PET Motion Correction via ML Time-Activity Curve Adjustment
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Solution Overview
Problem
PET imaging devices face challenges in motion correction during scanning, leading to compromised image quality and pharmacokinetic analysis accuracy due to object movement during the lengthy scanning process.
Innovation Solution
A method and system for motion correction of PET images using machine learning models to determine corrected time-activity curves and kinetic parameters, which correct for object motion by processing scanned images at multiple time points, improving image quality and analysis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PET scanning is performed for a long time to ensure adequate data collection, then measurement precision is improved, but object movement during scanning causes motion artifacts that deteriorate image quality
Solution Approach 1:
The system performs preliminary motion detection and correction by establishing a motion model before final image reconstruction. Motion parameters are estimated from the scanned images themselves, and correction is applied to the time-activity curves before pharmacokinetic analysis, preventing motion artifacts from degrading the final results
Solution Approach 2:
The system uses feedback from the scanned images to detect motion and adjust correction parameters. By monitoring changes in time-activity curves and comparing them against expected patterns, the system identifies motion events and applies appropriate correction factors to maintain image quality throughout the extended scanning period
2Reliability
If motion correction is applied to scanned images, then image quality is improved, but computational complexity increases due to additional processing steps
Solution Approach 1:
The system extracts motion information directly from the scanned images by analyzing time-activity curves, separating the motion component from the pharmacokinetic signal. This extracted motion data is then used to correct the images, avoiding the need for complex external motion tracking hardware while maintaining correction effectiveness
Solution Approach 2:
The system changes parameters of the time-activity curves by applying motion correction factors derived from the motion model. By adjusting kinetic parameters and activity values based on detected motion, the system corrects image quality issues without requiring complete reprocessing of the raw scan data
3Measurement precision
If motion correction processing is performed on scanned images, then pharmacokinetic analysis accuracy is improved, but processing time increases
Solution Approach 1:
The system performs motion detection and correction parameters estimation in advance during the scanning process itself, rather than as a separate post-processing step. By preparing correction factors while data is being collected, the system minimizes additional processing time after scanning is complete
Solution Approach 2:
The motion correction process operates continuously throughout the scanning procedure, with correction parameters being updated and applied in real-time to time-activity curves. This continuous processing ensures that pharmacokinetic analysis accuracy is maintained without requiring a separate batch processing stage
Data Source
AI summary
The present disclosure provides systems and methods for motion correction of a positron emission computed tomography (PET) image. The method may include: obtaining scanned images of a scanned object generated at a plurality of time points; and determining a parametric image by performing a correction processing on the scanned images, wherein the correction processing may be configured to correct an influence of a motion of the scanned object on the scanned images.