AI MRI Coil Positioning via 3D Depth Camera
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MRI systems face challenges in obtaining high-quality images due to operator errors in subject positioning and coil placement, leading to frequent image rejections, especially for complex body regions like the chest, abdomen, and spine, which result in inefficiencies and increased patient discomfort.
Innovation Solution
An AI-based deep learning module utilizing a 3D depth camera to identify table boundaries, key anatomical points, and coil orientations, ensuring accurate positioning and orientation of subjects within MRI systems, thereby guiding operators for optimal coil placement and reducing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operator manually positions subject and places coils, then flexibility and adaptability are maintained, but positioning accuracy and image quality deteriorate due to human error
Solution Approach 1:
The system performs self-positioning by using the depth camera to automatically detect subject anatomy, determine optimal coil placement locations, and guide the positioning process without requiring manual measurement or expert operator intervention. The AI module autonomously completes tasks that would otherwise require skilled human operators.
Solution Approach 2:
The patent replaces manual mechanical positioning operations with an automated vision-based system. The depth camera captures spatial information, and the AI module processes this data to determine positioning, substituting the mechanical/manual positioning process with an optical-digital-intelligence system.
2Reliability
If multiple imaging scans are performed to ensure quality, then image quality improves, but time consumption and patient discomfort increase
Solution Approach 1:
The system performs preliminary positioning verification using the depth camera before the actual MRI scanning begins. By detecting anatomical landmarks and confirming correct coil placement in advance, the system ensures that the subsequent scan will produce high-quality images, eliminating the need for repeat scans.
Solution Approach 2:
The system provides real-time feedback during the positioning process by analyzing depth images and comparing detected anatomy with expected anatomical structures. This feedback mechanism allows operators to immediately correct positioning errors before scanning, preventing the need for re-scanning.
3Measurement precision
If advanced deep learning models are used for anatomy identification, then positioning precision improves, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential anatomical features needed for positioning from the depth images using the deep learning model. Rather than performing full anatomical analysis, the system identifies key landmarks and structural relationships required for coil placement, reducing computational burden while maintaining positioning accuracy.
Data Source
AI summary
Systems and methods for automated patient anatomy and orientation identification using an artificial intelligence (AI) based deep learning module are provided. The method comprises positioning a subject over a table of a magnetic resonance imaging (MRI) system and wrapping at least one radiofrequency (RF) imaging coil over the subject. The method comprises obtaining a plurality of depth images, color images and infrared images of the subject using a three-dimensional (3D) depth camera and identifying the table boundary of the MRI system using the images obtained by the 3D camera. The method further comprises identifying a location of the subject over the table to determine if the subject is positioned within the table boundary of the MRI system and identifying a plurality of key anatomical points or regions corresponding to a plurality of organs of the subject body. The method further comprises identifying all DICOM orientations of the subject over the table of the MRI system and identifying the coils of the MRI system wrapped around the subject body and determining the orientation of the subject with respect to the coils of the MRI system, hospital gown, and blankets. The method further comprises identifying the anatomical key points occluded by the coils of the MRI system, hospital gown, and blankets to determine accurate positioning of the coils of the MRI system over the subject anatomy for automated landmarking of anatomies and imaging.