scholarly journals Muography of different structures using muon scattering and absorption algorithms

2018 ◽  
Vol 377 (2137) ◽  
pp. 20180051 ◽  
Author(s):  
S. Vanini ◽  
P. Calvini ◽  
P. Checchia ◽  
A. Rigoni Garola ◽  
J. Klinger ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Noise Suppression ◽  
Spent Nuclear Fuel ◽  
Simulated Data ◽  
Cosmic Ray ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Novel Approach ◽  
Muon Momentum

In recent decades, muon imaging has found a plethora of applications in many fields. This technique succeeds to infer the density distribution of big inaccessible structures where conventional techniques cannot be used. The requirements of different applications demand specific implementations of image reconstruction algorithms for either multiple scattering or absorption–transmission data analysis, as well as noise-suppression filters and muon momentum estimators. This paper presents successful results of image reconstruction techniques applied to simulated data of some representative applications. In addition to well-known reconstruction methods, a novel approach, the so-called μCT, is proposed for the inspection of spent nuclear fuel canisters. Results obtained based on both μCT and the maximum-likelihood expectation maximization reconstruction algorithms are presented. This article is part of the Theo Murphy meeting issue ‘Cosmic-ray muography’.

scholarly journals Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Hsuan-Ming Huang ◽  
Ing-Tsung Hsiao
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Low Dose ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Acceleration Techniques ◽  
Reconstruction Methods ◽  
Speed Up ◽  
Phantom Studies

Background and Objective. Over the past decade, image quality in low-dose computed tomography has been greatly improved by various compressive sensing- (CS-) based reconstruction methods. However, these methods have some disadvantages including high computational cost and slow convergence rate. Many different speed-up techniques for CS-based reconstruction algorithms have been developed. The purpose of this paper is to propose a fast reconstruction framework that combines a CS-based reconstruction algorithm with several speed-up techniques.Methods. First, total difference minimization (TDM) was implemented using the soft-threshold filtering (STF). Second, we combined TDM-STF with the ordered subsets transmission (OSTR) algorithm for accelerating the convergence. To further speed up the convergence of the proposed method, we applied the power factor and the fast iterative shrinkage thresholding algorithm to OSTR and TDM-STF, respectively.Results. Results obtained from simulation and phantom studies showed that many speed-up techniques could be combined to greatly improve the convergence speed of a CS-based reconstruction algorithm. More importantly, the increased computation time (≤10%) was minor as compared to the acceleration provided by the proposed method.Conclusions. In this paper, we have presented a CS-based reconstruction framework that combines several acceleration techniques. Both simulation and phantom studies provide evidence that the proposed method has the potential to satisfy the requirement of fast image reconstruction in practical CT.

scholarly journals Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

2019 ◽  
Vol 28 (1) ◽  
pp. 426-435 ◽  
Author(s):  
Zhengzhi Liu ◽  
Stylianos Chatzidakis ◽  
John M. Scaglione ◽  
Can Liao ◽  
Haori Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Cosmic Ray ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Algorithms

scholarly journals A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3701 ◽  
Author(s):  
Jin Zheng ◽  
Jinku Li ◽  
Yi Li ◽  
Lihui Peng
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Benchmark Dataset ◽  
Electrical Capacitance Tomography ◽  
Electrical Capacitance ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Image Reconstruction Algorithms ◽  
Capacitance Tomography ◽  
The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

Research and Developments in Medical Image Reconstruction Methods and Its Applications

2017 ◽  
pp. 491-535
Author(s):  
Shailendra Tiwari ◽  
Rajeev Srivastava
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Medical Image ◽  
Ct Scanner ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Development Technology ◽  
Medical Image Reconstruction

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures. In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.

scholarly journals Magnetic resonance imaging of the subthalamic nucleus for deep brain stimulation

2016 ◽  
Vol 124 (1) ◽  
pp. 96-105 ◽  
Author(s):  
Arjun S. Chandran ◽  
Michael Bynevelt ◽  
Christopher R. P. Lind
Keyword(s):  
Image Reconstruction ◽  
Subthalamic Nucleus ◽  
Spin Echo ◽  
Quantitative Susceptibility Mapping ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Mri Sequences ◽  
Direct Targeting ◽  
Accurate Imaging ◽  
Deep Brain

The subthalamic nucleus (STN) is one of the most important stereotactic targets in neurosurgery, and its accurate imaging is crucial. With improving MRI sequences there is impetus for direct targeting of the STN. High-quality, distortion-free images are paramount. Image reconstruction techniques appear to show the greatest promise in balancing the issue of geometrical distortion and STN edge detection. Existing spin echo- and susceptibility-based MRI sequences are compared with new image reconstruction methods. Quantitative susceptibility mapping is the most promising technique for stereotactic imaging of the STN.

Research and Developments in Medical Image Reconstruction Methods and its Applications

2014 ◽  
pp. 274-312
Author(s):  
Shailendra Tiwari ◽  
Rajeev Srivastava
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Medical Imaging ◽  
Medical Image ◽  
Ct Scanner ◽  
Image Reconstruction Techniques ◽  
Reconstruction Methods ◽  
Dimensional Reconstruction ◽  
Development Technology ◽  
Medical Image Reconstruction

Image reconstruction from projection is the field that lays the foundation for Medical Imaging or Medical Image Processing. The rapid and proceeding progress in medical image reconstruction, and the related developments in analysis methods and computer-aided diagnosis, has promoted medical imaging into one of the most important sub-fields in scientific imaging. Computer technology has enabled tomographic and three-dimensional reconstruction of images, illustrating both anatomical features and physiological functioning, free from overlying structures.In this chapter, the authors share their opinions on the research and development in the field of Medical Image Reconstruction Techniques, Computed Tomography (CT), challenges and the impact of future technology developments in CT, Computed Tomography Metrology in industrial research & development, technology, and clinical performance of different CT-scanner generations used for cardiac imaging, such as Electron Beam CT (EBCT), single-slice CT, and Multi-Detector row CT (MDCT) with 4, 16, and 64 simultaneously acquired slices. The authors identify the limitations of current CT-scanners, indicate potential of improvement and discuss alternative system concepts such as CT with area detectors and Dual Source CT (DSCT), recent technology with a focus on generation and detection of X-rays, as well as image reconstruction are discussed. Furthermore, the chapter includes aspects of applications, dose exposure in computed tomography, and a brief overview on special CT developments. Since this chapter gives a review of the major accomplishments and future directions in this field, with emphasis on developments over the past 50 years, the interested reader is referred to recent literature on computed tomography including a detailed discussion of CT technology in the references section.

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

scholarly journals New algorithms for computed tomography image reconstruction to eliminate artifacts

2021 ◽  
Author(s):  
Eli Lechtman
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Filtered Backprojection ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Tomography Image ◽  
Ct Simulation ◽  
Novel Algorithms ◽  
Computed Tomography Image

Computed tomography (CT) relies on computational algorithms to reconstruct images from CT projections. Current filtered backprojection reconstruction methods have inherent limitations in situations with sharp density gradients and limited beam views. In this thesis two novel reconstruction algorithms were introduced: the Algebraic Image Reconstruction (AIR) algorithm, and the Geometric Image Reconstruction Algorithm (GIRA). A CT simulation was developed to test these novel algorithms and compare their images to filtered backprojection images. AIR and GIRA each demonstrated their proof of principle in these preliminary tests. AIR and its extension, the Parsed AIR algorithm (PAIR), were able to reconstruct optimal images compared to filtered backprojection after empirically determining parameters relevant to the algorithms. While GIRA reconstructed optimal images in preliminary tests, reconstruction was complicated by error propagation for larger imaging domains. The initial success of these novel approaches justifies continued research and development to determine their feasibility for practical CT image reconstruction.

scholarly journals Optical breast atlas as a testbed for image reconstruction in optical mammography

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yidan Xing ◽  
Yubo Duan ◽  
Padmeya P. Indurkar ◽  
Anqi Qiu ◽  
Nanguang Chen
Keyword(s):  
Image Reconstruction ◽  
Simulated Data ◽  
Ground Truth ◽  
Optical Measurements ◽  
Light Sources ◽  
Reconstruction Algorithms ◽  
Optical Mammography ◽  
Breast Compression ◽  
Optical Images ◽  
Image Reconstruction Algorithms

AbstractWe present two optical breast atlases for optical mammography, aiming to advance the image reconstruction research by providing a common platform to test advanced image reconstruction algorithms. Each atlas consists of five individual breast models. The first atlas provides breast vasculature surface models, which are derived from human breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data using image segmentation. A finite element-based method is used to deform the breast vasculature models from their natural shapes to generate the second atlas, compressed breast models. Breast compression is typically done in X-ray mammography but also necessary for some optical mammography systems. Technical validation is presented to demonstrate how the atlases can be used to study the image reconstruction algorithms. Optical measurements are generated numerically with compressed breast models and a predefined configuration of light sources and photodetectors. The simulated data is fed into three standard image reconstruction algorithms to reconstruct optical images of the vasculature, which can then be compared with the ground truth to evaluate their performance.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

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