Jean-Marc Girault

Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization

In the field of biological tissue characterization, fundamental acoustic attenuation properties have been demonstrated to have diagnostic importance. Attenuation caused by scattering and absorption shifts the instantaneous spectrum to the lower frequencies. Due to the time-dependence of the spectrum, the attenuation phenomenon is a time-variant process. This downward shift may be evaluated either by the maximum energy frequency of the spectrum or by the center frequency. In order to improve, in strongly attenuating media, the results given by the short-time Fourier analysis and the short-time parametric analysis, we propose two approaches adapted to this time-variant process: an adaptive method and a time-varying method. Signals backscattered by an homogeneous medium of scatterers are modeled by a computer algorithm with attenuation values ranging from 1 to 5 dB/cm MHz and a 45 MHz transducer center frequency. Under these conditions, the preliminary results obtained with the proposed time-variant methods, compared with the classical short-time Fourier analysis and the short-time auto-regressive (AR) analysis, are superior in terms of standard deviation (SD) of the attenuation coefficient estimate. This study, based on nonstationary AR spectral estimation, promises encouraging perspectives for in vitro and in vivo applications both in weakly and highly attenuating media.

New Estimators and Guidelines for Better Use of Fetal Heart Rate Estimators with Doppler Ultrasound Devices

Characterizing fetal wellbeing with a Doppler ultrasound device requires computation of a score based on fetal parameters. In order to analyze the parameters derived from the fetal heart rate correctly, an accuracy of 0.25 beats per minute is needed. Simultaneously with the lowest false negative rate and the highest sensitivity, we investigated whether various Doppler techniques ensure this accuracy. We found that the accuracy was ensured if directional Doppler signals and autocorrelation estimation were used. Our best estimator provided sensitivity of 95.5%, corresponding to an improvement of 14% compared to the standard estimator.

Compressed sensing of ultrasound images: Sampling of spatial and frequency domains

This paper proposes a comparison between an established (used in magnetic resonance imaging) and a innovative compressed sensing (CS) approach, both adapted to ultrasound (US) imaging. Two undersampling patterns suited to US imaging were investigated in each approach on simulated and in vivo radio-frequency US images. Reconstructions of simulated and in vivo US images using CS show minimal information loss. The best strategy (minimising the errors of reconstruction) was a uniform random sampling in the two directions of the spatial RF US image associated with the reconstruction of its k-space.

High resolution processing techniques for ultrasound Doppler velocimetry in the presence of colored noise. I. Nonstationary methods

Real-time flow velocity measurement is a practical issue in industrial and biomedical applications. Because their good frequency resolution, parametric methods such as autoregressive (AR) modeling and time-frequency distributions (TFD) are generally preferred to Fourier analysis. However, these methods become highly inaccurate in the presence of colored noise. We review here the principal parametric and nonparametric techniques and show their limitations in the estimation of Doppler frequency in the presence of strong colored noise. Different solutions to overcome these limitations are then proposed and compared using synthetic Doppler signals with colored noise.

High resolution processing techniques for ultrasound Doppler velocimetry in the presence of colored noise. II. Multiplephase pipe-flow velocity measurement

For pt.I see ibid., vol.50, no.3, p.267-78 (2003). This paper presents an application of continuous wave ultrasound Doppler velocity measurements to two-phase flow in pipes. In many petroleum wells, the multiphase flow is separated into two phases: the first is a liquid phase and the second is a gas phase with small scatterers. The problem of multiphase velocity profile measurements has not been satisfactorily solved by classical approaches due to the multiphase nature of the fluid and the presence of colored noise, which introduces a significant bias in classical frequency estimators. We propose the use of resolution frequency techniques to overcome the classical limitations. Direct estimation of Doppler frequency then obtained using either time frequency maximum frequency or arguments of poles of the parametric model that identifies the Doppler part of the signal is discussed. The tests made with synthetic Doppler signals and two-phase flow have demonstrated the excellent performance of the high resolution techniques based on reassignment and parametric techniques.

Analytical formulation of the fractal dimension of filtered stochastic signals

The aim of this study was to investigate the effects of a linear filter on the regularity of a given stochastic process in terms of the fractal dimension. This general approach, described in a continuous time domain, is new and is characterized by its simplicity. The framework of this problem is general since it emerges when a fractal process undertakes a transformation, as is the case in denoising or measurement processes.

Estimation of the blood Doppler frequency shift by a time-varying parametric approach

Doppler ultrasound is widely used in medical applications to extract the blood Doppler flow velocity in the arteries via spectral analysis. The spectral analysis of non-stationary signals and particularly Doppler signals requires adequate tools that should present both good time and frequency resolutions. It is well-known that the most commonly used time-windowed Fourier transform, which provides a time-frequency representation, is limited by the intrinsic trade-off between time and frequency resolutions. Parametric methods have then been introduced as an alternative to overcome this resolution problem. However, the performance of those methods deteriorates when high non-stationarities are present in the Doppler signal. For the purpose of accurately estimating the Doppler frequency shift, even when the temporal flow velocity is rapid (high non-stationarity), we propose to combine the use of the time-varying autoregressive (AR) method and the (dominant) pole frequency. This proposed method performs well in the context where non-stationarities are very high. A comparative evaluation has been made between classical (FFT based) and AR (both block and recursive) algorithms. Among recursive algorithms we test an adaptive recursive method as well as a time-varying recursive method. Finally, the superiority of the time-varying parametric approach in terms of frequency tracking and delay in the frequency estimate is illustrated for both simulated and in vivo Doppler signals.

Segmentation of dynamic PET images with kinetic spectral clustering

Segmentation is often required for the analysis of dynamic positron emission tomography (PET) images. However, noise and low spatial resolution make it a difficult task and several supervised and unsupervised methods have been proposed in the literature to perform the segmentation based on semi-automatic clustering of the time activity curves of voxels. In this paper we propose a new method based on spectral clustering that does not require any prior information on the shape of clusters in the space in which they are identified. In our approach, the p-dimensional data, where p is the number of time frames, is first mapped into a high dimensional space and then clustering is performed in a low-dimensional space of the Laplacian matrix. An estimation of the bounds for the scale parameter involved in the spectral clustering is derived. The method is assessed using dynamic brain PET images simulated with GATE and results on real images are presented. We demonstrate the usefulness of the method and its superior performance over three other clustering methods from the literature. The proposed approach appears as a promising pre-processing tool before parametric map calculation or ROI-based quantification tasks.

Optimization of contrast-to-tissue ratio by frequency adaptation in pulse inversion imaging

Contrast imaging has significantly improved clinical diagnosis by increasing the contrast-to-tissue ratio after microbubble injection. Pulse inversion imaging is the most commonly used contrast imaging technique because it greatly increases the contrast-to-tissue ratio by extracting microbubble nonlinearities. The main purpose of our study was to propose an automatic technique providing the best contrast- to-tissue ratio throughout the experiment. For reasons of simplicity, we proposed maximizing the contrast-to-tissue ratio with an appropriate choice of the transmit frequency. The contrast-to-tissue ratio was maximized by a closed-loop system including the pulse inversion technique. An algorithm based on gradient descent provided iterative determination of the optimal transmit frequency. The optimization method converged quickly after six iterations. This optimal control method is easy to implement and it optimizes the contrast-to-tissue ratio by adaptively selecting the transmit frequency.

Unsupervised Spectral Clustering for Segmentation of Dynamic PET Images

Segmentation of dynamic PET images is needed to extract the time activity curves (TAC) of regions of interest (ROI). These TAC can be used in compartmental models for in vivo quantification of the radiotracer target. While unsupervised clustering methods have been proposed to segment PET sequences, they are often sensitive to initial conditions or favour convex shaped clusters. Kinetic spectral clustering (KSC) of dynamic PET images was recently proposed to handle arbitrary shaped clusters in the space in which they are identified. While improved results were obtained with KSC compared to three state of art methods, its use for clinical applications is still hindered by the manual setting of several parameters. In this paper, we develop an extension of KSC to automatically estimate the parameters involved in the method and to make it deterministic. First, a global search procedure is used to locate the optimal cluster centroids from the projected data. Then an unsupervised clustering criterion is tailored and used in a global optimization scheme to automatically estimate the scale parameter and the weighting factors involved in the proposed Automatic and Deterministic Kinetic Spectral Clustering (AD-KSC). We validate the method using GATE Monte Carlo simulations of dynamic numerical phantoms and present results on real dynamic images. The deterministic results obtained with AD-KSC agree well with those obtained with optimal manual parameterization of KSC, and improve the ROI identification compared to three other clustering methods. The proposed approach could have significant impact for quantification of dynamic PET images in molecular imaging studies.