via Activity Distribution
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current PET systems face challenges in efficiently determining the position information of a moving couch during scanning, which is essential for filtering out incorrect lines of response and normalizing efficiency, as existing methods require additional devices and process large amounts of data.
Innovation Solution
A system and method that utilize a PET dataset to determine the activity distribution of a tracer species over time segments, allowing for the calculation of couch offsets by registering distribution curves, thereby eliminating the need for additional devices and reducing data processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If additional devices are used to determine couch position, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The PET system uses its own acquired PET data to determine couch position information without requiring external measurement devices. The system processes coincidence events and activity distribution data that are already collected during normal PET scanning to calculate couch offset, making the system self-sufficient and eliminating additional hardware requirements
Solution Approach 2:
The PET data acquisition system serves dual purposes: it collects data for both image reconstruction and couch position determination. The same coincidence events and activity distribution data used for medical imaging are also utilized to calculate couch offset, allowing one system to perform multiple functions simultaneously
2Measurement precision
If all PET data is processed to determine couch position, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The method extracts only the necessary subset of PET data for couch position determination, specifically using coincidence events and activity distribution information from selected time segments rather than processing the entire PET dataset. This selective extraction reduces computational burden while maintaining sufficient accuracy for couch positioning
Solution Approach 2:
The PET scanning time period is divided into multiple time segments, and couch position is determined using data from specific segments rather than the complete scan. This segmentation allows the system to process smaller, manageable portions of data while still achieving accurate couch position measurement
3Productivity
If time segment length is increased, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system dynamically adjusts the time segment length based on the actual couch moving speed. When the couch moves faster, shorter time segments are used to maintain measurement precision; when the couch moves slower, longer time segments can be used to improve productivity. This dynamic adaptation optimizes the balance between scanning efficiency and measurement accuracy
Data Source
AI summary
The present disclosure may provide a method. The method may include obtaining a PET dataset relating to a subject acquired, based on a tracer species in the subject, by a PET device. The PET dataset relating to the subject may be acquired for a time period when the subject moves by moving a couch on which the subject is supported. The time period may include a first time segment and a second time segment. The method may also include determining an activity distribution set of the tracer species in the subject within the time period. The activity distribution set may include a plurality of segment activity distributions. Further, the method may include determining an offset of the couch within the second time segment with respect to the first time segment based on the activity distribution set of the tracer species.