Contrast Agent-Free Virtual Contrast-Enhanced Magnetic Resonance Imaging Apparatus and Data Preprocessing Apparatus and Method thereof
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Solution Overview
Problem
The challenge of generalizing machine learning models for virtual contrast-enhanced magnetic resonance imaging (VCE-MRI) across different institutions is hindered by data privacy regulations, and existing methods struggle to utilize large-scale, heterogeneous data from diverse MRI devices, limiting the model's applicability and accuracy.
Innovation Solution
A contrast agent-free VCE-MRI apparatus and method utilizing federated learning (FL) to train a multimodal guided collaborative neural network (MMgSN-Net) on highly heterogeneous multi-center data from various MRI devices, ensuring patient data privacy by preprocessing and anonymizing data, and employing a data preprocessing apparatus to standardize and register MRI sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning models are trained on large-scale multi-institutional data to improve generalization, then model accuracy and applicability improve, but data privacy regulations prevent cross-institutional data sharing
Solution Approach 1:
The system segments data processing by training local models at each institution with their own data, then aggregating model parameters rather than sharing raw data. This allows each institution to maintain data privacy while collectively improving model generalization through parameter fusion from multiple centers.
Solution Approach 2:
A central server acts as an intermediary to aggregate model parameters from multiple institutions without accessing or storing raw patient data. The server collects updated model parameters from each institution, merges them to create a global model, and distributes this back to institutions, enabling collaboration while preserving data privacy.
2Adaptability or versatility
If data from heterogeneous MRI devices from multiple institutions is collected to enhance model generalization, then model applicability improves, but data preprocessing complexity increases due to varying imaging parameters
Solution Approach 1:
The system applies parameter transformation to convert MRI images from different scanning parameters and devices into a unified parameter space. By adjusting and normalizing imaging parameters such as contrast, brightness, and resolution, the system creates standardized representations that can be processed consistently across heterogeneous devices.
Solution Approach 2:
The neural network model is designed with universal feature extraction capabilities that can handle diverse MRI data from multiple institutions and devices. The model learns invariant features that are consistent across different imaging protocols, enabling it to generalize to new data from unseen devices and centers.
3Object-affected harmful factors
If federated learning is used to train multi-institutional models without sharing data, then patient privacy is protected, but model training efficiency decreases due to distributed computation requirements
Solution Approach 1:
The system merges model parameters from multiple distributed institutions into a single aggregated global model. By combining the learned representations from each institution's local data, the system achieves the computational efficiency of centralized training while maintaining the privacy benefits of distributed data access.
Solution Approach 2:
The federated learning system implements feedback loops where the central server receives updated model parameters from institutions, aggregates them to create an improved global model, and sends this back to institutions for further training. This iterative feedback process enables continuous model improvement while maintaining data privacy through distributed computation.
Data Source
AI summary
Apparatus and method for contrast agent-free virtual contrast-enhanced magnetic resonance imaging (VCE-MRI) based on a VCE-MRI model of federated learning (FL) are provided. The VCE-MRI model is trained using large-scale, highly heterogeneous multi-center data for data of nasopharyngeal carcinoma (NPC) patients, protecting patient data privacy while guaranteeing high generalization of the model. Apparatus and method for preprocessing VCE-MRI data are also provided. In the preprocessing of the data, a training dataset and/or a test dataset suitable for the FL model from patient data from different medical institutions to be used for model training and/or local verification of the FL model are obtained, respectively. The results of clinical evaluation show that the VCE-MRI model developed in the present application has high generalization and high clinical use value.