Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

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Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction

BioMed Research International ◽

10.1155/2017/6753831 ◽

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.

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Evaluation of image reconstruction algorithms for non-destructive characterization of thermal interfaces

International Journal of Thermal Sciences ◽

10.1016/j.ijthermalsci.2011.02.002 ◽

2011 ◽

Vol 50 (6) ◽

pp. 906-917 ◽

Cited By ~ 9

Author(s):

Hakan Erturk

Keyword(s):

Image Reconstruction ◽

Reconstruction Algorithms ◽

Thermal Interfaces ◽

Image Reconstruction Algorithms ◽

Non Destructive ◽

Non Destructive Characterization

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Comparing lesion detection performance for PET image reconstruction algorithms: A case study

1996 IEEE Nuclear Science Symposium. Conference Record ◽

10.1109/nssmic.1996.587921 ◽

2002 ◽

Cited By ~ 1

Author(s):

M.T. Chan ◽

R.M. Leahy ◽

E.U. Mumcuoglu ◽

S.R. Cherry ◽

J. Czernin ◽

...

Keyword(s):

Image Reconstruction ◽

Detection Performance ◽

Lesion Detection ◽

Reconstruction Algorithms ◽

Pet Image Reconstruction ◽

Image Reconstruction Algorithms

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A Benchmark Dataset and Deep Learning-Based Image Reconstruction for Electrical Capacitance Tomography

Sensors ◽

10.3390/s18113701 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3701 ◽

Cited By ~ 9

Author(s):

Jin Zheng ◽

Jinku Li ◽

Yi Li ◽

Lihui Peng

Keyword(s):

Machine Learning ◽

Image Reconstruction ◽

Benchmark Dataset ◽

Electrical Capacitance Tomography ◽

Electrical Capacitance ◽

Reconstruction Algorithms ◽

Reconstruction Methods ◽

Image Reconstruction Algorithms ◽

Capacitance Tomography ◽

The Relationship

Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.

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