Deep Cross-Modal Attention for 2D–3D Image Registration
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Solution Overview
Problem
Existing multi-modal image registration techniques, particularly in medical imaging, face challenges due to differences in intensity and texture between imaging modalities like MRI and ultrasound, and the dimensionality mismatch between 2D and 3D images, leading to inefficiencies and high error rates, especially in intra-procedural applications.
Innovation Solution
An end-to-end machine learning approach using cross-modal attention blocks and deep registration methods to align multi-dimensional images, employing feature extractors, cross-modal attention, and deep registrators to generate registered images without external tracking devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep neural networks with convolutional layers are used to find anatomical correspondence between imaging modalities, then the system can handle multi-modal image differences, but the network complexity increases and error levels rise
Solution Approach 1:
The patent segments the image registration task into distinct processing stages: a first neural network extracts features from the first image modality, a second neural network extracts features from the second image modality, and a third neural network performs the actual registration. This segmentation allows each network to be specialized and simpler than a single monolithic network, reducing overall complexity while maintaining adaptability to handle intensity and texture differences between modalities.
2Measurement precision
If iterative optimization methods are used for slice-to-volume registration, then the similarity metric can be optimized, but the processing efficiency decreases and the method becomes unsuitable for intra-procedural use
Solution Approach 1:
The patent replaces the traditional iterative mechanical optimization process with a direct neural network-based registration approach. The third neural network directly computes the optimal transformation parameters by learning from training data, eliminating the need for iterative optimization loops. This substitution maintains measurement precision through learned similarity metrics while dramatically improving productivity to enable real-time intra-procedural registration.
Solution Approach 2:
The system performs preliminary training of the neural networks using training datasets before actual registration. During this preliminary phase, the networks learn optimal feature extraction and registration strategies. When deployed for actual slice-to-volume registration, the pre-trained networks execute quickly without requiring iterative optimization, thus maintaining precision while achieving the speed needed for intra-procedural applications.
3Adaptability or versatility
If external hardware tracking systems are used to deal with dimensionality difference between 2D and 3D images, then the registration can be performed, but the device complexity and cost increase
Solution Approach 1:
The patent replaces external hardware tracking systems with a purely software-based neural network approach. The neural networks process the dimensional differences between 2D and 3D images through learned feature representations and transformation models, eliminating the need for electromagnetic trackers, optical markers, or other external hardware. This substitution maintains the ability to handle dimensionality mismatch while significantly reducing device complexity and cost.
Data Source
AI summary
A method of multi-modal image registration is provided. The method includes receiving as input a fixed image from a first imaging device, receiving as input a moving image from a second imaging device, performing feature extraction on the fixed image via a first feature extractor to generate a fixed image feature map, performing feature extraction on the moving image via second feature extractor to generate a moving image feature map, performing cross-modal attention on the fixed image feature map and the moving image feature map to generate cross-modal feature attention data, performing deep registration on the cross-modal feature attention data via a deep registrator, and outputting a multi-modal registered image.