An Appraisal of Gardner Transform-Based Methods of Transient Multiexponential Signal Analysis

Jibia, Abdussamad Umar; Salami, Momoh Jimoh Eyiomika

doi:10.7763/ijcte.2012.v4.420

Cited by 13 publications

(10 citation statements)

References 17 publications

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“…Such extraction constitutes an inverse Laplace transform of noisy data, to which there is no unique solution. Different methods for solving this problem without the need for imposing the number of T2 components characterizing the model exist (37)(38)(39)(40)(41) and are still the matter of research for the optimization of inversion methods. Our choice for a regularized inversion method was based on the fact that it yields nonnegative solutions and allows regularization, which has been shown by our results to ensure the reproducibility of the solutions.…”

Section: Discussionmentioning

confidence: 99%

New Insights on Human Skeletal Muscle Tissue Compartments Revealed by In Vivo T2 NMR Relaxometry

Araujo

Fromes

Carlier

2014

Biophysical Journal

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The spin-spin (T2) relaxation of (1)H-NMR signals in human skeletal muscle has been previously hypothesized to reveal information about myowater compartmentation. Although experimental support has been provided, no consensus has yet emerged concerning the attribution of specific anatomical compartments to the observed T2 components. Potential application of a noninvasive tool that might offer such information urges the quest for a definitive answer to this question. The purpose of this work was to obtain new information that might help elucidate the mechanism of T2 distribution in muscle. To do so, in vivo T2 relaxation data was acquired from the soleus of eight healthy volunteers using a localized Carr-Purcell-Meiboom-Gill technique. Each acquisition contained 1000 echoes with an interecho spacing of 1 ms. Data were acquired from each subject under different vascular filling preparations expected to change exclusively the extracellular water fraction. Two exponential components were systematically observed: an intermediate component (T2 ~ 32 ms) and a long component (100 < T2 < 210 ms). The relative fraction and T2 value characterizing the long component systematically increased after progressive augmentation of extracellular water volume. Characteristic relaxation behavior for each vascular filling condition was analyzed with a two-site exchange model and a three-site two-exchange model. We show that a two-site exchange model can only predict the observations for small exchange rates, much more representative of transendothelial than transcytolemmal exchange regimes. The three-site two-exchange model representing the intracellular, interstitial, and vascular spaces was capable of precisely predicting the observations for realistic transcytolemmal and transendothelial exchange rates. The estimated intrinsic relative fractions of each of these compartments corroborate with estimations from previous works and strongly suggest that the T2 relaxation from water within the intracellular and interstitial spaces is described by the intermediate component, whereas the long component represents water within the vascular space.

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Section: Discussionmentioning

confidence: 99%

New Insights on Human Skeletal Muscle Tissue Compartments Revealed by In Vivo T2 NMR Relaxometry

Araujo

Fromes

Carlier

2014

Biophysical Journal

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“…In the relevant literature, especially in the d\omain of automation and control, different approaches for dynamic object identification are presented. [1][2][3][4][5][6][7][8][9][10][11][12][13] Many methods have been elaborated and are recommended for use in electrical and electronic applications. 1,2,[11][12][13] Typically, the system is presented as a passive multistage circuit containing resistances, capacitors and inductances.…”

Section: Introductionmentioning

confidence: 99%

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Strąkowska

Chatzipanagiotou

Mey

et al. 2020

Circuit Theory & Apps

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Summary This paper deals with the identification of the thermal parameters of multilayer objects using the concept of thermal impedance. In order to perform such identification, temperature evolution in time is obtained by an infrared camera after power excitation is applied in the investigated structure. Infrared thermography offers the advantage of being a noncontact temperature detection and measurement method. In many practical cases, it is impossible to use contact temperature measurements. Typically, the power in the form of a step function is applied. In order to calculate the thermal impedance of an object, temperature and power are converted into the frequency domain using the Laplace transform for s = jω. Then, the poles of the thermal impedance are identified using vector fitting, which allows calculating the thermal impedance as a sum of partial fractions. This corresponds directly to the Foster network of a thermal object. In addition, the vector fitting method offers much better convergence in comparison with other methods using the polynomial rational approximation of thermal impedance. A considerable improvement of the numerical Laplace transform in high frequency range is proposed. In this approach, the variable s = jω is replaced by trues~=()α+jω, and then, the integration result is corrected by the Taylor series. It leads to a kind of filtering of the temperature signal.

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“…Transform methods have also been developed [10], in which the data is Fourier transformed to create a spectral plot with spikes representing exponential components [11]. However, this approach exacerbates high frequency noise in the deconvolution process [14], causing ripples and broadening of the spectral peaks, making interpretations of the results difficult. Overall, there is a need to develop improved analysis methods for multi-exponential data.…”

Section: Introductionmentioning

confidence: 99%

Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data

Metcalfe-Smith

Meeus

Novák

et al. 2019

IEEE Trans. Biomed. Eng.

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Objective: A new method for fitting diffusionweighted magnetic resonance imaging (DW-MRI) data composed of an unknown number of multi-exponential components is presented and evaluated. Methods: The auto-regressive discrete acquisition points transformation (ADAPT) method is an adaption of the auto-regressive moving average system, which allows for the modeling of multiexponential data and enables the estimation of the number of exponential components without prior assumptions. ADAPT was evaluated on simulated DW-MRI data. The optimum ADAPT fit was then applied to human brain DWI data and the correlation between the ADAPT coefficients and the parameters of the commonly used bi-exponential intravoxel incoherent motion (IVIM) method were investigated. Results: The ADAPT method can correctly identify the number of components and model the exponential data. The ADAPT coefficients were found to have strong correlations with the IVIM parameters. ADAPT(1,1)-β 0 correlated with IVIM-D: ρ = 0.708, P < 0.001. ADAPT(1,1)-α 1 correlated with IVIM-f: ρ = 0.667, P < 0.001. ADAPT(1,1)-β 1 correlated with IVIM-D * : ρ = 0.741, P < 0.001). Conclusion: ADAPT provides a method that can identify the number of exponential components in DWI data without prior assumptions, and determine potential complex diffusion biomarkers. Significance: ADAPT has the potential to provide a generalized

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An Appraisal of Gardner Transform-Based Methods of Transient Multiexponential Signal Analysis

Cited by 13 publications

References 17 publications

New Insights on Human Skeletal Muscle Tissue Compartments Revealed by In Vivo T2 NMR Relaxometry

New Insights on Human Skeletal Muscle Tissue Compartments Revealed by In Vivo T2 NMR Relaxometry

Multilayer thermal object identification in frequency domain using IR thermography and vector fitting

Auto-Regressive Discrete Acquisition Points Transformation for Diffusion Weighted MRI Data

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