Coronary magnetic resonance angiography (MRA) acquired using steady-state free precession (SSFP) sequences tends to suffer from image artifacts caused by local magnetic field inhomogeneities. Flow- and gradient-switching-induced eddy currents are important sources of such phase errors, especially under off-resonant conditions. In this study, we propose to reduce these image artifacts by using a linear centric-encoding (LCE) scheme in the phase-encoding (PE) direction. Abrupt change in gradients, including magnitude and polarity between consecutive radiofrequency cycles, is minimized using the LCE scheme. Results from numeric simulations and phantom studies demonstrated that signal oscillation can be markedly reduced using LCE as compared to conventional alternating centric-encoding (ACE) scheme. The image quality of coronary arteries was improved at both 1.5 and 3.0 T using LCE compared to those acquired using ACE PE scheme (1.5 T: ACE/LCE=2.2+/-0.8/3.0+/-0.6, P=.02; 3.0 T: ACE/LCE=2.1+/-1.1/3.0+/-0.8, P=.01). In conclusion, flow- and eddy-currents-induced imaging artifacts in coronary MRA using SSFP sequence can be markedly reduced with LCE acquisition of PE lines. 相似文献
Raman spectroscopy combined with surface enhanced technology was adopted for analysis of phosmet pesticide. Continuous wavelet transforms (CWT) and successive projections algorithm (SPA) were used for Raman spectral preprocess and characteristic Raman shifts selection, respectively. Multi-linear regression (MLR) was used for spectral modeling. It is shown that enhanced chips can achieve enhanced Raman spectral signal for low concentration of pesticides. CWT can improve spectral resolution and smoothness, and remove translation error. Characteristic Raman shifts selection method of SPA can improve analytical precision, and simplify modeling variables of MLR CWT-SPA-MLR model can improve correlation coefficient (r) of prediction from 0. 823 to 0. 903, and reduce root mean square error of prediction (RMSEP) from 1. 640 to 1. 122. CWT-SPA-MLR method can be used for constructing analytical models for Raman spectra and has good interpretability and repeatability. 相似文献
Wavelet transform is a versatile time‐frequency analysis technique, which allows localization of useful signals in time or space and separates them from noise. The detector output from any analytical instrument is mathematically equivalent to a digital image. Signals obtained in chemical separations that vary in time (e.g., high‐performance liquid chromatography) or space (e.g., planar chromatography) are amenable to wavelet analysis. This article gives an overview of wavelet analysis, and graphically explains all the relevant concepts. Continuous wavelet transform and discrete wavelet transform concepts are pictorially explained along with their chromatographic applications. An example is shown for qualitative peak overlap detection in a noisy chromatogram using continuous wavelet transform. The concept of signal decomposition, denoising, and then signal reconstruction is graphically discussed for discrete wavelet transform. All the digital filters in chromatographic instruments used today potentially broaden and distort narrow peaks. Finally, a low signal‐to‐noise ratio chromatogram is denoised using the procedure. Significant gains (>tenfold) in signal‐to‐noise ratio are shown with wavelet analysis. Peaks that were not initially visible were recovered with good accuracy. Since discrete wavelet transform denoising analysis applies to any detector used in separation science, researchers should strongly consider using wavelets for their research. 相似文献
The wavelet multiresolution interpolation for continuous functions defined on a finite interval is developed in this study by using a simple alternative of transformation matrix. The wavelet multiresolution interpolation Galerkin method that applies this interpolation to represent the unknown function and nonlinear terms independently is proposed to solve the boundary value problems with the mixed Dirichlet-Robin boundary conditions and various nonlinearities, including transcendental ones, in which the discretization process is as simple as that in solving linear problems, and only common two-term connection coefficients are needed. All matrices are independent of unknown node values and lead to high efficiency in the calculation of the residual and Jacobian matrices needed in Newton’s method, which does not require numerical integration in the resulting nonlinear discrete system. The validity of the proposed method is examined through several nonlinear problems with interior or boundary layers. The results demonstrate that the proposed wavelet method shows excellent accuracy and stability against nonuniform grids, and high resolution of localized steep gradients can be achieved by using local refined multiresolution grids. In addition, Newton’s method converges rapidly in solving the nonlinear discrete system created by the proposed wavelet method, including the initial guess far from real solutions.
Most of the techniques developed for infrared (IR) image enhancement (IE) depend heavily on the scene, environmental conditions, and the properties of the imaging system. So, with a set of predefined scenario properties, a content-based IR-IE technique can be developed for better situational awareness. This study proposes an adaptive IR-IE technique based on clustering of wavelet coefficients of an image for sea surveillance systems. Discrete wavelet transform (DWT) of an image is computed and feature vectors are constructed from subband images. Clustering operation is applied to group similar feature vectors that belong to different scene components such as target or background. Depending on the feature vectors, a weight is assigned to each cluster and these weights are used to compute gain matrices which are used to multiply wavelet coefficients for the enhancement of the original image. Enhancement results are presented and a comparison of the performance of the proposed algorithm is given through subjective tests with other well known frequency and histogram based enhancement techniques. The proposed algorithm outperforms previous ones in the truthfulness, detail visibility of the target, artificiality, and total quality criteria, while providing an acceptable computational load. 相似文献