Transient multiexponential signals analysis using Bayesian deconvolution

Lai, Wei–Chih; Liu, Xianming; Chen, Weimin; Li, Xiaohua; Tang, Xiaosheng; Zang, Zhigang

doi:10.1016/j.amc.2015.05.032

Cited by 7 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, a transient characterization is under investigation. The definition of the adjusted heat capacity parameters is based on one-dimensional network identification using stochastic Bayesian deconvolution [23].…”

Section: Discussionmentioning

confidence: 99%

Thermo-Fluidic Characterizations of Multi-Port Compact Thermal Model of Ball-Grid-Array Electronic Package

et al. 2020

View full text Add to dashboard Cite

The concept of a single-input/multi-output thermal network was proposed by the Development of Libraries of Physical models for an Integrated design environment (DELPHI) consortium more than twenty years ago. The present work highlights the recent improvements made to efficiently derive a low-computing-effort model from a fully detailed numerical model and to characterize its performances. The temperature predictions of a deduced ball-grid-array (BGA) dynamic compact thermal model are compared to those of a realistic three-dimensional representation, including the large set of internal copper traces, as well as its board structure, which has been validated by experiment. The current study discloses a method for creating an amalgam reduced-order modal model (AROMM) for that electronic component family that allows the preservation of the geometry integrity and shortening scenarios computation. Typically, the AROMM method reduces by a factor of 600 the computation time needed to obtain the solution while keeping the error on the maximum temperature below 2%. Then, a meta-heuristic optimization is run to derive a more practical low-order resistor capacitor model that enables a thermo-fluidic analysis at the board level. Based on the calibrated numerical model, a novel AROMM method was investigated in order to address the chip behavior submitted to multiple heat sources. The first results highlight the capability to enforce a non-uniform power distribution on the upper surface of the silicon chip. Thus, the chip design layout can be analyzed and optimized to prevent thermal and reliability issues.

show abstract

Section: Discussionmentioning

confidence: 99%

Thermo-Fluidic Characterizations of Multi-Port Compact Thermal Model of Ball-Grid-Array Electronic Package

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Other studies have proposed methods that do not explicitly impose the number of compartments, but rather derive them from the data . Although prone to overfitting, such methods have been shown to provide good results in problems in which the number of components is difficult to establish a priori, such as crossing fibers or the joint diffusion– T 2 relaxometry quantification …”

Section: Introductionmentioning

confidence: 99%

“…Other studies have proposed methods that do not explicitly impose the number of compartments, but rather derive them from the data. [31][32][33][34] Although prone to overfitting, 35 such methods have been shown to provide good results in problems in which the number of components is difficult to establish a priori, such as crossing fibers [36][37][38] or the joint diffusion-T 2 relaxometry quantification. 39 Recently, Keil et al 40 have proposed to fit the data acquired at multiple diffusion weightings in three orthogonal directions with a deconvolution method to separate pseudo-diffusion from hindered diffusion in tissues.…”

Section: Introductionmentioning

confidence: 99%

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

et al. 2018

View full text Add to dashboard Cite

The diffusion‐weighted magnetic resonance imaging (dMRI) signal measured in vivo arises from multiple diffusion domains, including hindered and restricted water pools, free water and blood pseudo‐diffusion. Not accounting for the correct number of components can bias metrics obtained from model fitting because of partial volume effects that are present in, for instance, diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI). Approaches that aim to overcome this shortcoming generally make assumptions about the number of considered components, which are not likely to hold for all voxels. The spectral analysis of the dMRI signal has been proposed to relax assumptions on the number of components. However, it currently requires a clinically challenging signal‐to‐noise ratio (SNR) and accounts only for two diffusion processes defined by hard thresholds. In this work, we developed a method to automatically identify the number of components in the spectral analysis, and enforced its robustness to noise, including outlier rejection and a data‐driven regularization term. Furthermore, we showed how this method can be used to take into account partial volume effects in DTI and DKI fitting. The proof of concept and performance of the method were evaluated through numerical simulations and in vivo MRI data acquired at 3 T. With simulations our method reliably decomposed three diffusion components from SNR = 30. Biases in metrics derived from DTI and DKI were considerably reduced when components beyond hindered diffusion were taken into account. With the in vivo data our method determined three macro‐compartments, which were consistent with hindered diffusion, free water and pseudo‐diffusion. Taking free water and pseudo‐diffusion into account in DKI resulted in lower mean diffusivity and higher fractional anisotropy values in both gray and white matter. In conclusion, the proposed method allows one to determine co‐existing diffusion compartments without prior assumptions on their number, and to account for undesired signal contaminations within clinically achievable SNR levels.

show abstract

“…P(E) is the total likelihood (also known as the marginal likelihood) of the evidence at all possible points in the parameter space and acts as a normalization. The Bayesian method is employed (23,(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38) to estimate parameters where there are insufficient evidences. It has been used in fluorescence-lifetime imaging (30,32,37), F€ orster resonance energy transfer (30) and fluorescence correlation spectroscopy (35,36) experiments.…”

Section: Introductionmentioning

confidence: 99%

“…The Bayesian method is employed to estimate parameters where there are insufficient evidences. It has been used in fluorescence‐lifetime imaging , Förster resonance energy transfer and fluorescence correlation spectroscopy experiments.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Approach for Extracting Fluorescence Lifetimes from Sparse Data Sets and Its Significance for Imaging Experiments

Santra

Smith

Song

et al. 2018

Photochem & Photobiology

View full text Add to dashboard Cite

The measurement of fluorescence lifetimes, especially in small sample volumes, presents the dual challenge of probing a small number of fluorophores and fitting the concomitant sparse data set to the appropriate excited‐state decay function. A common method of analysis, such as the maximum likelihood (ML) technique, assumes a uniform probability distribution of the parameters describing the fluorescence decay function. An improvement is thus suggested by implementing a suitable nonuniform distribution, as is provided by a Bayesian framework, where the distribution of parameters is obtained from both their prior knowledge and the evidence‐based likelihood of an event for a given set of parameters. We have also considered the Dirichlet prior distribution, whose mathematical form enables analytical solutions of the fitting parameters to be rapidly obtained. If Gaussian and exponential prior distributions are judiciously chosen, they reproduce the experimental target lifetime to within 20% with as few as 20 total photon counts for the data set, as does the Dirichlet prior distribution. But because of the analytical solutions afforded by the Dirichlet prior distribution, it is proposed to employ a Dirichlet prior to search parameter space rapidly to provide, if necessary, appropriate parameters for subsequent employment of a Gaussian or exponential prior distribution.

show abstract

Transient multiexponential signals analysis using Bayesian deconvolution

Cited by 7 publications

References 22 publications

Thermo-Fluidic Characterizations of Multi-Port Compact Thermal Model of Ball-Grid-Array Electronic Package

Thermo-Fluidic Characterizations of Multi-Port Compact Thermal Model of Ball-Grid-Array Electronic Package

A robust deconvolution method to disentangle multiple water pools in diffusion MRI

A Bayesian Approach for Extracting Fluorescence Lifetimes from Sparse Data Sets and Its Significance for Imaging Experiments

Contact Info

Product

Resources

About