Patient Motion Detection in Diagnostic Imaging via Frame Correlation
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Solution Overview
Problem
Patient movement during PET-CT scans can lead to reduced diagnostic quality and increased resource utilization due to the need for re-scans and image correction, as existing methods lack real-time monitoring and automatic assessment of gross patient motion.
Innovation Solution
A method and system for automatically detecting gross patient motion by acquiring spatial and temporal frames of image data, positioning time windows, calculating statistical correlation values, and comparing their derivatives to threshold values to indicate motion, utilizing a diagnostic medical imaging system with a scanner, processor, and display to output motion indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring by technologist is used, then patient motion can be detected, but diagnostic quality deteriorates due to blurring and increased re-scan requirements
Solution Approach 1:
The imaging system automatically monitors patient motion using its own acquired image data without requiring external manual monitoring. The system processes PET and CT images to detect motion through correlation analysis, enabling self-monitoring that maintains diagnostic quality while improving scan efficiency.
Solution Approach 2:
The manual mechanical monitoring process is replaced with an automated computational system that uses image processing algorithms. The system calculates correlation values between image frames to detect motion, substituting human observation with automated digital analysis.
2Manufacturing precision
If real-time motion detection is implemented, then image quality is maintained, but system complexity increases
Solution Approach 1:
The system uses the existing PET and CT imaging infrastructure to perform motion detection as an additional function. By utilizing the same image acquisition and processing capabilities already present in the system, motion detection is integrated without requiring separate dedicated hardware, thus reducing overall system complexity.
Solution Approach 2:
The system detects motion by changing the parameter of image correlation over time. It calculates correlation values between sequential image frames and monitors changes in these values to identify motion events, using mathematical parameter transformation rather than complex hardware modifications.
3Productivity
If automated motion assessment is used, then re-scan requirements are reduced, but measurement precision must be ensured
Solution Approach 1:
The system continuously monitors correlation values between image frames and provides real-time feedback on patient motion status. When motion is detected beyond a threshold, the system can trigger alerts or adjustments, ensuring accurate motion assessment that reduces unnecessary re-scans while maintaining measurement precision through iterative monitoring.
Data Source
AI summary
Methods and systems for automatically detecting gross patient motion using a diagnostic medical imaging system are provided. The method provides for acquiring a plurality of frames of image data, positioning a first time window and a second time window over overlapping frames of image data, calculating a statistical correlation value based on the first time window and the second time window, and comparing a first derivative of the statistical correlation value to a threshold value to determine patient motion.