Abstract:A new time-varying autoregressive (TVAR) modelling approach is proposed for nonstationary signal processing and analysis, with application to EEG data modelling and power spectral estimation. In the new parametric modelling framework, the time-dependent coefficients of the TVAR model are represented using a novel multi-wavelet decomposition scheme. The timevarying modelling problem is then reduced to regression selection and parameter estimation, which can be effectively resolved by using a forward orthogonal regression algorithm. Two examples, one for an artificial signal and another for an EEG signal, are given to show the effectiveness and applicability of the new TVAR modelling method.
This study introduces a method to reduce the artefact caused by metal implant, and improve the image quality. The factors affecting the image quality of the existing methods are analysed from a different point of view. The observed projection is decomposed into several components including the correspondence of biological tissues, the correspondence of metal implant, noises, and inconsistencies caused by beam hardening. The correspondence of metal implant is identified by initial reconstruction and forward modelling, and the noises are detected and isolated from the projection in wavelet domain. The inconsistencies are approximated and compensated. A patient with medial epicondyle of right femur replaced using femoral component made by Biomet Inc was scanned using a GE Lightspeed 16 X-ray CT scanner and the observed projection is processed by this method. The output image shows that all of the streaking caused by metal implant is eliminated, and the darkness areas caused by beam hardening are retrieved. Comparing with the default output of the CT scanner, the image quality has been significantly improved.
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