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Evaluation of 10 current image reconstruction algorithms for linear array photoacoustic imaging

JOURNAL OF BIOPHOTONICS 2024年第3期17卷 e202300117-e202300117页

作者： Prakash, Ravi Manwar, Rayyan Avanaki, Kamran Univ Illinois Richard & Loan Hill Dept Biomed Engn Chicago IL 60607 USA Univ Illinois Dept Dermatol Chicago IL USA

Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications. Reconstructed photoacoustic images of blood-filled tubes in intralipid ***

关键词： image reconstruction algorithms linear array transducer photoacoustic imaging

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MR image reconstruction algorithms for sparse k-space data:: a Java-based integration

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MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2002年第1-3期15卷 18-26页

作者： de Beer, R Coron, A Graveron-Demilly, D Lethmate, R Nastase, S van Ormondt, D Wajer, FTAW Delft Univ Technol Dept Appl Phys NL-2600 GA Delft Netherlands Univ Lyon 1 CNRS Lab RMN UMR 5012CPE F-69622 Villeurbanne France

We have worked on multi-dimensional magnetic resonance imaging (MRI) data acquisition and related image reconstruction methods that aim at reducing the MRI scan time. To achieve this scan-time reduction we have combined the approach of 'increasing the speed' of k-space acquisition with that of 'deliberately omitting' acquisition of k-space trajectories (sparse sampling). Today we have a whole range of (sparse) sampling distributions and related reconstruction methods. In the context of a European Union Training and Mobility of Researchers project we have decided to integrate all methods into one coordinating software system. This system meets the requirements that it is highly structured in an object-oriented manner using the Unified Modeling Language and the Java programming environment, that it uses the client-server approach, that it allows multi-client communication sessions with facilities for sharing data and that it is a true distributed computing system with guaranteed reliability using core activities of the Java Jini package. (C) 2002 Published by Elsevier Science B.V.

关键词： magnetic resonance imaging software system image reconstruction algorithms sparse sampling Java Jini multi-client data sharing

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A Superresolution image reconstruction Algorithm Based on Landweber in Electrical Capacitance Tomography

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MATHEMATICAL PROBLEMS IN ENGINEERING 2013年第1期2013卷 1-8页

作者： Chen Deyun Li Zhiqiang Gao Ming Wang Lili Yu Xiaoyang Harbin Univ Sci & Technol Higher Educ Key Lab Measuring & Control Technol & Harbin Peoples R China

According to the image reconstruction accuracy influenced by the "soft field" nature and ill-conditioned problems in electrical capacitance tomography, a superresolution image reconstruction algorithm based on Landweber is proposed in the paper, which is based on the working principle of the electrical capacitance tomography system. The method uses the algorithm which is derived by regularization of solutions derived and derives closed solution by fast Fourier transform of the convolution kernel. So, it ensures the certainty of the solution and improves the stability and quality of image reconstruction results. Simulation results show that the imaging precision and real-time imaging of the algorithm are better than Landweber algorithm, and this algorithm proposes a new method for the electrical capacitance tomography image reconstruction algorithm.

关键词： HIGH resolution imaging image reconstruction algorithms ELECTRICAL capacitance tomography COMPUTER algorithms REGULARIZATION (Mathematics) COMPUTER simulation

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image reconstruction for Electrical Impedance Tomography: Experimental Comparison of Radial Basis Neural Network and Gauss - Newton Method

Image Reconstruction for Electrical Impedance Tomography: Ex...

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15th IFAC/IEEE Conference on Programmable Devices and Embedded Systems (PDeS)

作者： Hrabuska, Radek Prauzek, Michal Venclikova, Marketa Konecny, Jaromir VSB Tech Univ Ostrava Dept Cybernet & Biomed Engn Ostrava 70833 Czech Republic

Electrical impedance tomography (EIT) is an intensively researched noninvasive diagnostic method for medical use, that can help to improve the lung diagnostics, artificial lung ventilation and prevent lung injuries. Further improvements of reconstruction algorithms and measurement devices are essential to widen the use of EIT as a lung diagnostic method. To test potential of Radial Basis Neural Networks (RBNN) and Hopfield Neural Networks (HNN) for image reconstruction experiment is carried. Said neural networks are compared with Gauss Newton (GN) algorithm. Results of the experiment show higher reconstruction accuracy with RBNN and HNN over GN algorithm. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Electrical Impedance Tomography Neural Networks image reconstruction algorithms

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Improved Compressed Sensing-Based Algorithm for Sparse-View CT image reconstruction

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COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2013年第1期2013卷 185750-185750页

作者： Zhu, Zangen Wahid, Khan Babyn, Paul Cooper, David Pratt, Isaac Carter, Yasmin Univ Saskatchewan Dept Elect & Comp Engn Saskatoon SK S7N 5A9 Canada Saskatoon Hlth Reg Dept Med Imaging Saskatoon SK S7N 0W8 Canada Univ Saskatchewan Coll Med Saskatoon SK S7N 5E5 Canada

In computed tomography (CT), there are many situations where reconstruction has to be performed with sparse-view data. In sparse-view CT imaging, strong streak artifacts may appear in conventionally reconstructed images due to limited sampling rate that compromises image quality. Compressed sensing (CS) algorithm has shown potential to accurately recover images from highly undersampled data. In the past few years, total-variation-(TV-) based compressed sensing algorithms have been proposed to suppress the streak artifact in CT image reconstruction. In this paper, we propose an efficient compressed sensing-based algorithm for CT image reconstruction from few-view data where we simultaneously minimize three parameters: the l(1) norm, total variation, and a least squares measure. The main feature of our algorithm is the use of two sparsity transforms-discrete wavelet transform and discrete gradient transform. Experiments have been conducted using simulated phantoms and clinical data to evaluate the performance of the proposed algorithm. The results using the proposed scheme show much smaller streaking artifacts and reconstruction errors than other conventional methods.

关键词： image reconstruction algorithms COMPUTED tomography COMPRESSED sensing (Signal processing) image quality analysis PARAMETER estimation DISCRETE systems PERFORMANCE evaluation

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Adaptive tight frame based medical image reconstruction: a proof-of-concept study for computed tomography

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INVERSE PROBLEMS 2013年第12期29卷 125006-125005页

作者： Zhou, Weifeng Cai, Jian-Feng Gao, Hao Hunan Univ Coll Math & Econometr Changsha 410082 Hunan Peoples R China Shanghai Jiao Tong Univ Dept Math Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Biomed Engn Shanghai 200240 Peoples R China Emory Univ Dept Math & Comp Sci Atlanta GA 30322 USA Univ Iowa Dept Math Iowa City IA 52242 USA Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA

A popular approach for medical image reconstruction has been through the sparsity regularization, assuming the targeted image can be well approximated by sparse coefficients under some properly designed system. The wavelet tight frame is such a widely used system due to its capability for sparsely approximating piecewise-smooth functions, such as medical images. However, using a fixed system may not always be optimal for reconstructing a variety of diversified images. Recently, the method based on the adaptive over-complete dictionary that is specific to structures of the targeted images has demonstrated its superiority for image processing. This work is to develop the adaptive wavelet tight frame method image reconstruction. The proposed scheme first constructs the adaptive wavelet tight frame that is task specific, and then reconstructs the image of interest by solving an l(1)-regularized minimization problem using the constructed adaptive tight frame system. The proof-of-concept study is performed for computed tomography (CT), and the simulation results suggest that the adaptive tight frame method improves the reconstructed CT image quality from the traditional tight frame method.

关键词： image reconstruction algorithms DIAGNOSTIC imaging SPARSE matrices COMPUTED tomography REGULARIZATION parameter

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Computer-Aided Measurements of Pulmonary Emphysema in Chest Multidetector-Row Spiral Computed Tomography: Effect of image reconstruction Parameters

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JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY 2008年第6期32卷 899-904页

作者： Behrendt, Florian F. Das, Marco Mahnken, Andreas H. Kraus, Thomas Bakai, Annemarie Stanzel, Sven Guenther, Rolf W. Wildberger, Joachim E. Rhein Westfal TH Aachen Univ Hosp Dept Diagnost Radiol Aachen Germany Rhein Westfal TH Aachen Univ Hosp Inst & Outpatient Clin Occupat & Social Med Aachen Germany Siemens Med Solut Comp Tomog Forchheim Germany Rhein Westfal TH Aachen Univ Hosp Inst Med Stat Aachen Germany

Objective: To evaluate the effect of different image reconstruction parameters on quantitative automated measurements of pulmonary emphysema in chest multidetector-row spiral computed tomography. Materials and Methods: Thirty patients with known emphysema underwent multidetector-row spiral computed tomography. Retrospective reconstruction with a soft tissue kernel (Siemens B20 at Imin. 2-mm, and 3-mm slices) and 4 alternative kernel grades (from smooth to sharp: Siemens 1330, B40, B50, B60 at 1-mm slices) was performed. Total lung volume, emphysema volume (EV), 15th percentile density, and 4 EV clusters were quantified. Results were compared with those of standard algorithm B20/1-mm slices. Results: Differences in total lung volume were less than 0.2%. Alternative kernel grades resulted in a significantly increased average EV The 15th percentile density showed a significant average difference for all alternative algorithms. The large emphysema cluster showed a significant change for reconstruction algorithms 1350, 1360, B20/2 mm and B20/3 mm. Conclusions: Pulmonary EV is significantly affected by different reconstruction algorithms.

关键词： pulmonary emphysema computed tomography quantification image reconstruction algorithms analysis software

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Investigation of THz image reconstruction by Transposed Convolution Algorithm 10

Investigation of THz Image Reconstruction by Transposed Conv...

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10th UK-Europe-China Workshop on Millimetre Waves and Terahertz Technologies (UCMMT)

作者： Zhou, Min Hu, Shaoqing Alfadhl, Yasir Chen, Xiaodong Queen Mary Univ London Sch Elect Engn & Comp Sci Mile End Rd London E1 4NS England

ISBN: (纸本)9781538627204

This paper presents our study on image reconstruction algorithms for THz near field scanning systems. Based on the principle of Physical Optics (PO) algorithm, we have proposed and investigated a novel transposed convolution image reconstruction algorithm (TC), in comparison with the back propagation algorithm (BP) in simulation and experiment.

关键词： image reconstruction algorithms near field scanning systems PO algorithm transposed convolution image reconstruction algorithm

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EVALUATION OF USEFULNESS OF DUAL-ENERGY COMPUTED TOMOGRAPHYS IN RADIOTHERAPY PLANNING FOR PATIENTS WITH HIP ENDOPROSTHESIS

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ACTA PHYSICA POLONICA B 2019年第3期50卷 349-360页

作者： Mazur, L. Kisielewicz, K. Dziecichowicz, A. Sapikowska, A. Maria Sklodowska Curie Mem Inst Oncol Ctr Oncol Krakow Branch Garncarska 11 PL-31115 Krakow Poland

Based on computed tomography (CT) scans of the interior of a patient's body, it is possible to precisely locate planning target volume (PTV) and organs at risk (OARs) for radiotherapy. Constant development of this kind of imaging techniques has led to the emergence of dual-energy CT, which in conjunction with Metal Artifact Reduction software (MARs) allows to restore the structures and compensate the disorders resulting from the presence of metallic implants in the patient's body. Such implants cause artifacts in the CT image which carries false information about the area between endoprosthesis. The aim of this thesis is evaluation of usefulness of dual-energy CT in radiotherapy planning for patients with hip endoprosthesis in comparison to manual method of reduction. This method relies heavily on estimating where a given tissue passes into another and inflicting one average HU (Hounsfield Units) value for the artifact site, based on the HU measurement for several neighboring tissues. Treatment plans were created using TPS (Treatment Planning System) Eclipse. Calculated dose distributions were imported into the Sun Nuclear application and subjected to gamma analysis. It can be concluded that the MARs algorithm is very useful for treatment planning, but it should be used with at most care.

关键词： DUAL energy CT (Tomography) FRAUD image reconstruction algorithms RADIOTHERAPY COMPUTED tomography CONTENT analysis

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Automatic Segmentation for Plant Leaves via Multiview Stereo reconstruction

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MATHEMATICAL PROBLEMS IN ENGINEERING 2017年第1期2017卷 1-11页

作者： Guo, Jingwei Xu, Lihong Tongji Univ Coll Elect & Informat Engn Shanghai Peoples R China

This paper presented a new method for automatic plant point cloud acquisition and leaves segmentation. Quasi-dense point cloud of the plant is obtained from multiview stereo reconstruction based on surface expansion. In order to overcome the negative effects from complex natural light changes and to obtain a more accurate plant point cloud, the Adaptive Normalized Cross-Correlation algorithm is used in calculating the matching cost between two images, which is robust to radiometric factors and can reduce the fattening effect around boundaries. In the stage of segmentation for each single leaf, an improved region growing method based on fully connected conditional random field (CRF) is proposed to separate the connected leaves with similar color. The method has three steps: boundary erosion, initial segmentation, and segmentation refinement. First, the edge of each leaf point cloud is eroded to remove the connectivity between leaves. Then leaves will be initially segmented by region growing algorithm based on local surface normal and curvature. Finally an efficient CRF inference method based on mean field approximation is applied to remove small isolated regions. Experimental results show that our multiview stereo reconstruction method is robust to illumination changes and can obtain accurate color point clouds. And the improved region growing method based on CRF can effectively separate the connected leaves in obtained point cloud.

关键词： SEGMENTATION (image processing) STEREO image processing image reconstruction algorithms DATA acquisition systems SURFACES (Geometry)

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