scholarly journals A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

2018 ◽  
Vol 8 (9) ◽  
pp. 1570 ◽  
Author(s):  
Parsa Omidi ◽  
Mohsin Zafar ◽  
Moein Mozaffarzadeh ◽  
Ali Hariri ◽  
Xiangzhi Haung ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Signal To Noise Ratio ◽  
Iterative Algorithms ◽  
Cost Effective ◽  
Photoacoustic Signal ◽  
Reconstruction Algorithms ◽  
Edge Preservation ◽  
High Quality ◽  
Minimum Number

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

scholarly journals Increasing the Performance of the Iterative Computed Tomography Image Reconstruction Algorithms

2020 ◽  
Vol 23 (2) ◽  
pp. 194-203
Author(s):  
Shimaa Abdulsalam Khazal ◽  
Mohammed Hussein Ali
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Iterative Algorithms ◽  
Computation Time ◽  
Ct Imaging ◽  
Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Reconstruction Process ◽  
Simulation Results ◽  
Reconstruction Time

Computed tomography (CT) imaging is an important diagnostic tool. CT imaging facilitates the internal rendering of a scanned object by measuring the attenuation of beams of X-ray radiation. CT employs a mathematical technique of image reconstruction; those techniques are classified as; analytical and iterative. The iterative reconstruction (IR) methods have been proven to be superior over the analytical methods, but due to their prolonged reconstruction time, those methods are excluded from routine use in clinical applications. In this paper the reconstruction time of an IR algorithm is minimized through the employment of an adaptive region growing segmentation method that focuses the image reconstruction process on a specified region, thus ignoring unwanted pixels that increase the computation time. This method is tested on the iterative algebraic reconstruction technique (ART) algorithm. Some phantom images are used in this paper to demonstrate the effects of the segmentation process. The simulation results are executed using MATLAB (version R2018b) programming language, and a computer system with the following specifications: CPU core i7 (2.40 GHz) for processing. Simulation results indicate that this method will reduce the reconstruction time of the iterative algorithms, and will enhance the quality of the reconstructed image.

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 Evaluation of Algebraic Iterative Image Reconstruction Methods for Tetrahedron Beam Computed Tomography Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Joshua Kim ◽  
Huaiqun Guan ◽  
David Gersten ◽  
Tiezhi Zhang
Keyword(s):  
Computed Tomography ◽  
Iterative Reconstruction ◽  
Cone Angle ◽  
Iterative Algorithms ◽  
Projection Data ◽  
Reconstruction Algorithms ◽  
Reconstruction Methods ◽  
Large Cone Angle ◽  
Using Data ◽  
Dual Detector

Tetrahedron beam computed tomography (TBCT) performs volumetric imaging using a stack of fan beams generated by a multiple pixel X-ray source. While the TBCT system was designed to overcome the scatter and detector issues faced by cone beam computed tomography (CBCT), it still suffers the same large cone angle artifacts as CBCT due to the use of approximate reconstruction algorithms. It has been shown that iterative reconstruction algorithms are better able to model irregular system geometries and that algebraic iterative algorithms in particular have been able to reduce cone artifacts appearing at large cone angles. In this paper, the SART algorithm is modified for the use with the different TBCT geometries and is tested using both simulated projection data and data acquired using the TBCT benchtop system. The modified SART reconstruction algorithms were able to mitigate the effects of using data generated at large cone angles and were also able to reconstruct CT images without the introduction of artifacts due to either the longitudinal or transverse truncation in the data sets. Algebraic iterative reconstruction can be especially useful for dual-source dual-detector TBCT, wherein the cone angle is the largest in the center of the field of view.

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

Image reconstruction algorithms of computed tomography

2013 ◽  
Vol 6 (5) ◽  
pp. 617-632
Author(s):  
阎春生 YAN Chun-sheng ◽  
廖延彪 LIAO Yan-biao ◽  
田芊 TIAN Qian
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Algorithms

Radiation dose and image quality inK-edge subtraction computed tomography of lungin vivo

2014 ◽  
Vol 21 (6) ◽  
pp. 1305-1313 ◽  
Author(s):  
S. Strengell ◽  
J. Keyriläinen ◽  
P. Suortti ◽  
S. Bayat ◽  
A. R. A. Sovijärvi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Dose ◽  
Signal To Noise Ratio ◽  
Regional Distribution ◽  
Lung Ventilation ◽  
Structural Features ◽  
Reconstruction Algorithms ◽  
Human Thorax

K-edge subtraction computed tomography (KES-CT) allows simultaneous imaging of both structural features and regional distribution of contrast elements inside an organ. Using this technique, regional lung ventilation and blood volume distributions can be measured experimentallyin vivo. In order for this imaging technology to be applicable in humans, it is crucial to minimize exposure to ionizing radiation with little compromise in image quality. The goal of this study was to assess the changes in signal-to-noise ratio (SNR) of KES-CT lung images as a function of radiation dose. The experiments were performed in anesthetized and ventilated rabbits using inhaled xenon gas in O2at two concentrations: 20% and 70%. Radiation dose, defined as air kerma (Ka), was measured free-in-air and in a 16 cm polymethyl methacrylate phantom with a cylindrical ionization chamber. The dose free-in-air was varied from 2.7 mGy to 8.0 Gy. SNR in the images of xenon in air spaces was above the Rose criterion (SNR > 5) whenKawas over 400 mGy with 20% xenon, and over 40 mGy with 70% xenon. Although in human thorax attenuation is higher, based on these findings it is estimated that, by optimizing the imaging sequence and reconstruction algorithms, the radiation dose could be further reduced to clinically acceptable levels.

CT Image Reconstruction using NLMfuzzyCD Regularization Method

2021 ◽  
Vol 17 ◽  
Author(s):  
Manju Devi ◽  
Sukhdip Singh ◽  
Shailendra Tiwari
Keyword(s):  
Computed Tomography ◽  
Maximum Likelihood ◽  
Image Reconstruction ◽  
Reconstruction Algorithm ◽  
Diagnostic Tools ◽  
Edge Preservation ◽  
Nonlocal Means ◽  
Iterative Reconstruction Algorithm ◽  
Complex Diffusion ◽  
Aims And Scope

Aims and scope: Computed Tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of its projections at different angles. Background: To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used. Objective: Although the conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from the optimal smoothing as the number of iterations increase. Methods: For solving this problem, in this paper present a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means fuzzy complex diffusion as a regularization term for noise reduction and edge preservation. Results: The proposed model was evaluated on four test cases phantoms. Conclusion: Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compare with the state-of-the-art techniques.

BIFURCATION ANALYSIS OF ITERATIVE IMAGE RECONSTRUCTION METHOD FOR COMPUTED TOMOGRAPHY

2008 ◽  
Vol 18 (04) ◽  
pp. 1219-1225 ◽  
Author(s):  
TETSUYA YOSHINAGA ◽  
YOSHIHIRO IMAKURA ◽  
KEN'ICHI FUJIMOTO ◽  
TETSUSHI UETA
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Computational Method ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Algebraic Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Iterative Image Reconstruction ◽  
Multiplicative Algebraic Reconstruction Technique ◽  
Algebraic Reconstruction

Of the iterative image reconstruction algorithms for computed tomography (CT), the power multiplicative algebraic reconstruction technique (PMART) is known to have good properties for speeding convergence and maximizing entropy. We analyze here bifurcations of fixed and periodic points that correspond to reconstructed images observed using PMART with an image made of multiple pixels and we investigate an extended PMART, which is a dynamical class for accelerating convergence. The convergence process for the state in the neighborhood of the true reconstructed image can be reduced to the property of a fixed point observed in the dynamical system. To investigate the speed of convergence, we present a computational method of obtaining parameter sets in which the given real or absolute values of the characteristic multiplier are equal. The advantage of the extended PMART is verified by comparing it with the standard multiplicative algebraic reconstruction technique (MART) using numerical experiments.

scholarly journals Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3498 ◽  
Author(s):  
Rayyan Manwar ◽  
Matin Hosseinzadeh ◽  
Ali Hariri ◽  
Karl Kratkiewicz ◽  
Shahryar Noei ◽  
...  
Keyword(s):  
Photoacoustic Imaging ◽  
Signal To Noise Ratio ◽  
Laser Diodes ◽  
Cost Effective ◽  
Photoacoustic Signal ◽  
Low Energy ◽  
Low Snr ◽  
Energy Laser ◽  
Noisy Measurements ◽  
Low Energy Laser

In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster.

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