Accelerated Coupled Filtering for 3D Tissue Deformation
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Solution Overview
Problem
Existing coupled filtering methods for tissue deformation analysis are computationally expensive and impractical for three-dimensional implementations, limiting their application to two-dimensional analysis due to the vast number of required operations.
Innovation Solution
An accelerated coupled filtering method (FastCF) that applies filters to pre- and post-deformation images, utilizing a modified motion matrix and envelope detection to reduce computational load, and employs a coarse-to-fine search strategy to optimize parameter search, thereby accelerating the process while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the coupled filtering method is applied to 3D tissue deformation analysis, then measurement precision is improved, but computational complexity increases to billions of times more operations than 2D implementation
Solution Approach 1:
The patent divides the 3D volume into multiple 2D slices and processes them independently through the coupled filtering method. This segmentation approach maintains the measurement precision of the coupled filtering method while reducing computational complexity from billions of operations to manageable levels by processing each slice separately rather than the entire 3D volume at once.
Solution Approach 2:
The patent transitions from 2D to 3D analysis by adding the elevational dimension to the existing axial and lateral directions. This is achieved by stacking multiple 2D slices along the elevational axis, enabling comprehensive 3D tissue deformation analysis while managing computational load through the segmentation approach.
2Measurement precision
If the coupled filtering method is implemented for complete tissue deformation analysis, then measurement precision is improved, but implementation time becomes impractical for real-world applications
Solution Approach 1:
By segmenting the 3D volume into independent 2D slices that can be processed in parallel, the patent dramatically reduces processing time while maintaining the high precision of coupled filtering. This enables real-world clinical applications where rapid diagnosis is critical.
Solution Approach 2:
The patent implements automated parallel processing of multiple 2D slices, allowing the system to efficiently utilize available computational resources without requiring manual intervention for each slice, thereby reducing overall processing time for complete 3D analysis.
Data Source
AI summary
There is provided an accelerated coupled filtering method for tissue deformation analysis. The method includes steps of applying, by a processing device, a first filter on a pre-deformation image of a tissue to obtain a filtered pre-deformation image, and applying, by the processing device, a second filter on a post-deformation image of the tissue to obtain a filtered post-deformation image. The filtered pre-deformation image and the filtered post-deformation image can be correlated by a first motion matrix including a plurality of first motion parameters. The plurality of first motion parameters can include at least three fundamental first motion parameters, each of the three fundamental first motion parameters can represent movement of the tissue along an axial direction relative to the axial direction, an elevational direction and a lateral direction, respectively. The method further includes a step of estimating, by the processing device, a respective value for each of the plurality of first motion parameters. Each of the estimated respective values can represent a difference between the pre-deformation and post-deformation filtered images. The method further includes a step of, in response to determining that at least one of the estimated respective values meets at least one predefined criterion, updating the at least one of the estimated respective values as an optimal value for at least one corresponding first motion parameter of the plurality of first motion parameters.