List Mode Motion Correction in Medical Imaging
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Solution Overview
Problem
Anatomical motion during medical imaging, such as in PET, SPECT, and CT scans, results in motion-averaged images with low quality and limited quantification capabilities, as existing motion correction techniques are complex, time-consuming, and often require external sensors for breathing state data.
Innovation Solution
A method that estimates a characteristic feature of a region of interest within the image from list mode data, corrects the raw data for motion, and reconstructs a motion-corrected image, eliminating the need for physical motion detectors and complex algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional active motion correction schemes (e.g., deformation model) are used, then motion artifact correction is achieved, but device complexity and processing time increase significantly
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) and processes each region independently. This segmentation allows the system to apply motion correction only where needed, reducing overall computational complexity while maintaining correction effectiveness in critical areas.
Solution Approach 2:
The patent extracts motion information directly from the acquired list mode data itself, rather than requiring external sensors or complex deformation models. By taking out and utilizing the breathing state information already present in the acquisition data, the system achieves motion correction without adding external complexity.
2Manufacturing precision
If conventional active motion correction schemes are used, then motion artifact correction is achieved, but acquisition time increases
Solution Approach 1:
The patent performs motion correction during the image reconstruction process itself, rather than as a separate post-processing step. By incorporating motion correction into the reconstruction pipeline, the system eliminates additional processing time that would be required if correction were applied afterward.
Solution Approach 2:
The patent merges the motion correction function with the image reconstruction process. Instead of treating them as separate operations, the system combines them into a unified workflow where motion-corrected images are reconstructed directly from the list mode data in a single processing pass.
3Measurement precision
If external sensors are used for motion correction, then breathing state data is obtained, but device complexity and cost increase
Solution Approach 1:
The patent makes the imaging system self-sufficient by extracting breathing state information directly from the list mode data acquired during the scan. The system uses its own acquisition data to determine patient motion, eliminating the need for external sensors and making the system self-correcting.
Solution Approach 2:
The patent enables the list mode data to serve multiple functions: it is used both for image reconstruction and for determining breathing state information for motion correction. This multi-functionality eliminates the need for separate sensing systems while maximizing the utility of the acquired data.
Data Source
AI summary
A method for locally correcting motion in an image reconstructed by a reconstruction system (42) of an imaging system (10) with raw data includes estimating a characteristic feature of a region of interest within the reconstructed image from the raw data, correcting the raw data associated with the region of interest for motion with the estimated region characteristic feature, and reconstructing a motion-corrected image corresponding to the region of interest with the corrected raw data.