Motion Classification Model for Medical Imaging Subject Motion Detection
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Solution Overview
Problem
Subject motion during medical imaging leads to image blurring and artifacts, resulting in decreased efficiency and increased costs due to repeated scans, and existing motion correction algorithms are not effective in preventing misdiagnosis.
Innovation Solution
A method for detecting subject motion using a motion classification model that represents relationships between image features and subject motion values, extracted from medical slice images, to determine the presence and severity of subject motion, allowing for preventative or corrective actions to avoid repeat scans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If subject motion detection is not performed, then imaging process remains simple, but image quality deteriorates due to blurring and artifacts
Solution Approach 1:
The system performs preliminary motion detection by extracting features from navigator echoes before the main imaging sequence completes. This early detection allows motion assessment to be done in advance, enabling corrective actions without delaying the overall imaging process significantly.
Solution Approach 2:
The patent uses navigator echoes as an intermediary element to detect subject motion. These low-bandwidth signals serve as a mediator between the subject's motion and the imaging system's response, providing motion information without requiring full imaging sequences.
2Manufacturing precision
If motion correction algorithms are applied, then image quality may improve, but processing time and complexity increase
Solution Approach 1:
Motion detection and classification are performed preliminarily using navigator echoes before the main imaging data is fully acquired. This allows the system to identify motion artifacts early and determine whether correction is needed, avoiding unnecessary processing time for images that don't require correction.
Solution Approach 2:
The system applies motion correction selectively based on the detected motion characteristics. Not all images undergo the same level of correction - the processing intensity is adjusted locally based on the specific motion detected in each navigator echo, optimizing the balance between quality and processing time.
3Reliability
If repeated scans are performed due to motion artifacts, then diagnostic accuracy may be maintained, but efficiency decreases and costs increase
Solution Approach 1:
The system implements a feedback mechanism where navigator echoes continuously monitor subject motion during the imaging process. This real-time feedback allows the system to detect motion artifacts as they occur and trigger appropriate corrective actions, preventing the need for complete repeat scans and maintaining diagnostic accuracy while improving efficiency.
Solution Approach 2:
The imaging system performs self-monitoring and self-correction for motion artifacts. By automatically detecting motion through navigator echoes and applying corrections without requiring external intervention or complete rescan, the system maintains diagnostic reliability while minimizing efficiency losses.
Data Source
AI summary
Presented are concepts for detecting subject motion in medical imaging of a subject. One such concept obtains a motion classification model representing relationships between motion of image features and subject motion values. For each of a plurality of medical slice images of an imaged volume of the subject, an image feature of the medical slice image is extracted. Based on the extracted image feature for each of the plurality of medical slice images, motion information for the image feature is determined. Based on the motion information for the image feature and the obtained motion classification model, a subject motion value is determined.