Example-Based Affine Registration for Medical Imaging
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Solution Overview
Problem
Optimization-based affine registration methods are sensitive to initialization and prone to local optimal solutions, leading to inconsistent results when registering similar images to the same target image.
Innovation Solution
An example-based approach for affine registration, where the affine transformation for a test image is determined by a weighted linear combination of affine transformations from similar training images, providing a more robust initialization for optimization-based methods and reducing the risk of converging to local optimum solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If optimization-based affine registration methods are used, then alignment can be achieved, but the results are sensitive to initialization and prone to local optimal solutions
Solution Approach 1:
The patent applies preliminary action by pre-computing affine transformations from multiple training images to a reference image before processing the test image. These pre-computed transformations are stored and later combined through weighted linear combination to initialize the registration of the test image, avoiding the need for random or manual initialization that leads to local optima.
Solution Approach 2:
The patent introduces an intermediary approach by using multiple training images and their pre-computed affine transformations as mediators between the test image and the final registration result. Instead of directly optimizing the test image registration (which is sensitive to initialization), the system uses the training images as intermediate steps to guide the registration through weighted combination of their transformations.
2Measurement precision
If multiple training images are processed to improve robustness, then registration accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary computation of affine transformations for all training images before the actual registration task. By pre-computing and storing these transformations, the system avoids re-computing them during test image registration, thus reducing the computational complexity at runtime while maintaining high accuracy through the use of multiple training images.
Solution Approach 2:
The patent changes the parameter representation by working with pre-computed affine transformation parameters from training images rather than directly optimizing image intensities or features during registration. This parameter transformation allows the system to leverage multiple training images for improved accuracy while reducing the computational burden through efficient parameter combination.
Data Source
AI summary
Example-based affine registration is provided. In various embodiments, a plurality of training images is read. A predetermined affine transform is read for each of the plurality of training images. Each affine transform maps its associated image to a template. Weights are determined for each of the plurality of training images. The weights are determined to minimize a difference between the test image and a weighted linear combination of the training images. An affine transform is determined mapping the test image to the template by computing a weighted linear combination of the affine transforms using the weights.