Real Time Waveguide Parameter Estimation Using Sparse Multimode Disperse Radon Transform

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dc.contributor.authorAraya, Claudio
dc.contributor.authorMartinez, Alejandro
dc.contributor.authorRamiandrisoam Donatien
dc.contributor.authorTa, Dean
dc.contributor.authorXu, Kailiang
dc.contributor.authorOsses, Axel
dc.contributor.authorMinonzio, Jean-Gabriel
dc.date.accessioned2022-11-30T02:46:10Z
dc.date.available2022-11-30T02:46:10Z
dc.date.issued2021
dc.description.abstractOsteoporosis and associated fragility fractures are still a societal problem. Several quantitative ultrasound approaches have been proposed to overcome limitations of the current gold standard DXA. Bi Directional Axial Transmission (BDAT) is based on the measurement of waves guided by the cortical bone shell. Cortical thickness (Ct.Th) and porosity (Ct.Po) estimates correspond to the maxima of the objective function Proj(Ct.Th,Ct.Po), initially defined as the projection of a tested model in the singular vector basis (method 1). Each model matrix has the same dimension, i.e., Nf=124 x Nk=256, 512, 1024 or 2048 pixels, of an ultrasonic guided wave spectrum experimental image Norm(f,k). The total number of models is equal to Nth,=38 x Npo=25, i.e., the number of cortical thickness and porosity taken into account, ranging respectively from 0.8 to 4.5 mm and 1 to 25%. Finally, each pixel of the alternative objective function (NthxNpo pixels) corresponds to the pixel-wise image multiplication between one model and the experimental guided wave spectrum image (method 2) or a sparse matrix multiplication between experimental and model reshaped vectors (method 3). The three methods were tested on data obtained on 400 measurements. It was observed that methods 2 and 3 provided the same Ct.Th Ct.Po values while differences with method 1 decreased with Nk. Acceptable differences, i.e., lower than the typical measurement resolution (0.2 mm for Ct.Th and 1% for Ct.Po) were achieved for Nk=2048. Using Matlab on a standard desktop, this calculation took 20, 4 and 0.3 s, for the methods 1 to 3, respectively. Method 3 calculation was achieved in 5 ms using C++. This last value opens perspective toward guiding interface improvement using real time objective function.en_ES
dc.facultadFacultad de Ingenieríaen_ES
dc.file.nameAraya_Rea2021.pdf
dc.identifier.citationC. Araya et al., "Real Time Waveguide Parameter Estimation Using Sparse Multimode Disperse Radon Transform," 2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS), 2021, pp. 1-4, doi: 10.1109/LAUS53676.2021.9639171.en_ES
dc.identifier.doi10.1109/LAUS53676.2021.9639171.
dc.identifier.urihttp://repositoriobibliotecas.uv.cl/handle/uvscl/7224
dc.languageen
dc.publisherIEEE
dc.source2021 IEEE UFFC Latin America Ultrasonics Symposium (LAUS)
dc.subjectTRANSMISSION LINE MATRIX METHODSen_ES
dc.subjectULTRASONIC IMAGINGen_ES
dc.subjectULTRASONIC VARIABLES MEASUREMENTen_ES
dc.subjectTRANSFORMSen_ES
dc.subjectLINEAR PROGRAMMINGen_ES
dc.subjectMATHEMATICAL MODELSen_ES
dc.subjectACOUSTICSen_ES
dc.titleReal Time Waveguide Parameter Estimation Using Sparse Multimode Disperse Radon Transform
dc.typeArticulo
uv.departamentoEscuela de Ingenieria Informatica
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