Masked Micro-CT Reconstruction for Large Voxel Volume Reduction
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Solution Overview
Problem
Iterative reconstruction methods in X-ray micro tomography systems face challenges with prohibitively slow processing times and high memory requirements due to the large volume of voxels, especially when dealing with samples where a significant portion is air, leading to inefficient use of computational resources.
Innovation Solution
Implementing 3D volume masks or bounding boxes to define a reconstruction volume within the field of view, reducing the volume to be reconstructed, and using iterative reconstruction based on these masks to optimize memory and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If iterative reconstruction is performed on the entire field of view volume, then reconstruction completeness is improved, but memory consumption and processing time increase significantly
Solution Approach 1:
The patent divides the large field of view volume into multiple smaller sub-volumes or regions of interest. By segmenting the reconstruction task, the system processes only relevant portions of the data, reducing memory consumption and processing time while maintaining reconstruction completeness for the areas that matter most.
Solution Approach 2:
The patent extracts and identifies the actual sample region from the entire field of view using detection algorithms or preliminary scanning. By separating the sample-containing region from the empty space, the system performs iterative reconstruction only on the necessary volume, eliminating wasted computational resources on air-filled regions.
2Reliability
If iterative reconstruction is performed on the entire field of view volume, then reconstruction completeness is improved, but computational resource efficiency deteriorates
Solution Approach 1:
The patent segments the field of view into multiple processing regions, allowing parallel computation on smaller sub-volumes. This segmentation enables better utilization of computational resources and improves overall processing efficiency while maintaining the ability to reconstruct the complete sample structure.
Solution Approach 2:
The patent applies iterative reconstruction selectively to only the portions of the field of view that contain sample material, rather than processing the entire volume. This partial action approach maintains reconstruction completeness for the sample while avoiding excessive computation on empty regions, thereby improving computational efficiency.
3Productivity
If the reconstruction volume is reduced using masks, then processing speed is improved, but reconstruction accuracy may deteriorate
Solution Approach 1:
The patent applies different processing strategies to different regions: masked regions receive focused iterative reconstruction with higher computational effort, while unmasked regions use faster algorithms. This local quality approach ensures high accuracy where the sample is located while maintaining overall processing speed.
Solution Approach 2:
The patent performs preliminary detection or low-resolution scanning to identify the sample region and create masks before performing high-resolution iterative reconstruction. This preliminary action ensures that the masks accurately represent the sample boundaries, preventing loss of reconstruction accuracy while enabling speed improvements.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Reduces memory consumption and computational time by focusing reconstruction on the actual sample area, improving processing speed and efficiency in X-ray tomography systems.
Implementation Method 1
X-rays are directed to the sample, and are absorbed or scattered by the sample as the X-rays travel through the sample
Implementation Method 2
X-rays are directed to the sample, and are absorbed or scattered by the sample as the X-rays travel through the sample
Implementation Method 3
A detector system receives the transmitted X-rays, and creates an image representation, in pixels, of the received X-rays
Data Source
AI summary
Iterative reconstruction performance is improved by saving or better utilizing memory by introducing an image mask or bounding box or other volumetric mask. These are used to reduce the reconstructed volume with respect to the field of view (FOV), possibly even to the sample itself.