X-ray Tomographic Volume Data Fusion for Contrast Optimization
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Solution Overview
Problem
Current high-resolution x-ray imaging systems face limitations in optimizing image quality and contrast, particularly when comparing volumes captured under different conditions such as dual-energy imaging, absorption contrast tomography, phase contrast tomography, or with and without contrast agents, due to variations in x-ray energy and reconstruction methods leading to artifacts.
Innovation Solution
A multi-energy x-ray imaging data acquisition and image reconstruction system that includes an image tuning tool allowing operators to manipulate and optimize image display parameters by combining reconstructed volumes from different conditions, enabling the creation of synthetic images and enhancing the spatial distribution analysis of elements within the sample through noise reduction, alignment, and statistical histogram analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate tomographic volume data sets are reconstructed under different conditions (different energies, contrast agents, reconstruction methods), then the ability to analyze different aspects of the sample is improved, but artifacts and inconsistencies arise due to variations in acquisition parameters
Solution Approach 1:
The patent combines multiple separate tomographic volume data sets (acquired under different energies, with/without contrast agents, or using different reconstruction methods) into a unified integrated data set. This merging process allows the system to preserve and analyze the unique characteristics of each condition while eliminating artifacts and inconsistencies that arise from viewing data in isolation, thereby resolving the contradiction between versatility and reliability.
Solution Approach 2:
The patent introduces an intermediary processing stage that includes noise reduction filters, alignment algorithms, and statistical histogram analysis. These intermediary tools mediate between the raw data from different acquisition conditions and the final visual output, adjusting and normalizing the data to ensure consistency and reliability while maintaining the ability to analyze different sample aspects.
2Manufacturing precision
If image display parameters are optimized for specific conditions (e.g., dual-energy imaging), then image contrast and quality are improved for that condition, but the system becomes less adaptable to other conditions
Solution Approach 1:
The patent creates a universal image processing framework that can handle multiple imaging conditions (single-energy, dual-energy, absorption contrast, phase contrast, with/without contrast agents) through a single integrated system. The universal tools include adaptive noise reduction filters, multi-condition alignment algorithms, and statistical histogram analysis that work across all data types, eliminating the need for separate optimized systems for each condition.
Solution Approach 2:
The patent implements dynamic image display parameter optimization where the system automatically adjusts imaging parameters based on the specific condition being analyzed. The system can dynamically switch between different processing algorithms and display parameters depending on whether the data is from single-energy or dual-energy imaging, with or without contrast agents, thereby maintaining optimal image quality across all conditions without sacrificing adaptability.
3Manufacturing precision
If noise reduction filters are applied to improve image quality, then image contrast is enhanced, but fine details and spatial resolution may be degraded
Solution Approach 1:
The patent applies local quality enhancement by implementing adaptive noise reduction filters that adjust their strength and characteristics based on the local content and requirements of the image. Different regions of the image can receive different levels of noise reduction processing, allowing the system to enhance contrast in areas where it is most needed while preserving fine spatial details in regions where resolution is critical, thus resolving the contradiction between image quality and spatial resolution.
Solution Approach 2:
The patent utilizes parameter changes in the noise reduction algorithm based on statistical histogram analysis of the image data. The system dynamically adjusts filtering parameters (such as filter strength, kernel size, and threshold values) depending on the image characteristics and acquisition conditions. This parameter adaptation allows the system to optimize the balance between noise reduction for contrast enhancement and preservation of spatial resolution for detailed analysis.
4Adaptability or versatility
If multiple reconstruction methods are used to handle different artifacts, then the analysis capability is improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex reconstruction process into distinct, manageable modules, each dedicated to handling specific artifacts or optimization goals. Separate reconstruction algorithms can be applied to different data sets or different regions of the same data set depending on the specific analysis requirements. This modular segmentation allows the system to maintain high analysis capability through multiple specialized methods while reducing overall system complexity by organizing the processing steps into clear, independent units that can be selected and applied as needed.
Data Source
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AI summary
A method for combining tomographic volume data sets and an Image Analysis Tool of an X-Ray Imaging Microscopy System are disclosed, which enable optimizing the image parameters based on multiple tomographic volume date sets of the sample that have been captured under different conditions using an x-ray microscopy system. This enables the operator to control the image contrast, for example, of selected slices, and apply the information associated with optimizing the contrast of the selected slice to all slices in two or more tomographic volume data sets. This creates a combined volume with optimized image contrast throughout. Also, the system enables navigation within the volumes through functional annotation, improvements in volume registration and improvements in noise suppression both within the volumes and within slice histograms of the sample.