Motion Compensated Iterative Reconstruction Using Regular Grid Resampling
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Solution Overview
Problem
Iterative reconstruction algorithms in computed tomography (CT) are computationally costly and time-consuming, especially when incorporating motion vector fields to reduce image blurring, making them impractical for clinical use due to increased reconstruction times.
Innovation Solution
The method involves re-sampling image data to maintain voxels on a regular grid during forward- and back-projection, allowing for parallel processing and reducing reconstruction time by keeping voxel position and geometry consistent across different motion states, thereby enabling efficient motion-compensated iterative reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If motion vector fields are incorporated into iterative reconstruction by modifying voxel position, size and shape for each heart phase, then image quality is improved and dose reduction is achieved, but reconstruction time increases significantly
Solution Approach 1:
The patent changes the representation parameters of motion compensation by using displacement vectors applied to a fixed regular grid rather than modifying voxel positions, sizes and shapes. This parameter transformation maintains image quality improvement while enabling efficient parallel processing on regular grids, thus reducing reconstruction time significantly
Solution Approach 2:
The patent replaces the complex mechanical-like system of variable voxel positions and shapes with a simpler computational approach using fixed grid positions and displacement vector fields. This substitution enables the use of parallel processing architectures while maintaining motion compensation effectiveness
2Manufacturing precision
If iterative reconstruction algorithms are used to reduce dose and improve image quality, then manufacturing precision is improved, but device complexity and computational cost increase
Solution Approach 1:
The patent segments the reconstruction process into distinct forward projection and back projection steps that operate on regularly gridded data. This segmentation allows each step to be independently optimized and parallelized, reducing overall computational complexity while maintaining iterative reconstruction quality
Solution Approach 2:
The patent creates a universal framework where motion compensation can be applied to any iterative reconstruction algorithm through the use of displacement vector fields on regular grids. This multi-functional approach allows the same efficient methodology to be applied across different reconstruction algorithms, reducing overall system complexity
3Manufacturing precision
If voxel position and geometry are changed for motion compensation, then motion artifacts are reduced, but ease of operation and parallel processing capability deteriorate
Solution Approach 1:
Instead of changing voxel positions to achieve motion compensation, the patent inverts the approach by keeping voxels on a fixed regular grid and applying motion compensation through displacement vectors during the projection operations. This inversion maintains parallel processing capability while achieving motion artifact reduction
Solution Approach 2:
The patent changes the parameter representation from variable voxel geometry to fixed grid positions with vector displacement fields. This parameter transformation enables efficient parallel processing while maintaining the ability to compensate for motion artifacts through the displacement vectors
Data Source
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AI summary
A method includes re-sampling current image data representing a reference motion state into a plurality of different groups, each group corresponding to a different motion state of moving tissue of interest, forward projecting each of the plurality of groups, generating a plurality of groups of forward projected data, each group of forward projected data corresponding to a group of the re-sampled current image data, determining update projection data based on a comparison between the forward projected data and the measured projection data, grouping the update projection data into a plurality of groups, each group corresponding to a different motion state of the moving tissue of interest, back projecting each of the plurality of groups, generating a plurality of groups of update image data, re¬ sampling each group of update image data to the reference motion state of the current image, and generating new current image data based on the current image data and the re-sampled update image data.