Virtual Contrast-Enhanced MRI for Generalizable Tumor Delineation
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Solution Overview
Problem
Existing deep learning-assisted gadolinium-free contrast-enhanced MRI (GFCE-MRI) models suffer from low or unknown model generalizability, particularly in nasopharyngeal carcinoma, due to the lack of leveraging complementary information between imaging modalities and varying scanning conditions across different medical institutions.
Innovation Solution
A multi-hospital data-guided neural network (MHDgN-Net) that incorporates a multimodality-guided synergistic neural network (MMgSN-Net) with a mixture model and external data distribution matching method to increase training dataset diversity and minimize intensity variation, using T1-weighted and T2-weighted MRI data from multiple hospitals for generating virtual contrast-enhanced MRI (VCE-MRI).
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If gadolinium-based contrast agents are used to enhance MRI contrast, then tumor-to-normal tissue contrast is improved, but allergic reactions and nephrogenic systemic fibrosis occur
Solution Approach 1:
The patent uses deep learning to generate virtual contrast-enhanced MRI images that copy the appearance and diagnostic value of gadolinium-enhanced images without actually using the contrast agent. The neural network learns the mapping between non-contrast and contrast-enhanced images, creating synthetic images that preserve tumor-to-normal tissue contrast while eliminating gadolinium exposure
Solution Approach 2:
The patent replaces the chemical mechanism of gadolinium-based contrast agents with a computational mechanism using deep learning neural networks. Instead of using chemical substances to enhance contrast, the system uses algorithms to synthesize contrast-enhanced images from non-contrast images, substituting a mechanical/computational system for a chemical one
2Object-affected harmful factors
If existing GFCE-MRI models are used to reduce gadolinium dosage, then gadolinium exposure is reduced, but model generalizability remains low or unknown
Solution Approach 1:
The patent combines multiple data sources including data from different medical institutions, different MRI scanners, and multiple imaging sequences (T1-weighted, T2-weighted, FLAIR) into a unified training dataset. This merging of diverse data sources enables the model to learn robust patterns that generalize across different scanning conditions and institutions
Solution Approach 2:
The patent systematically varies multiple parameters in the training data including scanner manufacturer, field strength (1.5T vs 3T), imaging sequences, and patient demographics. By training on data with diverse parameter configurations, the model learns to generalize across different scanning conditions rather than overfitting to specific parameters
3Ease of manufacture
If deep learning models are trained on single-institution data, then training is simpler, but model generalizability to external institutions deteriorates
Solution Approach 1:
The patent creates a universal training framework that incorporates data from multiple institutions and scanner types, enabling the model to function effectively across diverse external environments. The multi-institutional training approach makes the model universally applicable rather than institution-specific, allowing it to generalize to external hospitals and scanners
Data Source
AI summary
The present disclosure provides a system and method for precision tumor delineation in cancer treatment or diagnosis for subjects with carcinoma, in particular, nasopharyngeal carcinoma, based on contrast agent-free, virtual contrast-enhanced MRI data (VCE-MRI) synthesized by a modified multimodality-guided synergistic neural network trained with a more diversified training dataset and having a higher generalizability by minimizing data distribution variation between an external dataset and the training dataset through a data distribution matching mechanism before VCE-MRI synthesis.