scholarly journals A Comparative Study of Four Total Variational Regularization Reconstruction Algorithms for Sparse-View Photoacoustic Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xueyan Liu ◽  
Limei Zhang ◽  
Yining Zhang ◽  
Lishan Qiao
Keyword(s):  
Total Variation ◽  
Photoacoustic Imaging ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Absolute Error ◽  
Biological Tissues ◽  
Total Variation Regularization ◽  
Reconstruction Algorithms ◽  
Scanning Time ◽  
Comparative Results

Photoacoustic imaging (PAI) is a new nonionizing, noninvasive biomedical imaging technology that has been employed to reconstruct the light absorption characteristics of biological tissues. The latest developments in compressed sensing (CS) technology have shown that it is possible to accurately reconstruct PAI images from sparse data, which can greatly reduce scanning time. This study focuses on the comparative analysis of different CS-based total variation regularization reconstruction algorithms, aimed at finding a method suitable for PAI image reconstruction. The performance of four total variation regularization algorithms is evaluated through the reconstruction experiment of sparse numerical simulation signal and agar phantom signal data. The evaluation parameters include the signal-to-noise ratio and normalized mean absolute error of the PAI image and the CPU time. The comparative results demonstrate that the TVAL3 algorithm can well balance the quality and efficiency of the reconstruction. The results of this study can provide some useful guidance for the development of the PAI sparse reconstruction algorithm.

An Enhanced NAS-RIF Algorithm for Blind Image Restoration Based on Total Variation Regularization

2013 ◽  
Vol 423-426 ◽  
pp. 2522-2525
Author(s):  
Xin Ke Li ◽  
Chao Gao ◽  
Yong Cai Guo ◽  
Yan Hua Shao
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Tikhonov Regularization Method ◽  
Total Variation Regularization ◽  
Inverse Filtering ◽  
Blind Image Restoration ◽  
Spatially Adaptive ◽  
Blind Image

In order to improve the quality of blind image restoration, we propose an algorithm which combines Non-negativity and Support constraint Recursive Inverse Filtering (NAS-RIF) and adaptive total variation regularization. In the proposed algorithm, the total variation regularization constraint term is added in the NAS-RIF algorithm cost function. The majorization-minimization approach and conjugate gradient iterative algorithm are adopted to improve the convergence speed. We do the simulation experiments for the blurred classic test image which is added additive random noise. Experimental results show that the restoration effect of our algorithm is better than the spatially adaptive Tikhonov regularization method and the NAS-RIF spatially adaptive regularization algorithm, while the value of improvement of signal to noise ratio (ISNR) has improved.

scholarly journals Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Daniela Ribeiro ◽  
William Hallett ◽  
Adriana A. S. Tavares
Keyword(s):  
Kinetic Modelling ◽  
Signal To Noise Ratio ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
Brain Pet ◽  
Good Spatial Resolution ◽  
Positron Emission ◽  
Pet Ct ◽  
Modelling Studies ◽  
Pet Scans

Abstract Background Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM. Methods Contrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined. Results For in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction. Conclusion This study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.

scholarly journals An iterative reconstruction algorithm for pole figure inversion using total variation regularization

2019 ◽  
Vol 52 (6) ◽  
pp. 1329-1341
Author(s):  
Saransh Singh ◽  
Prabhat Kc ◽  
Shivram Kashyap Sridhar ◽  
Marc De Graef
Keyword(s):  
Total Variation ◽  
Reconstruction Algorithm ◽  
Orientation Distribution ◽  
Total Variation Regularization ◽  
Projection Model ◽  
Discrete Method ◽  
Iterative Reconstruction Algorithm ◽  
Orientation Space ◽  
Pole Figures ◽  
Forward Projection

This paper describes a new discrete method for inverting X-ray pole figures to estimate the orientation distribution function (ODF). The method employs the equal-volume `cubochoric' representation for uniform discretization of orientation space, SO(3). The forward-projection model is combined with an anisotropic total variation term to iteratively determine the ODF. The efficacy of the new method is evaluated with both model and experimental data and compared with existing discrete and series expansion methods.

scholarly journals An Efficient Variational Method for Image Restoration

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jun Liu ◽  
Ting-Zhu Huang ◽  
Xiao-Guang Lv ◽  
Si Wang
Keyword(s):  
Image Restoration ◽  
Total Variation ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Total Variation Regularization ◽  
Split Bregman ◽  
Split Bregman Iteration ◽  
Imaging Science ◽  
Constrained Minimization Problem ◽  
Structure Similarity Index

Image restoration is one of the most fundamental issues in imaging science. Total variation regularization is widely used in image restoration problems for its capability to preserve edges. In this paper, we consider a constrained minimization problem with double total variation regularization terms. To solve this problem, we employ the split Bregman iteration method and the Chambolle’s algorithm. The convergence property of the algorithm is established. The numerical results demonstrate the effectiveness of the proposed method in terms of peak signal-to-noise ratio (PSNR) and the structure similarity index (SSIM).

scholarly journals Adaptive Reconstruction Algorithm Based on Compressed Sensing Broadband Receiver

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei-Jian Si ◽  
Qiang Liu ◽  
Zhi-An Deng
Keyword(s):  
Compressed Sensing ◽  
Signal To Noise Ratio ◽  
Adaptive Algorithms ◽  
Reconstruction Algorithm ◽  
Position Estimation ◽  
Accurate Estimation ◽  
Blind Estimation ◽  
Reconstruction Algorithms ◽  
Receiver System ◽  
Adaptive Reconstruction

Existing greedy reconstruction algorithms require signal sparsity, and the remaining sparsity adaptive algorithms can be reconstructed but cannot achieve accurate sparsity estimation. To address this problem, a blind sparsity reconstruction algorithm is proposed in this paper, which is applied to compressed sensing radar receiver system. The proposed algorithm can realize the estimation of signal sparsity and channel position estimation, which mainly consists of two parts. The first part is to use fast search based on dichotomy search, which is based on the high probability reconstruction of greedy algorithm, and uses dichotomy search to cover the number of sparsity. The second part is the signal matching and tracking algorithm, which is mainly used to judge the signal position and reconstruct the signal. Combine the two parts together to realize the blind estimation of the sparsity and the accurate estimation of the number of signals when the number of signals is unknown. The experimental analyses are carried out to evaluate the performance of the reconstruction probability, the accuracy of sparsity estimation, the running time of the algorithm, and the signal-to-noise ratio.

scholarly journals Semiblind Image Deconvolution with Spatially Adaptive Total Variation Regularization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yaduan Ruan ◽  
Houzhang Fang ◽  
Qimei Chen
Keyword(s):  
Total Variation ◽  
Spatial Information ◽  
Total Variation Regularization ◽  
Image Deconvolution ◽  
Split Bregman ◽  
Edge Information ◽  
Spatially Adaptive ◽  
Comparative Results ◽  
Simulated Images ◽  
Adaptive Total Variation

A semiblind image deconvolution algorithm with spatially adaptive total variation (SATV) regularization is introduced. The spatial information in different image regions is incorporated into regularization by using the edge indicator called difference eigenvalue to distinguish flat areas from edges. Meanwhile, the split Bregman method is used to optimize the proposed SATV model. The proposed algorithm integrates the spatial constraint and parametric blur-kernel and thus effectively reduces the noise in flat regions and preserves the edge information. Comparative results on simulated images and real passive millimeter-wave (PMMW) images are reported.

scholarly journals Deflection Tomography Reconstruction Based on Diagonal Total Variation

2018 ◽  
Vol 246 ◽  
pp. 03022
Author(s):  
Huaxin Li ◽  
Jinxiao Pan
Keyword(s):  
Sparse Representation ◽  
Total Variation ◽  
Reconstruction Algorithm ◽  
Reconstruction Algorithms ◽  
High Quality ◽  
Directional Information ◽  
Field Reconstruction ◽  
Tomography Reconstruction ◽  
Measured Field ◽  
Representation Method

In view of the shortages of the reconstruction algorithm based on Total Variation (TV) minimum under the framework of measured field compressed sensing, we study the measured field sparse representation method and solving method of optimization equation, and propose the measured field reconstruction algorithms based on Diagonal Total Variation (DTV). When there is no obvious change in the reconstruction iteration of TV, gradient transformation of diagonal direction is introduced, the multi-directional information is used to obtain a more sparse representation of the measured field in the reconstruction. Under the condition of sparse projections, experimental results of this algorithm are demonstrated and compared with the results from the TV method. Comparisons show that this method can reconstruct high-quality measured field.

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