Analytical and experimental comparisons between the frequency-modulated–frequency-shift measurement and the pulsed-wave–time-shift measurement Doppler systems

In previous publications, a new echo-ranging Doppler system based on transmission of repetitive coherent frequency-modulated (FM) sinusoids in two different implementations was presented. One of these implementations, the frequency-modulated-frequency-shift measurement (FM-fsm) Doppler system is, in this paper, compared with its pulsed-wave counterpart, the pulsed-wave-time-shift measurement (PW-tsm) Doppler system. When using transmitted PW and FM signals with a Gaussian envelope, the parallelism. between the two systems can be stated explicitly and comparison can be made between the main performance indices for the two Doppler systems. the performance of the FM and PW Doppler systems is evaluated by means of numerical simulation and measurements of actual flow profiles. the results indicate that the two Doppler systems have very similar levels of performance

Surface roughness detection of arteries via texture analysis of ultrasound images for early diagnosis of atherosclerosis

There is a strong research interest in identifying the surface roughness of the carotid arterial inner wall via texture analysis for early diagnosis of atherosclerosis. The purpose of this study is to assess the efficacy of texture analysis methods for identifying arterial roughness in the early stage of atherosclerosis. Ultrasound images of common carotid arteries of 15 normal mice fed a normal diet and 28 apoE−/− mice fed a high-fat diet were recorded by a high-frequency ultrasound system (Vevo 2100, frequency: 40 MHz). Six different texture feature sets were extracted based on the following methods: first-order statistics, fractal dimension texture analysis, spatial gray level dependence matrix, gray level difference statistics, the neighborhood gray tone difference matrix, and the statistical feature matrix. Statistical analysis indicates that 11 of 19 texture features can be used to distinguish between normal and abnormal groups (p<0.05). When the 11 optimal features were used as inputs to a support vector machine classifier, we achieved over 89% accuracy, 87% sensitivity and 93% specificity. The accuracy, sensitivity and specificity for the k-nearest neighbor classifier were 73%, 75% and 70%, respectively. The results show that it is feasible to identify arterial surface roughness based on texture features extracted from ultrasound images of the carotid arterial wall. This method is shown to be useful for early detection and diagnosis of atherosclerosis.Lili Niu, Ming Qian, Wei Yang, Long Meng, Yang Xiao, Kelvin K. L. Wong, Derek Abbott, Xin Liu, Hairong Zhen