Multi-Contrast MRI Reconstruction via Joint Neural Network Optimization
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Solution Overview
Problem
Conventional MRI techniques treat each contrast independently during sampling and reconstruction, leading to sub-optimal outcomes in terms of reconstruction quality and acquisition time for multi-contrast MRI studies.
Innovation Solution
The use of an artificial neural network (ANN) to jointly determine and optimize sampling patterns and reconstruct multi-contrast MRI images by sharing information across contrasts, leveraging characteristics from one contrast to improve the reconstruction of another.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional independent sampling and reconstruction techniques are used for each contrast, then optimal results for each individual contrast are achieved, but the overall MRI study becomes sub-optimal in terms of reconstruction quality and acquisition time
Solution Approach 1:
The patent combines multiple contrast images into a multi-contrast image stack and processes them together through a unified neural network architecture. The encoder processes all contrasts simultaneously, and the decoder generates multiple contrast images from shared latent representations, enabling information sharing across contrasts to improve overall reconstruction quality while reducing acquisition time.
Solution Approach 2:
The neural network is designed with multi-functionality to handle multiple contrast types simultaneously. The encoder-decoder architecture with skip connections serves as a universal framework that can process different contrast images (T1, T2, FLAIR, etc.) using shared computational resources and learned features, making the system adaptable to various contrast combinations without requiring separate independent processing for each contrast.
2Reliability
If independent reconstruction is performed for each contrast, then each individual contrast image is optimized, but information sharing among contrasts is not leveraged
Solution Approach 1:
Multiple contrast images are merged into a single multi-contrast input tensor that is processed by a unified encoder network. The encoder extracts shared features from all contrasts simultaneously, and the decoder reconstructs all contrast images from these shared features, ensuring consistency across contrasts while avoiding the need for multiple independent reconstruction systems.
Solution Approach 2:
The reconstruction system is segmented into distinct functional modules: an encoder that extracts shared features, a bottleneck layer that compresses information, and decoders for each contrast type that generate final images. Skip connections are segmented to preserve high-frequency details. This modular segmentation manages system complexity by organizing the multi-contrast processing into manageable, reusable components.
3Productivity
If multiple contrasts are acquired independently, then each contrast can be optimized separately, but the overall study efficiency decreases
Solution Approach 1:
The neural network performs continuous processing of all contrast images simultaneously through the encoder-decoder framework. The shared latent representations and skip connections enable continuous information flow and feature sharing across all contrasts during a single forward pass, maintaining reconstruction precision while significantly improving study efficiency by eliminating sequential processing overhead.
Data Source
AI summary
Described herein are systems, methods, and instrumentalities associated with reconstruction of multi-contrast magnetic resonance imaging (MRI) images. The reconstruction may be performed based on under-sampled MRI data collected for the multiple contrasts using corresponding sampling patterns. The sampling patterns and the reconstruction operations for the multiple contrasts may be jointly optimized using deep learning techniques implemented through one or more neural networks. An end-to-end reconstruction optimizing framework is provided with which information collected while processing one contrast may be stored and used for another contrast. A differentiable sampler is described for obtaining the under-sampled MRI data from a k-space and a novel holistic recurrent neural network is used to reconstruct MRI images based on the under-sampled MRI data.