Photoacoustic imaging using a transvaginal ultrasound probe: a comparison of image reconstruction methods

2020 ◽  
Author(s):  
Guilherme Fernandes ◽  
Diego R. Sampaio ◽  
Theo Pavan
Keyword(s):  
Image Reconstruction ◽  
Photoacoustic Imaging ◽  
Transvaginal Ultrasound ◽  
Ultrasound Probe ◽  
Reconstruction Methods

scholarly journals Implementation of 2-Dimensional Medical Image Reconstruction Algorithm for Photoacoustic Imaging

2017 ◽  
Vol 4 (1) ◽  
pp. 6-11
Author(s):  
Enkhbat Batbayar ◽  
Enkhbaatar Tumenjargal ◽  
Woonchul Ham ◽  
Chulgyu Song
Keyword(s):  
Image Reconstruction ◽  
Imaging System ◽  
Photoacoustic Imaging ◽  
Reconstruction Algorithm ◽  
Ultrasound Probe ◽  
Geometric Information ◽  
Image Reconstruction Algorithm ◽  
Medical Image Reconstruction ◽  
Simulation Results

In this paper, we review and discuss our proposed image reconstruction algorithm for two dimensional stereo photo-acoustic medical imaging system. The proposed algorithms aim to evaluate the existence possibility of acoustic source within a search area by using the geometric information which is the distance between each sensor element of ultrasound probe and corresponding searching point. Besides, we implemented the hardware and software aspects of an photo-acoustic imaging system that is described in hardware implementation section of this paper. At the end of this paper, some simulation results are presented which utilized the proposed algorithm while compared the image quality of its and conventional k-wave algorithm in which FFT should be used. The simulation results proved the effectiveness of the proposed reconstruction algorithm.

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.

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