A consecutive reconstruction strategy for estimating absorption and scattering coefficient distribution in multiple-illumination photoacoustic tomography (MIPAT)

2014 ◽  
Author(s):  
Peng Shao ◽  
Tyler J. Harrison ◽  
Roger J. Zemp
Keyword(s):  
Scattering Coefficient ◽  
Photoacoustic Tomography ◽  
Coefficient Distribution ◽  
Reconstruction Strategy

scholarly journals GNSS-R Delay-Doppler Map Simulation Based on the 2004 Sumatra-Andaman Tsunami Event

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Qingyun Yan ◽  
Weimin Huang
Keyword(s):  
Region Of Interest ◽  
Final Analysis ◽  
Satellite System ◽  
Sea Surface ◽  
Scattering Coefficient ◽  
Scattering Coefficients ◽  
Coefficient Distribution ◽  
Simulation Based ◽  
Global Navigation Satellite ◽  
Fixed Region

A new method for simulating Global Navigation Satellite System-Reflectometry (GNSS-R) delay-Doppler maps (DDMs) of a tsunami-dominant sea surface is presented. In this method, the bistatic scattering Z-V model, the sea surface mean square slope model of Cox and Munk, and the tsunami-induced wind perturbation model are employed. The feasibility of the Cox and Munk model under a tsunami scenario is examined by comparing the Cox and Munk model based scattering coefficient with the Jason-1 measurement. A good consistency between these two results is obtained with a correlation coefficient of 0.93. After confirming the applicability of the Cox and Munk model for a tsunami-dominated sea, this study provides the simulations of the scattering coefficient distribution and the corresponding DDMs of a fixed region of interest before and during the tsunami. In the final analysis, by subtracting the simulation results that are free of tsunami from those with presence of tsunami, the tsunami-induced variations in scattering coefficients and DDMs can be clearly observed. As a result, the tsunami passage can be readily interpreted.

scholarly journals A sparsification and reconstruction strategy for compressed sensing photoacoustic tomography

2018 ◽  
Vol 143 (6) ◽  
pp. 3838-3848 ◽  
Author(s):  
Markus Haltmeier ◽  
Michael Sandbichler ◽  
Thomas Berer ◽  
Johannes Bauer-Marschallinger ◽  
Peter Burgholzer ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Photoacoustic Tomography ◽  
Reconstruction Strategy

Estimating Pigment Concentrations from Spectral Images Using an Encoder‐Decoder Neural Network

2020 ◽  
Vol 64 (3) ◽  
pp. 30502-1-30502-15
Author(s):  
Kensuke Fukumoto ◽  
Norimichi Tsumura ◽  
Roy Berns
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Absorption Coefficient ◽  
Spectral Data ◽  
High Accuracy ◽  
Pigment Concentration ◽  
Scattering Coefficient ◽  
A Value ◽  
Input And Output ◽  
Pigment Concentrations

Abstract A method is proposed to estimate the concentration of pigments mixed in a painting, using the encoder‐decoder model of neural networks. The model is trained to output a value that is the same as its input, and its middle output extracts a certain feature as compressed information about the input. In this instance, the input and output are spectral data of a painting. The model is trained with pigment concentration as the middle output. A dataset containing the scattering coefficient and absorption coefficient of each of 19 pigments was used. The Kubelka‐Munk theory was applied to the coefficients to obtain many patterns of synthetic spectral data, which were used for training. The proposed method was tested using spectral images of 33 paintings, which showed that the method estimates, with high accuracy, the concentrations that have a similar spectrum of the target pigments.

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