Machine Learning Motion Prediction for Physiological Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical imaging technologies face challenges in accurately measuring physiological motion, particularly in regions of interest like the heart, due to limitations in existing methods for tracking and analyzing motion phases, which can impact the accuracy of disease diagnosis and treatment.
Innovation Solution
A system utilizing a motion prediction model, trained through supervised or unsupervised learning techniques, acquires reference and target images of a region of interest to identify feature points and determine motion fields, enabling precise physiological condition assessment based on these motion fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to track physiological motion, then the measurement process is simpler, but the measurement precision is insufficient for accurate disease diagnosis
Solution Approach 1:
The patent replaces traditional mechanical image processing methods with a machine learning-based motion prediction model. The model takes reference and target images as input and directly predicts the motion field, eliminating the need for complex traditional tracking algorithms while achieving superior measurement precision for physiological motion.
Solution Approach 2:
The patent changes the approach from extracting motion information through complex image processing parameters to using a machine learning model that directly predicts motion fields. This parameter transformation enables more accurate physiological motion measurement while simplifying the overall system architecture.
2Adaptability or versatility
If predefined shape models are used for motion analysis, then the analysis framework is more structured, but the adaptability to different physiological conditions is reduced
Solution Approach 1:
The patent employs a universal motion prediction model that can analyze various physiological motions (cardiac, respiratory, etc.) without requiring predefined shape models for each specific condition. The model's multi-functionality allows it to adapt to different physiological states and anatomical variations, improving versatility while maintaining a unified analysis framework.
3Productivity
If manual feature point identification is used, then the measurement process is more controllable, but the productivity is reduced due to time-consuming manual intervention
Solution Approach 1:
The patent implements a self-service system where the motion prediction model automatically identifies and tracks physiological motion features without requiring manual intervention. The model processes reference and target images autonomously to generate motion fields, significantly improving productivity while maintaining ease of operation through automated feature extraction and analysis.
Data Source
AI summary
A system for physiological motion measurement is provided. The system may acquire a reference image corresponding to a reference motion phase of an ROI and a target image of the ROI corresponding to a target motion phase, wherein the reference motion phase may be different from the target motion phase. The system may identify one or more feature points relating to the ROI from the reference image, and determine a motion field of the feature points from the reference motion phase to the target motion phase using a motion prediction model. An input of the motion prediction model may include at least the reference image and the target image. The system may further determine a physiological condition of the ROI based on the motion field.