scholarly journals Use of non-Gaussian time-of-flight kernels for image reconstruction of Monte Carlo simulated data of ultra-fast PET scanners

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Nikos Efthimiou ◽  
Kris Thielemans ◽  
Elise Emond ◽  
Chris Cawthorne ◽  
Stephen J. Archibald ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Image Reconstruction ◽  
Time Of Flight ◽  
Simulated Data ◽  
Non Gaussian ◽  
Pet Scanners ◽  
Gaussian Time

scholarly journals A Simple Contrast Matching Rule for OSEM Reconstructed PET Images with Different Time of Flight Resolution

2021 ◽  
Vol 11 (16) ◽  
pp. 7548
Author(s):  
Luca Presotto ◽  
Valentino Bettinardi ◽  
Elisabetta De Bernardi
Keyword(s):  
Technological Development ◽  
Time Of Flight ◽  
Simulated Data ◽  
Matching Rule ◽  
Timing Resolution ◽  
Positron Emission ◽  
Contrast Matching ◽  
Coincidence Timing ◽  
Digital Simulations ◽  
Pet Scanners

Background: Time-of-Flight (TOF) is a leading technological development of Positron Emission Tomography (PET) scanners. It reduces noise at the Maximum-Likelihood solution, depending on the coincidence–timing–resolution (CTR). However, in clinical applications, it is still not clear how to best exploit TOF information, as early stopped reconstructions are generally used. Methods: A contrast-recovery (CR) matching rule for systems with different CTRs and non-TOF systems is theoretically derived and validated using (1) digital simulations of objects with different contrasts and background diameters, (2) realistic phantoms of different sizes acquired on two scanners with different CTRs. Results: With TOF, the CR matching rule prescribes modifying the iterations number by the CTRs ratio. Without TOF, the number of iterations depends on the background dimension. CR matching was confirmed by simulated and experimental data. With TOF, image noise followed the square root of the CTR when the rule was applied on simulated data, while a significant reduction was obtained on phantom data. Without TOF, preserving the CR on larger objects significantly increased the noise. Conclusions: TOF makes PET reconstructions less dependent on background dimensions, thus, improving the quantification robustness. Better CTRs allows performing fewer updates, thus, maintaining accuracy while minimizing noise.

scholarly journals Robust Weighted l1,2 Norm Filtering in Passive Radar Systems

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3270 ◽  
Author(s):  
Baris Satar ◽  
Gokhan Soysal ◽  
Xue Jiang ◽  
Murat Efe ◽  
Thiagalingam Kirubarajan
Keyword(s):  
Target Detection ◽  
Impulsive Noise ◽  
Simulated Data ◽  
Real Data ◽  
Detection Performance ◽  
Passive Radar ◽  
Mobile Telecommunication ◽  
Telecommunication System ◽  
Conventional Methods ◽  
Non Gaussian

Conventional methods such as matched filtering, fractional lower order statistics cross ambiguity function, and recent methods such as compressed sensing and track-before-detect are used for target detection by passive radars. Target detection using these algorithms usually assumes that the background noise is Gaussian. However, non-Gaussian impulsive noise is inherent in real world radar problems. In this paper, a new optimization based algorithm that uses weighted l 1 and l 2 norms is proposed as an alternative to the existing algorithms whose performance degrades in the presence of impulsive noise. To determine the weights of these norms, the parameter that quantifies the impulsiveness level of the noise is estimated. In the proposed algorithm, the aim is to increase the target detection performance of a universal mobile telecommunication system (UMTS) based passive radars by facilitating higher resolution with better suppression of the sidelobes in both range and Doppler. The results obtained from both simulated data with α stable distribution, and real data recorded by a UMTS based passive radar platform are presented to demonstrate the superiority of the proposed algorithm. The results show that the proposed algorithm provides more robust and accurate detection performance for noise models with different impulsiveness levels compared to the conventional methods.

A Monte-Carlo study of image reconstruction for PET

2003 ◽  
Author(s):  
S. Pavlopoulos ◽  
G. Tzanakos ◽  
M. Bhatia
Keyword(s):  
Monte Carlo ◽  
Image Reconstruction ◽  
Monte Carlo Study

scholarly journals Parallel Markov chain Monte Carlo for non-Gaussian posterior distributions

Stat ◽  
2015 ◽  
Vol 4 (1) ◽  
pp. 304-319 ◽  
Author(s):  
Alexey Miroshnikov ◽  
Zheng Wei ◽  
Erin Marie Conlon
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Posterior Distributions ◽  
Non Gaussian

scholarly journals A Bayesian model for binary Markov chains

2004 ◽  
Vol 2004 (8) ◽  
pp. 421-429 ◽  
Author(s):  
Souad Assoudou ◽  
Belkheir Essebbar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Bayesian Estimation ◽  
Bayesian Model ◽  
Transition Probabilities ◽  
Simulated Data ◽  
Bayesian Estimator ◽  
Jeffreys Prior ◽  
Data Set

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

Recent Developments in Image Reconstruction Using a Time-of-Flight-Assisted Positron Emission Tomograph: Super PETT I

1984 ◽  
Vol 31 (1) ◽  
pp. 605-608 ◽  
Author(s):  
D. C. Ficke ◽  
D. E. Beecher ◽  
G. R. Hoffman ◽  
T. J. Holmes ◽  
M. M. Ter-Pogossian
Keyword(s):  
Image Reconstruction ◽  
Time Of Flight ◽  
Positron Emission Tomograph ◽  
Positron Emission ◽  
Recent Developments

Structural Modal Multifurcation With Internal Resonance: Part 2—Stochastic Approach

1993 ◽  
Vol 115 (2) ◽  
pp. 193-201 ◽  
Author(s):  
R. A. Ibrahim ◽  
B. H. Lee ◽  
A. A. Afaneh
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Axial Load ◽  
Random Excitation ◽  
Stochastic Bifurcation ◽  
Density Level ◽  
Analytical Treatment ◽  
Asymmetric Mode ◽  
Non Gaussian ◽  
Gaussian Closure

Stochastic bifurcation in moments of a clamped-clamped beam response to a wide band random excitation is investigated analytically, numerically, and experimentally. The nonlinear response is represented by the first three normal modes. The response statistics are examined in the neighborhood of a critical static axial load where the normal mode frequencies are commensurable. The analytical treatment includes Gaussian and non-Gaussian closures. The Gaussian closure fails to predict bifurcation of asymmetric modes. Both non-Gaussian closure and numerical simulation yield bifurcation boundaries in terms of the axial load, excitation spectral density level, and damping ratios. The results of both methods are in good agreement only for symmetric response characteristics. In the neighborhood of the critical bifurcation parameter the Monte Carlo simulation yields strong nonstationary mean square response for the asymmetric mode which is not directly excited. Experimental and Monte Carlo simulation exhibit nonlinear features including a shift of the resonance peak in the response spectra as the excitation level increases. The observed shift is associated with a widening effect in the response bandwidth.

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