3D TOF MRA Lesion Detection With CNN-RNN Cerebrovascular Analysis
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Solution Overview
Problem
Conventional image analysis methods for diagnosing cerebrovascular diseases using deep learning are inaccurate due to reliance on local features, requiring significant user manipulation and varying results based on skill level, and 2D or 3D MRA methods are time-consuming and dependent on user interaction.
Innovation Solution
A cerebrovascular disease detection apparatus and method utilizing a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) to analyze 3D time-of-flight magnetic resonance angiography (3D TOF MRA) data, constructing models to identify space, frame, and lesion characteristics, minimizing user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning image analysis is used, then local features can be extracted, but recognition accuracy deteriorates when actual content differs from local information form
Solution Approach 1:
The patent transitions from 2D image analysis to 3D volumetric analysis by processing MRI scans as three-dimensional datasets. This dimensional change enables the system to capture spatial relationships and anatomical context that are lost in conventional 2D slicing, thereby improving recognition accuracy for cerebrovascular diseases while maintaining manageable model complexity through structured 3D processing
2Reliability
If 2D or 3D MRA manipulation is performed by users, then diagnosis can be made, but diagnosis time increases and results vary by skill level
Solution Approach 1:
The patent implements an automated deep learning system that performs diagnosis independently without requiring user manipulation of MRA images. The system processes 3D TOF MRA data automatically, extracting features and making diagnoses consistent with minimal human intervention, thereby eliminating variability based on skill level and reducing diagnosis time while maintaining high reliability
Solution Approach 2:
The patent replaces the mechanical interaction between users and MRA images (manual manipulation, zooming, rotating) with an automated computational system. The deep learning model substitutes human expertise with algorithmic processing that consistently applies the same diagnostic criteria, eliminating the variability introduced by different user skill levels and reducing overall diagnosis time
3Adaptability or versatility
If deep learning learns large amounts of data, then adaptive operation improves, but data processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and structuring the 3D TOF MRA data into a standardized format before feeding it to the deep learning model. This includes automatic orientation, segmentation into vascular and non-vascular regions, and normalization, which prepares the data for efficient processing and enables the model to operate adaptively without requiring extensive real-time data processing time
Data Source
AI summary
A cerebrovascular disease learning apparatus may be provided. The cerebrovascular disease learning apparatus may include a maximum intensity projection magnetic resonance angiography (MIP MRA) configuration unit configured to receive 3D time-of-flight magnetic resonance angiography (3D TOF MRA) and to construct MIP MRA including a plurality of image frames, a space characteristic learning unit configured to construct a space characteristic learning model, a frame characteristic learning unit configured to receive the space characteristics and to construct a frame characteristic learning model based on a recurrent neural network (RNN) learning method, and a lesion characteristic learning unit configured to receive the frame characteristics and to construct a lesion characteristic learning model based on the CNN learning method.