CNN and GAN Synthesis of Multi-Modality MRI Images
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Solution Overview
Problem
Conventional MRI scanning is time-consuming and inefficient, requiring prohibitively long durations for acquiring images in different modalities, with current multi-modality imaging techniques facing limitations in reconstruction accuracy and throughput.
Innovation Solution
A method and system utilizing a Convolutional Neural Network (CNN) and Generative Adversarial Networks (GAN) to simulate and construct MRI images in a second modality from a source image in the first modality, achieving pixel-level accuracy and reducing scan time by preprocessing images and using an ensemble of neural networks for enhanced image clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional MRI scanning is used to acquire images in multiple modalities, then comprehensive diagnostic information is obtained, but the scanning time becomes prohibitively long
Solution Approach 1:
The patent uses a Convolutional Neural Network (CNN) to generate synthetic MRI images in different modalities by learning the mapping between modalities from training data. The CNN creates copies of the diagnostic information in one modality that approximate the appearance and diagnostic content of actual images in other modalities, eliminating the need to physically scan all modalities
Solution Approach 2:
The system performs preliminary training of the CNN model using paired MRI images from different modalities before actual use. This preliminary action allows the model to learn the complex relationships between modalities in advance, enabling rapid generation of synthetic images during actual clinical use without performing all scans
2Adaptability or versatility
If current multi-modality imaging techniques are used, then multiple modalities are acquired, but reconstruction accuracy is insufficient
Solution Approach 1:
The patent transforms the image data through learned parameter transformations in the CNN model. The network learns to map intensity distributions, contrast characteristics, and texture patterns from one modality to another by adjusting multiple parameters simultaneously, achieving accurate reconstruction that preserves diagnostic features
Solution Approach 2:
The system uses a loss function that compares generated synthetic images with actual reference images from different modalities, providing feedback to the CNN during training. This feedback mechanism continuously refines the model's ability to accurately reconstruct images by minimizing the difference between synthetic and real images across multiple metrics
3Reliability
If conventional MRI scanning procedures are followed for each modality series, then complete imaging data is collected, but the throughput is reduced
Solution Approach 1:
The CNN generates synthetic copies of complete multi-modality imaging data from a single acquired modality, providing comprehensive diagnostic information equivalent to complete imaging data from multiple scans while processing only one actual scan, thereby dramatically increasing throughput
Data Source
AI summary
The present invention relates to an improved method and system for simulating and constructing original actual Magnetic Resonance Images MRI from first modality of a patient to second modality, wherein the system is configured to receive an input MRI image taken in first modality, pre-process the input MRI image, send the processed image to a Convolutional Neural Network (CNN), and obtain the new constructed MRI images in second modality that are identical at the pixel level to the actual image as captured by the MRI machines.