Proper orthogonal decomposition analysis of spatio-temporal behavior of renal scintigraphies

Veltri, P.; Vecchio, A.; Carbone, V.

doi:10.1016/j.ejmp.2009.07.002

Cited by 9 publications

(8 citation statements)

References 36 publications

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“…In such non-homogeneous, non-isotropic situations, one can take advantage of the characteristics of the proper orthogonal decomposition (POD) technique (see, for instance, [18][19][20], and references therein). This approach, which has been widely employed in the study of the turbulent evolution of inhomogeneous flows, has been applied over time to other fields of investigation, in which the temporal evolution of a generic quantity, not necessarily "turbulent", but with a complicated structure, was the subject of study [7][8][9][10]. In the present article, we applied the technique to study the turbulent structures obtained from the temporal evolution of the temperature field extracted through a remote sensing analysis of the satellite images of the Faraji hurricane.…”

Section: Proper Orthogonal Decomposition Analysismentioning

confidence: 99%

“…Each pixel (representing a total area of 1 Km 2 for SEVIRI) is assigned a brightness temperature value, so that the image is a matrix representing the temperature field of the cyclone observed at a given time. Starting from this scalar field, we carried out the analysis of the spatiotemporal dynamics of cyclones using the proper orthogonal decomposition (POD) technique [7][8][9][10]. Thus, the original field was decomposed on the basis of empirical eigenfunctions extracted directly from the data by diagonalizing the autocorrelation matrix of the temperature field, integrated on the whole domain (Fredholm problem of the second kind).…”

Section: Introductionmentioning

confidence: 99%

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Study of Turbulence Associated with the Faraji Cyclone

Ciardullo

Primavera

Ferrucci

et al. 2022

Climate

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The formation of a cyclonic region in which nonlinear interactions generate turbulence in the form of small-scale vortices can be observed because of the different rotating air masses. Turbulence dynamics in cyclones (specifically hurricanes) has been under-researched; therefore, assessing the shear term is crucial to identify the onset of cyclonic formation within a region of the atmosphere. Earth observation techniques are able to provide relevant information on this physical process. In this article, we propose a new framework that is useful for connecting the study of the dynamics of a cyclonic system with the observations generated by geostationary satellite facilities. In particular, we applied the proper orthogonal decomposition (POD), a technique widely used in turbulent analysis to decompose a generic scalar or vector field in empirical eigenfunction, to investigate a tropical cyclone, the Faraji hurricane, from a dynamic point of view, beginning from the temporal evolution of its temperature field. The latter was obtained by elaborating on data and images collected by the SEVIRI radiometer, installed on the Meteosat Second Generation-8 (IODC) satellite. Using the POD, the energy spectra of both the spatial and temporal components of the temperature field obtained through remote sensing techniques were studied separately. Important information was then extracted and used for an in-depth characterization of the properties of the turbulence in the non-linear evolution of this phenomenon.

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Section: Proper Orthogonal Decomposition Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study of Turbulence Associated with the Faraji Cyclone

Ciardullo

Primavera

Ferrucci

et al. 2022

Climate

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show abstract

“…Furthermore, starting from a certain point in the mid 2000s, approaches based on matrix decomposition and data-driven methods began to gain recognition in medical analysis field. Veltri et al [23] used proper orthogonal decomposition (POD) to help the diagnosis of kidney diseases. When analyzing six datasets, four belong to patients affected by renal pathologies and two for healthy patients, POD was able to underline the regions of the organ interested by the troubles allowing to analyze them independently (see also [24], [25]).…”

Section: Introductionmentioning

confidence: 99%

Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis

Groun¹,

Villalba-Orero²,

Lara‐Pezzi³

et al. 2022

Preprint

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In this work, we study in detail the performance of Higher Order Dynamic Mode Decomposition (HODMD) technique when applied to echocardiography images. HODMD is a data-driven method generally used in fluid dynamics and in the analysis of complex non-linear dynamical systems modeling several complex industrial applications. In this paper we apply HODMD, for the first time to the authors knowledge, for patterns recognition in echocardiography, specifically, echocardiography data taken from several mice, either in healthy conditions or afflicted by different cardiac diseases. We exploit the HODMD advantageous properties in dynamics identification and noise cleaning to identify the relevant frequencies and coherent patterns for each one of the diseases. The echocardiography datasets consist of video loops taken with respect to a long axis view (LAX) and a short axis view (SAX), where each video loop covers at least three cardiac cycles, formed by (at most) 300 frames each (called snapshots). The proposed algorithm, using only a maximum quantity of 200 snapshots, was able to capture two branches of frequencies, representing the heart rate and respiratory rate. Additionally, the algorithm provided a number of modes, which represent the dominant features and patterns in the different echocardiography images, also related to the heart and the lung. Six datasets were analyzed: one echocardiography taken from a healthy subject and five different sets of echocardiography taken from subjects with either Diabetic Cardiomyopathy, Obesity, SFSR4 Hypertrophy, TAC Hypertrophy or Myocardial Infarction. The results show that HODMD is robust and a suitable tool to identify characteristic patterns able to classify the different pathologies studied.

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“…The Rayleigh–Ritz method has been applied to structural dynamics , compressible flow , and quantum chemistry , among others. In the past decades, the projection‐based MOR techniques have been widely applied to numerous areas, such as structural dynamics , turbulence flows , fluid dynamics , aeroelastic‐coupled fluid‐structure system and structural subsystem , electro‐thermal microelectromechanical system , and biological image analysis , to name just a few.…”

Section: Introductionmentioning

confidence: 99%

Proper orthogonal decomposition‐based model order reduction via radial basis functions for molecular dynamics systems

Lee

Chen

2013

Numerical Meth Engineering

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SUMMARYModel order reduction for molecular dynamics (MD) systems exhibits intrinsic complexities because of the highly nonlinear and nonlocal multi-atomic interactions in high dimensions. In the present work, we introduce a proper orthogonal decomposition-based method in conjunction with the radial basis function (RBF) approximation of the nonlinear and nonlocal potential energies and inter-atomic forces for MD systems. This approach avoids coordinate transformation between the physical and reduced-order coordinates, and allows the potentials and inter-atomic forces to be calculated directly in the reduced-order space. The RBFapproximated potential energies and inter-atomic forces in the reduced-order space are discretized on the basis of the Smolyak sparse grid algorithm to further enhance the effectiveness of the proposed method. The good approximation properties of RBFs in interpolating scattered data make them ideal candidates for the reduced-order approximation of MD inter-atomic force calculations. The proposed approach is validated by performing the reduced-order simulations of DNA molecules under various external loadings. Copyright

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Proper orthogonal decomposition analysis of spatio-temporal behavior of renal scintigraphies

Cited by 9 publications

References 36 publications

Study of Turbulence Associated with the Faraji Cyclone

Study of Turbulence Associated with the Faraji Cyclone

Higher Order Dynamic Mode Decomposition: from Fluid Dynamics to Heart Disease Analysis

Proper orthogonal decomposition‐based model order reduction via radial basis functions for molecular dynamics systems

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