maximum likelihood expectation maximization
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2022 ◽  
Vol 17 (01) ◽  
pp. P01009
Author(s):  
K. Chaiwongkhot ◽  
T. Kin ◽  
Y. Nagata ◽  
T. Komori ◽  
N. Okamoto ◽  
...  

Abstract A feasibility demonstration of three-dimensional (3D) muon tomography was performed for infrastructure equivalent targets using the proposed portable muography detector. For the target, we used two sets of lead blocks placed at different heights. The detector consists of two muon position-sensitive detectors, made of plastic scintillating fibers (PSFs) and multi-pixel photon counters (MPPCs) with an angular resolution of 8 msr. In this work, the maximum likelihood-expectation maximization (ML-EM) method was used for the 3D imaging reconstruction of the muography. For both simulation and experiment, the reconstructed positions of the blocks produce consistent results with prior knowledge of the blocks' arrangement. This result demonstrates the potential of the 3D tomographic imaging of infrastructure by using seven detection positions for portable muography detectors to image infrastructure scale targets.


2021 ◽  
Vol 16 (12) ◽  
pp. C12005
Author(s):  
E. Panontin ◽  
A. Dal Molin ◽  
M. Nocente ◽  
G. Croci ◽  
J. Eriksson ◽  
...  

Abstract Unfolding techniques are employed to reconstruct the 1D energy distribution of runaway electrons from Bremsstrahlung hard X-ray spectrum emitted during plasma disruptions in tokamaks. Here we compare four inversion methods: truncated singular value decomposition, which is a linear algebra technique, maximum likelihood expectation maximization, which is an iterative method, and Tikhonov regularization applied to χ 2 and Poisson statistics, which are two minimization approaches. The reconstruction fidelity and the capability of estimating cumulative statistics, such as the mean and maximum energy, have been assessed on both synthetic and experimental spectra. The effect of measurements limitations, such as the low energy cut and few number of counts, on the final reconstruction has also been studied. We find that the iterative method performs best as it better describes the statistics of the experimental data and is more robust to noise in the recorded spectrum.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaya Prakash ◽  
Umang Agarwal ◽  
Phaneendra K. Yalavarthy

AbstractDigital rock is an emerging area of rock physics, which involves scanning reservoir rocks using X-ray micro computed tomography (XCT) scanners and using it for various petrophysical computations and evaluations. The acquired micro CT projections are used to reconstruct the X-ray attenuation maps of the rock. The image reconstruction problem can be solved by utilization of analytical (such as Feldkamp–Davis–Kress (FDK) algorithm) or iterative methods. Analytical schemes are typically computationally more efficient and hence preferred for large datasets such as digital rocks. Iterative schemes like maximum likelihood expectation maximization (MLEM) are known to generate accurate image representation over analytical scheme in limited data (and/or noisy) situations, however iterative schemes are computationally expensive. In this work, we have parallelized the forward and inverse operators used in the MLEM algorithm on multiple graphics processing units (multi-GPU) platforms. The multi-GPU implementation involves dividing the rock volumes and detector geometry into smaller modules (along with overlap regions). Each of the module was passed onto different GPU to enable computation of forward and inverse operations. We observed an acceleration of $$\sim 30$$ ∼ 30 times using our multi-GPU approach compared to the multi-core CPU implementation. Further multi-GPU based MLEM obtained superior reconstruction compared to traditional FDK algorithm.


2021 ◽  
Author(s):  
Valerie Nwadeyi ◽  
Paul maggi ◽  
Zhong He ◽  
Jerimy Polf

<p><i>Position sensitive CdZnTe Compton imaging cameras are currently being studied for their use of proton beam range verification for radiotherapy applications. This work presents the use of an experimental large volume CdZnTe detector for the detection of prompt gamma rays that are emitted from proton-nuclei interaction within plastic (C2H4) targets. Two experiments were conducted where the incident angle and the dose profile of the beam were varied. The energy spectra from these experiments show that the angle at which the beam enters the target can influence the photopeak to Compton continuum ratios, resulting in more than 18% increase at 718 keV when the beam is parallel to the detector. Images of the 718 keV and 4.44 MeV characteristic prompt gamma ray emission from carbon-proton interactions are reconstructed using list-mode maximum likelihood expectation maximization (MLEM). Images from these prompt gamma emissions line up well with the expected location of the proton beam within the plastic targets.</i><br></p>


2021 ◽  
Author(s):  
Valerie Nwadeyi ◽  
Paul maggi ◽  
Zhong He ◽  
Jerimy Polf

This manuscript discusses the use of a large volume array CZT detector for experimental prompt gamma-ray imaging. Namely, the 718 keV and the 4.44 MeV photopeaks produced from proton-carbon interactions are imaged using maximum likelihood expectation maximization (MLEM). Various proton beam irradiations are used to characterize the feasibility of using both photopeaks for beam range verification.


2021 ◽  
Author(s):  
Valerie Nwadeyi ◽  
Paul maggi ◽  
Zhong He ◽  
Jerimy Polf

This manuscript discusses the use of a large volume array CZT detector for experimental prompt gamma-ray imaging. Namely, the 718 keV and the 4.44 MeV photopeaks produced from proton-carbon interactions are imaged using maximum likelihood expectation maximization (MLEM). Various proton beam irradiations are used to characterize the feasibility of using both photopeaks for beam range verification.


2021 ◽  
Author(s):  
Valerie Nwadeyi ◽  
Paul maggi ◽  
Zhong He ◽  
Jerimy Polf

This manuscript discusses the use of a large volume array CZT detector for experimental prompt gamma-ray imaging. Namely, the 718 keV and the 4.44 MeV photopeaks produced from proton-carbon interactions are imaged using maximum likelihood expectation maximization (MLEM). Various proton beam irradiations are used to characterize the feasibility of using both photopeaks for beam range verification.


2020 ◽  
pp. 1-15
Author(s):  
Xuan Zheng ◽  
Gangrong Qu ◽  
Jiajia Zhou

BACKGROUND: A statistical method called maximum likelihood expectation maximization (MLEM) is quite attractive, especially in PET/SPECT. However, the convergence rate of the iterative scheme of MLEM is quite slow. OBJECTIVE: This study aims to develop and test a new method to speed up the convergence rate of the MLEM algorithm. METHODS: We introduce a relaxation parameter in the conventional MLEM iterative formula and propose the relaxation strategy on the condition that the spectral radius of the derived iterative matrix from the iterative scheme with the accelerated parameter reaches a minimum value. RESULTS: Experiments with Shepp-Logan phantom and an annual tree image demonstrate that the new computational strategy effectively accelerates computation time while maintains reasonable image quality. CONCLUSIONS: The proposed new computational method involving the relaxation strategy has a faster convergence speed than the original method.


AJEA ◽  
2020 ◽  
Author(s):  
Mariana Prieto Canalejo ◽  
Daniel Minsky

La Tomografía por Emisión de Positrones (PET) permite cuantificar el metabolismo celular, siendo una de las técnicas más poderosas en el diagnóstico precoz deenfermedades cardíacas, oncológicas y neurológicas. Las imágenes se obtienen mediante la administración de un radiofármaco que marca el proceso metabólico a estudiar. Los rayos gamma que emite el radiotrazador, inciden en detectores ubicados alrededor del paciente generando eventos que se almacenan para la reconstrucción de la imagen. Para obtener imágenes cuantificables, se realizan diversas correcciones a los eventos, entre ellas por eventos dispersos. Con el objetivo de implementar la corrección por eventos dispersos, se realiza, en primera instancia, el modelado de los mismos por medio del algoritmo Single Scatter Simulation (SSS) obteniendo una estimación de la dispersión. Posteriormente se realiza la corrección en la imagen de emisión. Para la corrección se incorpora al algoritmo de reconstrucción MLEM (maximum-likelihood expectation–maximization) la estimación obtenida con el modelado. Los resultados preliminares, obtenidos en simulaciones de volúmenes de actividad constante, han demostrado que la implementación realizada mejora la homogeneidad de la imagen de emisión.


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