Cross-Modal Image Registration for Real-Time 2D-3D Alignment
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Solution Overview
Problem
Existing image registration technologies face challenges in efficiently aligning multi-modal images with significant intensity and texture differences, particularly when dealing with 2D and 3D images, leading to high error rates and inefficiencies in clinical applications.
Innovation Solution
An end-to-end machine learning approach using cross-modal attention blocks and deep registration modules to align multi-modal images, incorporating feature extractors, cross-modal attention, and deep registrators to generate registered images, with optional contrastive pre-training for improved alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external hardware tracking systems (electromagnetic or optical tracking) are used for 2D/3D image registration, then registration accuracy can be maintained, but device complexity and cost increase significantly
Solution Approach 1:
The patent replaces external hardware tracking systems (electromagnetic or optical tracking devices) with a software-based deep learning model. The neural network processes image data directly to perform 2D/3D registration, eliminating the need for complex external tracking hardware while maintaining registration accuracy through learned feature correspondences between 2D ultrasound images and 3D MRI volumes.
2Measurement precision
If iterative optimization methods are used for slice-to-volume registration, then similarity metric optimization can be achieved, but processing efficiency is low and not suitable for intra-procedural use
Solution Approach 1:
The patent replaces iterative optimization algorithms with a trained deep learning model that performs registration in a single forward pass. The neural network has learned the registration transformation during training, allowing it to rapidly predict accurate alignments without requiring repeated iterations during intra-procedural use, thus achieving both precision and speed.
Solution Approach 2:
The patent performs the computationally intensive optimization work during the offline training phase, where the deep learning model learns optimal registration transformations from labeled training data. Once trained, the model can perform rapid inference during clinical procedures without requiring iterative optimization, as the registration knowledge has been pre-computed and embedded in the network weights.
3Extent of automation
If convolutional neural networks are used for multi-modal image registration, then automated feature extraction can be achieved, but intensity and texture differences between modalities lead to high error rates
Solution Approach 1:
The patent employs a multi-scale architecture that processes images at different spatial resolutions and feature hierarchies. By extracting features at multiple scales and combining them through skip connections, the network can capture both fine-grained local correspondences and coarse global structures, improving robustness to intensity and texture variations between different imaging modalities.
Solution Approach 2:
The patent introduces an attention mechanism as an intermediary component that learns to weigh and select the most relevant features from each modality. The attention mechanism helps the network focus on discriminative features that are consistent across modalities while suppressing modality-specific variations in intensity and texture, thereby improving registration accuracy.
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.