3D Image Registration Using Localized Cross-Correlation Alignment
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Solution Overview
Problem
Current methods for aligning large 3-dimensional image datasets are inefficient, requiring long computation times and lacking in accuracy, especially when using cross-correlation algorithms, and manual alignment methods are labor-intensive.
Innovation Solution
A fully automated algorithm that subdivides volumes into smaller subvolumes, performs cross-correlation on these subvolumes to determine alignment shifts, and uses a least squares fit to iteratively refine a transformation matrix for precise alignment, significantly reducing computation time and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional registration methods (FFD, B-splines, rigid transformations) are used, then the registration process is computationally efficient, but the accuracy of matching anatomical structures between moving and reference images deteriorates due to inability to handle non-rigid deformations and intensity variations
Solution Approach 1:
The patent divides the image registration process into multiple hierarchical levels (粗配准 and 精配准 stages) and breaks down the transformation into discrete control points. This segmentation allows the complex non-rigid transformation to be managed through simpler, localized operations at each level, improving accuracy without overwhelming computational complexity.
Solution Approach 2:
The patent applies different transformation models to different regions of the image. Rigid transformations are applied where appropriate, while non-rigid B-spline transformations are applied in regions requiring flexibility. This local adaptation of transformation quality allows high accuracy in critical areas while maintaining overall computational efficiency.
2Measurement precision
If non-rigid transformations are applied to match anatomical structures, then registration accuracy improves, but the computational time and processing complexity increase significantly
Solution Approach 1:
The patent performs a preliminary rigid or affine registration before applying non-rigid B-spline transformations. This preliminary alignment brings the images into close proximity, reducing the magnitude of subsequent non-rigid adjustments needed. This preliminary action significantly reduces computational time while maintaining high final accuracy.
Solution Approach 2:
The patent implements an iterative refinement process with multiple passes of non-rigid transformation, each pass making smaller adjustments based on residual misalignment. This periodic action allows the system to converge to high accuracy solutions efficiently by breaking down the complex transformation into manageable iterative steps.
3Measurement precision
If intensity-based metrics (MSE, NMSE) are used for evaluation, then the process is simple to implement, but the evaluation accuracy deteriorates due to sensitivity to intensity variations and normalization issues
Solution Approach 1:
The patent introduces gradient magnitude maps as an intermediary representation. Instead of directly comparing raw intensity values which are sensitive to normalization differences, the method computes gradients and compares gradient magnitudes and directions. This intermediary approach filters out intensity normalization issues while preserving structural alignment information, significantly improving evaluation accuracy.
Solution Approach 2:
The patent transforms the evaluation from direct intensity comparison to gradient-based comparison. By changing the parameter being measured from absolute intensity to intensity gradient (magnitude and direction), the evaluation becomes invariant to intensity normalization while remaining sensitive to structural misalignment, resolving the contradiction between simplicity and accuracy.
Data Source
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AI summary
Aligning multiple 3D images of an object can be difficult when the representative datasets (images) are large. An exemplary aspect of this technology teaches a technique to subdivide the images and use the alignments between the subdivided images to determine the alignment between the complete datasets.