Perturbation Monte Carlo in quantitative photoacoustic tomography

2021 ◽  
Author(s):  
Aki Pulkkinen ◽  
Aleksi Leino ◽  
Tuomas Lunttila ◽  
Meghdoot Mozumder ◽  
Tanja Tarvainen
Keyword(s):  
Monte Carlo ◽  
Photoacoustic Tomography

Monte-Carlo-based inversion scheme for 3D quantitative photoacoustic tomography

2017 ◽  
Author(s):  
Bernhard A. Kaplan ◽  
Jens Buchmann ◽  
Steffen Prohaska ◽  
Jan Laufer
Keyword(s):  
Monte Carlo ◽  
Photoacoustic Tomography

Monte Carlo light transport-based blood vessel quantification using linear array photoacoustic tomography

2017 ◽  
Vol 15 (11) ◽  
pp. 111701 ◽  
Author(s):  
Xiangwei Lin Xiangwei Lin ◽  
Mingjian Sun Mingjian Sun ◽  
Naizhang Feng Naizhang Feng ◽  
Depeng Hu Depeng Hu ◽  
Yi Shen Yi Shen
Keyword(s):  
Monte Carlo ◽  
Blood Vessel ◽  
Linear Array ◽  
Light Transport ◽  
Photoacoustic Tomography

scholarly journals Three-dimensional quantitative photoacoustic tomography using an adjoint radiance Monte Carlo model and gradient descent

2019 ◽  
Vol 24 (06) ◽  
pp. 1 ◽  
Author(s):  
Jens Buchmann ◽  
Bernhard A. Kaplan ◽  
Samuel Powell ◽  
Steffen Prohaska ◽  
Jan Laufer
Keyword(s):  
Monte Carlo ◽  
Gradient Descent ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
Photoacoustic Tomography

scholarly journals Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

2020 ◽  
Vol 39 (10) ◽  
pp. 2985-2995
Author(s):  
Aleksi A. Leino ◽  
Tuomas Lunttila ◽  
Meghdoot Mozumder ◽  
Aki Pulkkinen ◽  
Tanja Tarvainen
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Photoacoustic Tomography

scholarly journals Quantitative photoacoustic tomography using forward and adjoint Monte Carlo models of radiance

2016 ◽  
Vol 21 (12) ◽  
pp. 126004 ◽  
Author(s):  
Roman Hochuli ◽  
Samuel Powell ◽  
Simon Arridge ◽  
Ben Cox
Keyword(s):  
Monte Carlo ◽  
Photoacoustic Tomography

Two schemes for quantitative photoacoustic tomography based on Monte Carlo simulation

2016 ◽  
Vol 43 (7) ◽  
pp. 3987-3997 ◽  
Author(s):  
Yubin Liu ◽  
Huabei Jiang ◽  
Zhen Yuan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Photoacoustic Tomography

Outbursts of comet Schwassmann-Wachmann 1, and the cloud of interplanetary boulders

1974 ◽  
Vol 22 ◽  
pp. 307 ◽  
Author(s):  
Zdenek Sekanina
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Interplanetary Space ◽  
Porous Matrix ◽  
Maximum Diameter ◽  
Expansion Velocity ◽  
Solid Constituent ◽  
Meteoric Material ◽  
Periodic Comet

AbstractIt is suggested that the outbursts of Periodic Comet Schwassmann-Wachmann 1 are triggered by impacts of interplanetary boulders on the surface of the comet’s nucleus. The existence of a cloud of such boulders in interplanetary space was predicted by Harwit (1967). We have used the hypothesis to calculate the characteristics of the outbursts – such as their mean rate, optically important dimensions of ejected debris, expansion velocity of the ejecta, maximum diameter of the expanding cloud before it fades out, and the magnitude of the accompanying orbital impulse – and found them reasonably consistent with observations, if the solid constituent of the comet is assumed in the form of a porous matrix of lowstrength meteoric material. A Monte Carlo method was applied to simulate the distributions of impacts, their directions and impact velocities.

Monte Carlo Algorithms for Moments of Transition Arrays

1988 ◽  
Vol 102 ◽  
pp. 79-81
Author(s):  
A. Goldberg ◽  
S.D. Bloom
Keyword(s):  
Monte Carlo ◽  
State Vector ◽  
Basis Vector ◽  
Collective State ◽  
Dispersion Characteristics ◽  
Dipole Strength ◽  
The Third ◽  
Monte Carlo Algorithms ◽  
Third Moment ◽  
Very High

AbstractClosed expressions for the first, second, and (in some cases) the third moment of atomic transition arrays now exist. Recently a method has been developed for getting to very high moments (up to the 12th and beyond) in cases where a “collective” state-vector (i.e. a state-vector containing the entire electric dipole strength) can be created from each eigenstate in the parent configuration. Both of these approaches give exact results. Herein we describe astatistical(or Monte Carlo) approach which requires onlyonerepresentative state-vector |RV> for the entire parent manifold to get estimates of transition moments of high order. The representation is achieved through the random amplitudes associated with each basis vector making up |RV>. This also gives rise to the dispersion characterizing the method, which has been applied to a system (in the M shell) with≈250,000 lines where we have calculated up to the 5th moment. It turns out that the dispersion in the moments decreases with the size of the manifold, making its application to very big systems statistically advantageous. A discussion of the method and these dispersion characteristics will be presented.

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