MRI Image Processing Using Guided Enhancement Across Imaging Conditions
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Solution Overview
Problem
Existing MRI image processing techniques using machine learning face accuracy issues when dealing with images acquired under different imaging conditions, as they require extensive data preparation for each condition, leading to inefficiencies and reduced accuracy.
Innovation Solution
An image processing device and method that utilizes a first high image quality processing unit to enhance a first image, followed by a second high image quality processing unit using the enhanced image as a guide to improve a second image, regardless of the imaging conditions, employing convolutional neural networks and filters like guided and joint bilateral filters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a single learning model is used for high image quality processing, then processing speed is improved, but accuracy deteriorates when imaging conditions differ from training conditions
Solution Approach 1:
The patent applies universality by creating a unified processing system that handles multiple image types (T1-weighted, T2-weighted, FLAIR images) with different imaging conditions through a single integrated architecture. The system uses a shared base network for common feature extraction and adds type-specific modules only when needed, allowing one system to universally process diverse MRI sequences without requiring separate learning models for each condition.
2Measurement precision
If separate learning models are prepared for each imaging condition, then accuracy is improved, but device complexity and data preparation requirements increase
Solution Approach 1:
The patent merges multiple processing approaches into a single unified system. It combines a shared base network that handles common features across all image types with optional type-specific processing modules. This merging allows the system to maintain high accuracy for different imaging conditions while avoiding the complexity of completely separate learning models, as the shared components handle the majority of processing universally.
Solution Approach 2:
The patent segments the processing system into a shared base network and optional type-specific modules. The base network handles common feature extraction applicable to all MRI types, while specific modules are activated only when particular image types require specialized processing. This segmentation reduces overall system complexity compared to having fully separate models for each imaging condition.
3Measurement precision
If extensive data preparation is performed for each imaging condition, then learning accuracy is improved, but time consumption and resource requirements increase
Solution Approach 1:
The unified learning model is trained to handle multiple imaging conditions universally, reducing the need for extensive separate data preparation for each condition. By learning common patterns across different MRI types through shared processing components, the system achieves good accuracy with less condition-specific training data compared to preparing separate models for each imaging scenario.
Data Source
AI summary
A learning model learned to provide high image quality of a first image is generated. A first image and a second image are received from the same target, high image quality of the first image is provided by using the learned model, and a first high image quality image is obtained. By using the first high image quality image and the second image as inputs, a high image quality image of the second image having the image quality of the first high image quality image is generated while maintaining contrast of the second image.