Photoacoustic Imaging Reconstruction Algorithm Based on a Combined First and Second Order Total Variation Regularization

2019 ◽  
Vol 9 (1) ◽  
pp. 91-96
Author(s):  
Jin Wang ◽  
Chen Zhang ◽  
Yuanyuan Wang
Keyword(s):  
Total Variation ◽  
Photoacoustic Imaging ◽  
Reconstruction Algorithm ◽  
Second Order ◽  
Total Variation Regularization ◽  
Imaging Reconstruction

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.

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.

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