Computer Tomography (CT) systems are used to produced images that are finding increasing use in medicine and mineralogy as in the Natural History Museum. In order to maximise the system performance, the images or scans, must have high quality.
Ideally, the physical CT system should preserve the image quality. However, in reality,various physical processes degrade the quality of these images, producing noise and artefacts.
The goal of this work is to understand the noise and artefacts in high-resolution imaging application of micro computer tomography (micro-CT). The project specifically lookedat determining:
I. How micro-CT scan parameters be optimised to reduce noise, and
II. Which of the many commonly used noise reduction algorithms produce the best results, and
III. How current and exposure effect each other.
Experiments were carried out to obtain the raw data – images / scans for the study. Theoretical models were then implemented on the raw data to analyse and better understand the noise and artefacts in the images.
This work provides a better understanding of both the fundamental performance (i.e. image quality) of the micro-CT system, and the assessment of user-defined parameters that could be used to optimise the performance of the micro-CT systems.
These contributions will not only save time, money and resources, which will ultimately lead to better image quality (greater accuracy)
Table of Contents
1 Introduction
1.1 The Natural History Museum London (NHM)
1.1.1 The beginning of the British Museum (NHM London)
1.1.2 NHM in the 21st Century
1.2 Computed Tomography(CT) – Introduction
1.2.1 Industrial Computed Tomography
1.2.2 Micro-CT
1.2.3 Image J
1.3 Noise/Artefacts
1.3.1 Artefacts
1.3.1.1 Motion Artefacts
1.3.1.2 Ring Artefacts
1.3.1.3 Metal Artefacts
1.3.1.4 Beam Hardening Artefacts
1.3.1.5 Partial Volume Artefacts
1.3.1.6 Other artefacts
1.3.2 Noise
1.4 Filters
1.4.1 Reasons for using a digital filter
1.4.2 High-pass Filters
1.4.3 Low-pass Filters
1.4.4 Kalman Stack Filter
1.4.5 Gaussian blur Filter
1.4.6 Kuwahara Filter
2 Project Assignment: Analysing noise pattern in Micro-CT
2.1 Materials and Methods
2.1.1 Metris X-Tek HMX ST 225 CT System
2.1.2 Phantom Design
2.1.3 ImageJ plug-in Code
2.1.3.1 Development of the plug-in
2.1.3.2 Using the Plug-in
2.2 Description of project objectives: Part I, Part II and Part III
2.2.1 Part I: How do scan parameters affect noise?
2.2.2 Part II: What is the best type of noise reduction algorithm for CT?
2.2.3 Part III: How current, exposure and noise affect other?
2.3 Summary and Analysis
2.3.1 Analysing pattern of the noise variation
2.3.2 Optimising parameters for noise reduction
2.3.2.1 Effect of noise reduction filters
2.3.2.2 Comparing the filters
2.3.2.3 Optimum noise reduction algorithm
2.3.2.4 Comparison between Kalman stack filter with the reduction algorithm in CTpro
2.3.3 Analysing current vs. exposure effect
3 Conclusion and future work
3.1 Insight from experiments
3.2 Insights from post-processing
3.3 Suggestions for future work
Objectives and Topics
This work aims to understand and mitigate noise and artefacts in high-resolution micro-computed tomography (micro-CT) imaging. The primary research goal is to optimize scan parameters and evaluate noise reduction algorithms to improve image quality and diagnostic accuracy for biological and mineralogical specimens.
- Optimization of micro-CT scan parameters (voltage, current, exposure).
- Comparative analysis of digital noise reduction algorithms (e.g., Kalman, Gaussian, Kuwahara).
- Investigation of the relationship between current, exposure, and image noise.
- Development of a custom ImageJ plug-in for quantitative noise analysis.
- Evaluation of industrial CT-specific challenges and artefact types.
Excerpt from the Book
1.3 Noise/Artefacts
The foundations of imaging system performance and image quality can be traced back to the pioneering work of Albert Rose (U.S. National Library of Medicine). He showed that image quality is fundamentally limited by the statistical fluctuations in image quanta. Hence, the more image quanta used to create an image, the better the image quality.
The technology to produce images has improved dramatically during the last decades, but still researchers struggle with artefacts and noise in the image quality. Broadly speaking artefacts is an inherent property i.e. dependent on the X-ray system, whereas noise is an external influence – property that is affected by outside the X-ray system.
In the Figure 4 you can see in different colours, the classification of noise. To understand better what effect the different classifications could have on the quality of the data a closer look into each of the elements classified is essential.
Summary of Chapters
1 Introduction: Provides historical background on the Natural History Museum and explains the fundamental principles of CT and micro-CT, including definitions of noise and artefacts.
2 Project Assignment: Analysing noise pattern in Micro-CT: Details the experimental setup, methodologies, and the systematic investigation of scan parameters and digital filtering techniques.
3 Conclusion and future work: Summarizes the key findings regarding optimal scanning parameters and the effectiveness of specific filters, while suggesting further research directions.
Keywords
Micro-CT, Computed Tomography, Image Noise, Artefacts, Digital Filters, Kalman Stack Filter, Gaussian Blur, Kuwahara Filter, ImageJ, Scan Parameters, X-ray Energy, Signal-to-Noise Ratio, Industrial CT, Image Quality, Phantom Design.
Frequently Asked Questions
What is the primary focus of this work?
The work focuses on understanding and reducing noise and artefacts in high-resolution micro-CT imaging to improve quantitative analysis of samples.
What are the central thematic fields?
The central themes include the physics of CT imaging, the classification of noise/artefacts, and the application of post-processing digital filters.
What is the core research goal?
The goal is to determine optimal scan parameters and the most effective noise reduction algorithms for industrial micro-CT systems.
Which scientific methodology is employed?
The research uses experimental scanning of a glass-bead/flour phantom, followed by quantitative analysis using a custom-developed ImageJ plug-in.
What topics are covered in the main section?
The main part covers the technical details of the X-Tek CT system, the design of the phantom, and the comparative analysis of various noise reduction algorithms.
Which keywords characterize this work?
Key terms include Micro-CT, Noise Reduction, Kalman Stack Filter, Artefacts, ImageJ, and X-ray parameter optimization.
What is the difference between noise and artefacts in this context?
According to the text, artefacts are inherent to the X-ray system, whereas noise is considered an external influence affecting the signal.
What did the analysis of the Kalman Stack Filter reveal?
The analysis indicated that the Kalman Stack Filter provides the best performance for noise reduction, outperforming the in-built CTPro noise reduction algorithm.
What is the significance of the "critical current" mentioned in the results?
The study identified a critical current of 60 μA; deviations from this point in either direction were found to potentially increase standard deviation and noise levels.
- Citar trabajo
- Galina Bernhardt (Autor), 2010, Noise Patterns in industrial Micro Computed Tomography, Múnich, GRIN Verlag, https://www.grin.com/document/165584
