Échographie

Résumé

vignette|240px|droite|Échographie d'un fœtus de neuf semaines. L'échographie est une technique d' employant des ultrasons. Elle est utilisée de manière courante en médecine humaine et vétérinaire, mais peut aussi être employée en recherche et dans l'industrie. Terminologie Le mot « échographie » provient de la nymphe Écho dans la mythologie grecque qui personnifiait ce phénomène et d'une racine grecque Graphô (écrire). Il se définit donc comme étant « un écrit par l'écho ». Le terme « échographie » désigne aussi bien l'acte médical que l'image qui en découle, abrégé au féminin en « une écho ». L'appareil permettant l'échographie est un « échographe ». Les appareils modernes comportent tous une fonction Doppler. C'est pourquoi on parle d'« échographie Doppler » (abrégée en « écho-doppler »). Le médecin, le manipulateur en électroradiologie médicale, ou la sage femme qui pratique une échographie est un « échographiste ». Histoire L'échographie moderne est le fru

Source officielle

https://fr.wikipedia.org/wiki/%C3%89chographie

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Publications associées (60)

Simultaneous prediction of wrist and hand motions is essential for the natural interaction with hand prostheses. In this paper, we propose a novel multi-out Gaussian process (MOGP) model and a multi-task deep learning (MTDL) algorithm to achieve simultaneous prediction of wrist rotation (pronation/supination) and finger gestures for transradial amputees via a wearable ultrasound array. We target six finger gestures with concurrent wrist rotation in four transradial amputees. Results show that MOGP outperforms previously reported subclass discriminant analysis for both predictions of discrete finger gestures and continuous wrist rotation. Moreover, we find that MTDL has the potential to improve the accuracy of finger gesture prediction compared to MOGP and classification-specific deep learning, albeit at the expense of reducing the accuracy of wrist rotation prediction. Extended comparative analysis shows the superiority of ultrasound over surface electromyography. This paper prioritizes exploring the performance of wearable ultrasound on the simultaneous prediction of wrist and hand motions for transradial amputees, demonstrating the potential of ultrasound in future prosthetic control. Our ultrasound-based adaptive prosthetic control dataset (Ultra-Pro) will be released to promote the development of the prosthetic community.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

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Ultrasound can non-invasively detect muscle deformations, which has great potential applications in prosthetic hand control. This research developed a miniaturized ultrasound device that could be integrated into a prosthetic hand socket. This compact system included four A-mode ultrasound transducers, a signal processing module, and a prosthetic hand control module. The size of the ultrasound system was 65 * 75 * 25 mm, weighing only 85 g. For the first time, we integrated the ultrasound system into a prosthetic hand socket to evaluate its performance in practical prosthetic hand control. We designed an experiment to perform six commonly used gestures, and the classification accuracy was 95.33% +/- 7.26% for a participant. These experimental results demonstrated the efficacy of the designed prosthetic system based on the miniaturized A-mode ultrasound device, paving the way for an effective HMI system that could be widely used in prosthetic hand control.

SPRINGER INTERNATIONAL PUBLISHING AG2022

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Echocardiography is a widely used imaging technique to examine myocardial function in patients with known or suspected heart disease. The analysis of ventricular wall motion and deformation, in particular, allows to assess the extent of myocardial ischemia and infarction. In clinical practice, the analysis mainly relies on visual inspection or manual measurements by experienced cardiologists. Manual methods are tedious and time-consuming, and visual assessment leads to qualitative and subjective diagnoses that suffer from considerable inter- and intraobserver variability. Automating the analysis of echocardiographic images is therefore highly desirable but also challenging because of the low image quality and the high amount of speckle noise. In this thesis, we propose a framework for robust and quantitative analysis of echocardiographic sequences. We make the following key contributions: Motion and Deformation Analysis—We propose a novel optical-flow-based algorithm to estimate ventricular wall motion from B-mode echocardiograms. To account for typical heart motions such as contraction/expansion and shear, we use a local affine model for the velocity in space and time. An attractive feature of the affine motion model is that it gives also access to local strain rate parameters that describe local myocardial deformation such as wall thickening. The motion parameters are estimated in the least-squares sense within a sliding spatio-temporal B-spline window. The estimation of large motions is made possible through the use of a coarse-to-fine multi-scale strategy, which also adds robustness to the method. Computational Efficiency—We introduce the notion of multiresolution moment filters, a novel filtering scheme to compute local weighted geometric moments efficiently at dyadic scales by using a wavelet-like algorithm. Beyond their application in motion analysis, we demonstrate their usefulness for image denoising and feature extraction. Multi-Modality—We extend the proposed motion analysis algorithm by integrating directional, Doppler-based velocity measurements. The exploitation of two ultrasound modalities, i.e., B-mode and tissue Doppler, renders the method more accurate and robust. Visualization—We display diagnostically meaningful motion data inside a user-defined region of interest that is tracked in time. Myocardial inward and outward motion is visualized by color coding the radial motion component with respect to the ventricular center. Two-dimensional strain rate information is superimposed in the form of deforming ellipses. The display allows a more intuitive and simplified identification of regions with abnormal motion patterns. Validation—The proposed method is validated on 1) synthetic data, 2) real ultrasound phantom data, and 3) clinical echocardiograms. A large-scale validation study that includes 114 patients confirms its ability to detect and quantify wall motion abnormalities.

EPFL2004

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EPFL2004