MRI Saturation Band Placement Using Deep Neural Segmentation
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Solution Overview
Problem
Manual placement of saturation bands in MRI imaging is time-consuming and inconsistent, 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 for precise band positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual placement of saturation bands is used, then flexibility and adaptability are maintained, but time consumption increases and consistency deteriorates
Solution Approach 1:
The patent replaces the manual mechanical process of saturation band placement with an automated deep neural network system. The CNN automatically analyzes localizer images and generates saturation band prescriptions, eliminating the need for manual intervention while maintaining accuracy and consistency in band placement.
Solution Approach 2:
The system performs self-service by automatically processing localizer images and generating saturation band prescriptions without requiring operator intervention. The deep neural network independently completes the analysis and prescription generation, reducing both time consumption and variability in placement.
2Manufacturing precision
If manual placement of saturation bands is used, then operator judgment is applied, but consistency and accuracy deteriorate
Solution Approach 1:
The patent substitutes manual operator judgment with an automated deep neural network that consistently applies learned patterns from training data. The CNN processes localizer images and generates precise saturation band prescriptions with high accuracy, eliminating the variability inherent in manual placement while reducing time requirements.
Solution Approach 2:
The system incorporates feedback mechanisms where the deep neural network continuously refines its predictions based on training data and performance metrics. This feedback loop ensures consistent accuracy in saturation band placement by learning from previous successes and correcting errors, thereby maintaining high precision without increasing time consumption.
3Reliability
If automated placement using deep neural network is implemented, then consistency and speed improve, but system complexity increases
Solution Approach 1:
The patent replaces complex manual processes with a deep neural network that achieves consistent saturation band placement through learned patterns. The CNN architecture, trained on extensive data, provides reliable and repeatable results, maintaining consistency across different operators and patients while automating the placement process.
Solution Approach 2:
The system manages complexity by optimizing neural network parameters and architecture to achieve reliable performance. Through careful tuning of model parameters, training data characteristics, and computational resources, the system achieves consistent saturation band placement while controlling the complexity of the underlying neural network infrastructure.
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.