Deep Neural Network Placement of MRI Saturation Bands
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Solution Overview
Problem
Manual placement of saturation bands in MRI imaging is time-consuming and lacks consistency, leading to inefficiencies in suppressing unwanted signals and motion artifacts.
Innovation Solution
Utilizing a deep neural network to automatically generate and place saturation bands on localizer images based on anatomical features, leveraging a convolutional neural network to segment and overlay plane masks, thereby facilitating consistent and accurate saturation band positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual placement of saturation bands is used, then flexibility and adaptability to specific cases are maintained, but time consumption and inconsistency increase
Solution Approach 1:
The system enables automatic self-placement of saturation bands through the deep neural network, which autonomously identifies anatomical structures and determines optimal saturation band positions without requiring manual intervention, thereby resolving the contradiction between manual flexibility and time efficiency
Solution Approach 2:
The patent replaces the manual mechanical process of saturation band placement with an automated computational system using deep neural networks and image processing algorithms, substituting human operator actions with automated digital processing to reduce time while maintaining accuracy
2Adaptability or versatility
If manual placement of saturation bands is used, then operator judgment can be applied, but consistency and accuracy across multiple scans decrease
Solution Approach 1:
The deep neural network system processes localizer images through trained algorithms that provide consistent, repeatable analysis across multiple scans, using feedback from image data to automatically determine saturation band positions with high precision and consistency, eliminating variability introduced by manual placement
Solution Approach 2:
The system transforms the subjective parameter of operator judgment into objective computational parameters through the deep neural network, which analyzes image features and determines saturation band placement based on quantifiable image data, ensuring consistent results across different operators and scans
3Productivity
If automated deep neural network placement is used, then time efficiency and consistency improve, but system complexity increases
Solution Approach 1:
The deep neural network system serves multiple functions including automatic anatomical structure identification, saturation band position determination, and image analysis, consolidating what would otherwise require multiple separate tools or manual steps into a single automated platform, thereby managing complexity while improving productivity
Data Source
AI summary
Methods and systems are provided for automatic placement of at least one saturation band on a medical image, which may direct saturation pulses during a MRI scan. A method may include acquiring a localizer image of an imaging subject, determining a plane mask for the localizer image by entering the localizer image as input to a deep neural network trained to output the plane mask based on the localizer image, generating a saturation band based on the plane mask by positioning the saturation band at a position and an angulation of the plane mask, and outputting a graphical prescription for display on a display device, the graphical prescription including the saturation band overlaid on the medical image.