scholarly journals Image Reconstruction Methods for MATLAB Users - A Moore-Penrose Inverse Approach

10.5772/45811 ◽  
2012 ◽  
Author(s):  
S. Chountasis ◽  
V.N. Katsikis ◽  
D. Pappas
Keyword(s):  
Image Reconstruction ◽  
Inverse Approach ◽  
Reconstruction Methods

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.

Evaluating image reconstruction methods in improving effective parameters on image quality in IRI-microPET

2014 ◽  
Vol 57 (2) ◽  
pp. 218-221 ◽  
Author(s):  
S. Z. Islami rad ◽  
M. Shamsaei Zafarghandi ◽  
R. Gholipour Peyvandi ◽  
M. Ghannadi Maragheh
Keyword(s):  
Image Reconstruction ◽  
Image Quality ◽  
Effective Parameters ◽  
Reconstruction Methods

The award of medals by the President, Sir Andrew Huxley, O.M., at the Anniversary Meeting, 30 November 1985

1986 ◽  
Vol 405 (1829) ◽  
pp. 181-184
Keyword(s):  
Electron Microscopy ◽  
Image Reconstruction ◽  
Coat Protein ◽  
Tobacco Mosaic Virus ◽  
Mosaic Virus ◽  
Deep Insight ◽  
Living Matter ◽  
Fourier Image ◽  
Reconstruction Methods ◽  
Insight Into

The Copley Medal is awarded to Dr A. Klug, F. R. S., in recognition of his outstanding contributions to our understanding of complex biological structures and the methods used for determining them. Together with D. Kaspar, Klug developed a theory that predicted the arrangement of sub-units in the protein shells of spherical viruses. This theory brought order and understanding into a confused field ; nearly all the observed structures of small spherical viruses, many of them elucidated by Klug and his collaborators, are consistent with it. After more than 20 years’ work on tobacco mosaic virus Klug and his colleagues solved the structure of its coat protein in atomic detail. They also elucidated the mechanisms by which the helical virus particle assembles itself from its RNA and its 2130 protein sub-units. Recently his group succeeded in crystallizing chromatin, and solved its structure at a resolution sufficient to see the double-helical DNA coiled around the spool of histone. Many of Klug’s successes were made possible by his introduction of Fourier image reconstruction methods into electron microscopy. Klug’s work is characterized by deep insight into the physics of diffraction and image formation and the intricate geometry of living matter.

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 Deep learning in holography and coherent imaging

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Yair Rivenson ◽  
Yichen Wu ◽  
Aydogan Ozcan
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Imaging Systems ◽  
New Paradigm ◽  
Coherent Imaging ◽  
Holographic Image ◽  
Engineering Research ◽  
Recent Advances ◽  
Reconstruction Methods ◽  
Recovery Techniques

Abstract Recent advances in deep learning have given rise to a new paradigm of holographic image reconstruction and phase recovery techniques with real-time performance. Through data-driven approaches, these emerging techniques have overcome some of the challenges associated with existing holographic image reconstruction methods while also minimizing the hardware requirements of holography. These recent advances open up a myriad of new opportunities for the use of coherent imaging systems in biomedical and engineering research and related applications.

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