Microwave data inversion in hemorrhagic brain stroke imaging: A Newton-conjugate-gradient based approach in LpBanach spaces (Invited paper)

2017 ◽  
Author(s):  
Igor Bisio ◽  
Alessandro Fedeli ◽  
Fabio Lavagetto ◽  
Matteo Pastorino ◽  
Andrea Randazzo ◽  
...  
Keyword(s):  
Conjugate Gradient ◽  
Data Inversion ◽  
Stroke Imaging ◽  
Gradient Based ◽  
Brain Stroke ◽  
Microwave Data

scholarly journals A Penalized Linear and Nonlinear Combined Conjugate Gradient Method for the Reconstruction of Fluorescence Molecular Tomography

2007 ◽  
Vol 2007 ◽  
pp. 1-19 ◽  
Author(s):  
Shang Shang ◽  
Jing Bai ◽  
Xiaolei Song ◽  
Hongkai Wang ◽  
Jaclyn Lau
Keyword(s):  
Conjugate Gradient Method ◽  
Conjugate Gradient ◽  
Gradient Method ◽  
Optimization Problems ◽  
Gradient Methods ◽  
Reconstruction Algorithms ◽  
Fluorescence Molecular Tomography ◽  
Nonlinear Conjugate Gradient Method ◽  
Gradient Based ◽  
Linear And Nonlinear

Conjugate gradient method is verified to be efficient for nonlinear optimization problems of large-dimension data. In this paper, a penalized linear and nonlinear combined conjugate gradient method for the reconstruction of fluorescence molecular tomography (FMT) is presented. The algorithm combines the linear conjugate gradient method and the nonlinear conjugate gradient method together based on a restart strategy, in order to take advantage of the two kinds of conjugate gradient methods and compensate for the disadvantages. A quadratic penalty method is adopted to gain a nonnegative constraint and reduce the illposedness of the problem. Simulation studies show that the presented algorithm is accurate, stable, and fast. It has a better performance than the conventional conjugate gradient-based reconstruction algorithms. It offers an effective approach to reconstruct fluorochrome information for FMT.

Energy- and Area-Efficient Recursive-Conjugate-Gradient-Based MMSE Detector for Massive MIMO Systems

2020 ◽  
Vol 68 ◽  
pp. 573-588 ◽  
Author(s):  
Leibo Liu ◽  
Guiqiang Peng ◽  
Pan Wang ◽  
Sheng Zhou ◽  
Qiushi Wei ◽  
...  
Keyword(s):  
Conjugate Gradient ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Gradient Based ◽  
Mmse Detector ◽  
Area Efficient

AN INVESTIGATION OF GRADIENT-BASED RECONSTRUCTION ALGORITHMS FOR STATISTICAL PET TRANSMISSION IMAGING

2003 ◽  
Vol 15 (05) ◽  
pp. 179-185
Author(s):  
MING-YUE CHANG ◽  
CHING-HAN HSU
Keyword(s):  
Statistical Model ◽  
Conjugate Gradient ◽  
Iterative Algorithms ◽  
Preconditioned Conjugate Gradient ◽  
Reconstruction Algorithms ◽  
Steepest Ascent ◽  
Transmission Imaging ◽  
Transmission Data ◽  
Gradient Based ◽  
Phantom Studies

For PET transmission imaging, the conventional iterative algorithms based on expectation maximization type algorithms, could not effectively converge to optimal image solution. In this study, we suggest a statistical model PET transmission data, and then investigate a class of gradient-based optimization algorithms for transmission image reconstruction including steepest ascent, conjugate gradient, and preconditioned conjugate gradient. From phantom studies, the preconditioned conjugate algorithms can converge to good image results within limited number of iteration. Combined with the suggested statistical model of transmission data, the preconditioned conjugate algorithms can also produce attenuation maps with accurate linear attenuation coefficients for clinical data.

Conjugate gradient-based parameters identification

2010 ◽  
Author(s):  
Shengyu Wang ◽  
Hong Mi ◽  
Bin Xi ◽  
Daniel Sun
Keyword(s):  
Conjugate Gradient ◽  
Parameters Identification ◽  
Gradient Based
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