Masked X-Ray Micro Tomography Reconstruction for Memory Reduction
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Solution Overview
Problem
Current X-ray micro tomography systems face challenges with iterative reconstruction methods due to high computational demands and memory requirements, particularly when dealing with large volumes and high aspect ratio objects, leading to inefficient processing and memory consumption.
Innovation Solution
Implementing 3D volume masks or bounding boxes to define a reconstruction volume within the field of view, using techniques such as thresholding and region growing, and employing user guidance to create masks, thereby reducing the reconstruction volume and optimizing memory usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If iterative reconstruction is used to improve image quality and reduce artifacts, then manufacturing precision and reliability are improved, but processing time and computational resources increase significantly
Solution Approach 1:
The patent divides the large field of view into multiple smaller sub-volumes using masks, processing only relevant regions. This segmentation reduces the computational domain from the entire FOV to specific sub-volumes containing samples of interest, thereby maintaining image quality while reducing processing time and computational resources.
Solution Approach 2:
The patent applies different processing qualities to different regions by using masks to identify and process only sub-volumes containing samples. High-quality iterative reconstruction is applied locally to masked regions rather than uniformly across the entire field of view, optimizing resource allocation while maintaining precision where needed.
2Measurement precision
If the full field of view is reconstructed to ensure complete coverage, then measurement precision is improved, but memory requirements and computational power increase
Solution Approach 1:
The patent extracts and processes only the relevant sub-volumes containing samples using masks, rather than processing the entire field of view. This extraction approach maintains reconstruction accuracy for sample regions while significantly reducing memory consumption by excluding empty or irrelevant spaces from the reconstruction process.
3Manufacturing precision
If high resolution reconstruction is performed to visualize fine features, then manufacturing precision is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the reconstruction task into smaller sub-volumes using masks, allowing high-resolution processing to be applied only to regions containing samples. This reduces the overall computational complexity while maintaining high feature resolution within the masked regions of interest.
Data Source
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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.