Sébastien Ménigot

ESEO Group France


He was born in France in 1985. He received his master's degree in medical imaging technology from the University of Tours (France) in 2008. He obtained his Ph.D. degree in medical ultrasound imaging from the University of Tours in 2011.

From september 2018, he is associate professor at ESEO Group, Angers (France). He is also associate researcher at LAUM (UMR-CNRS 6613, Université du Maine, Le Mans, France).

His research interests are in signal processing applied to ultrasound imaging and monitoring (detection, modeling, optimization, beamforming, image reconstruction, waveform design). As such, he developed the optimal control concept for ultrasound imaging :
  automatic waveform design by deterministic and stochastic optimal command applied to ultrasound imaging system;
  development of imaging method adjusted to capacitive micromachined transducers (CMUT) ;
  sub-ultra-harmonics modelling and identification applied to ultrasound contrast agents;
  Doppler detectors and estimators applied to microemboli detection for stroke prevention.
These researches have been done in the lab Imaging and brain from 2008 to 2017 and in the lab Greman from 2017 to 2018.
Finally, he is also a member of the French Society of Acoustics and of IEEE.

He was a teaching assistant in electronics and computer science at the Institut of Technology of Ville d'Avray, Université Paris-X (France) between 2012 and 2014.
He has teached computer science and embedded system in the electronics department of the engineering school Polytech Tours since 2014. He is also the correspondent of the intensive foundation degree Peip department (since 2015) and communcation (since 2016) for the electronics department .

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Detection of Weak Doppler Microembolic Signature using Two-Dimensional-Adaptive Time-Frequency Threshold from Spectrogram


Prevention of cerebrovascular accidents (CVA) can be achieved by detecting their related precursor signs. A new generation of transcranial Doppler (TCD) systems is presented for detecting the smallest possible microemboli. However, many artefacts appear with these mono-gated Holter TCDs. Thus, the aim of the method becomes achieving microembolic detection while rejecting artefacts. For the clinicians’ procedure, the detection proposed is based on an adaptive thresholding applied on the spectrogram of the Doppler signal. The method required achieving three steps. First, the beginning of each cardiac cycle is assessed from the spectrogram of the Doppler signal. Second, by assuming that the Doppler signal is pseudo-cyclostationary, the spectrogram are segmented and time-normalised into sub-spectrograms for each cardiac cycles. Two two-dimensional-adaptive (2D-adaptive) thresholds of detection for microemboli and artefacts were statistically adjusted in both time and frequency. Third, the microembolus detection consists in both detecting the over-intensities in the sub-spectrograms and checking if the detected signatures are not artefacts. The ROC curve results show that the performances are 3.6 times higher compared to those of the standard detection. The detection rate can be increased by 22% compared to standard detection. Besides, the false alarm rate can be reduced by 28%. Using an 2D-adaptive threshold adjusted in both time and frequency, microemboli of weaker intensity can be detected. The analysis of a long acquisition could be possible, and better support of high-risk asymptomatic patient could be considered.

Optimal pump excitation frequency for improvement of damage detection by nonlinear vibro acoustic modulation method in a multiple scattering sample


We present a method to systematically optimize nonlinear damage detection in multiple scattering media by the nonlinear Vibro-Acoustic Modulation (VAM) technique. The latter consists here of exciting a medium simultaneously with a high frequency ultrasonic sinusoidal burst and with a low frequency continuous sinusoidal wave. Modulation of the high frequency (probe) by the low frequency (pump) is made possible by the presence of nonlinear scatterers, i.e. cracks, defects. A signal processing technique consisting of a closed loop system drives the automatic adaptation of the pumping frequency, yielding to the optimization of the nonlinear modulation (NM) of the output probing coda signal without a priori information on the medium and the scatterers. The correlation coefficient between a reference output probe signal without the pumping wave and an output modulated probe signal with a pumping wave was considered as our cost function. A multiple scattering solid beam where nonlinear scatterers can be controllably added or removed is designed and tested. The first step of this study is an empirical search of the correlation coefficient dependency on the pumping frequency to verify the performances of the proposed method. Then the implemented optimization algorithm based on genetic algorithm (GA) is used to find automatically the optimal pumping frequency. The obtained optimization results show a good agreement with the empirical study. Moreover, the genetic algorithm allowed to find the optimal pump frequency adapted to each configuration of nonlinear scatterers. This relatively fast search of the optimal nonlinear response could be extended to nonlinear scatterer imaging applications using the information on the resonant modes spatial shapes together with the associated optimal response.

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