Method for characterizing a tubular object such as a blood vessel by ultrasound imaging

Abstract

A method for characterizing a tubular object by ultrasound imaging, the method comprising the steps of: Acquiring (201) several ultrasound signals originating from the reflection of an ultrasound field emitted by the transducer on an area of ​​interest in a cross-sectional plane of the object, for different positions of the transducer relative to said area, all of the ultrasound signals forming an ultrasound image of the area, for each signal corresponding to a vector of the image, applying (202) a first predetermined filter to the signal, Selecting (203), from all of the signals, the signal for which the result of the filter has the extremum of the highest absolute value and recording the abscissa of this extremum, Determining (204) the center of the object from the recorded abscissa and the speed of the ultrasound signal. Figure 2

Description

Title of the invention: Method for characterizing a tubular-shaped object such as a blood vessel by ultrasound imaging,

[0001] The invention relates to the field of ultrasound imaging, in particular medical imaging and more specifically relates to a method for determining the center and diameter of a tubular object such as a blood vessel.

[0002] Ultrasound imaging, particularly in the medical field, aims to image different types of objects or organs in the human body to better characterize them.

[0003] In the field of medical imaging, there is a need to characterize blood vessels, particularly arteries. Indeed, arteries are a true health indicator, as they provide essential information on the state of health of the human body. Real-time monitoring of arteries allows the tracking of biological markers such as blood pressure to better understand the functioning of the human body and anticipate the development of diseases. The development of ultrasound-based technologies makes it possible to design compact imaging systems that offer a non-invasive solution. Real-time monitoring requires that the entire measurement chain, from acquisition, location of the artery, to the extraction of relevant information, needs to be fully automated.

[0004] In this general field, there is a particular need to characterize arteries via the detection of their center and the measurement of their diameter.

[0005] Ultrasound imaging methods that provide solutions for characterizing blood vessels are most often focused on the longitudinal dimension of the vessel, for example the carotid artery.

[0006] These methods are diverse but can be grouped into three non-exhaustive categories which include manual, semi-automatic and automatic methods.

[0007] Manual methods rely on human expertise to manually determine the position of arteries, for example using graphical tools applied to ultrasound images.

[0008] In the case of semi-automatic methods, assistance algorithms work in collaboration with a human. For example, the algorithm may continue to automatically locate the artery based on the human as a guide, who validates, corrects, and indicates reference information helping the algorithm find the correct location.

[0009] In the category of fully automatic methods, the algorithm determines at automatic artery localization. Most of these automatic methods specifically address the problem of carotid artery localization and do not work optimally with other arteries. Indeed, the carotid artery is a less restrictive case than other arteries such as the radial artery for several reasons. The carotid artery is larger with thicker walls and has a different intensity profile than the radial artery, with less noise and more contrast. Since the diameter of the carotid artery is larger than that of the radial artery, ultrasound images have better contrast. It is therefore easier to locate the center of the carotid artery simply by searching for the area with the lowest intensity profile. Compared to the radial artery, ultrasound images of the carotid artery naturally have fewer anatomical objects that can be confused with the artery.The larger size of the carotid artery also contributes, as it allows for limiting the fields of ultrasound images, which allows for images that contain the fewest noisy objects. Most methods developed for the carotid artery focus on the longitudinal dimension, which further facilitates localization by simply identifying the horizontal trace with the lowest intensity profile. Other anatomical objects have a less noisy footprint on this dimension. Most algorithms localize the area with the lowest intensity vertically, scanning the entire image; subsequently, the artery is localized according to a consensus of the identified points after removing outliers. These methods based on the longitudinal dimension are sensitive to the orientation of the probe and all require precise positioning of the probe on the skin during acquisition.Orientation uncertainty will make the measurement of diameter and other properties inaccurate. It is possible to circumvent this problem by using multidimensional probes but these are more expensive.

[0010] Although the transverse dimension is more difficult to process, because the shape of the anatomical objects on this dimension appears frontally and disrupts the localization, particularly in the case of the radial artery, the advantage of the transverse dimension is that it does not require precise positioning of the probe on the skin, and the diameter measurements remain robust and reliable with respect to errors in orientation of the probe.

[0011] Existing solutions for automatic localization on the transverse dimension of arteries or more generally of blood vessels are rare and all suffer from one or more limitations. Methods based on deep learning are effective but consume a lot of energy, are not suitable for small portable systems, they are complex to implement and require a lot of resources. It is difficult to achieve real-time performance especially when it comes to a portable calculator. These methods are found integrated into assistance interfaces for which the requirements are not limited in time and resources. Similarly, neural networks characterized by their black box aspect, suffer from the problem of unpredictability. It has been shown that a single pixel of an image can lead a neural network to provide aberrant data. This problem is also the main cause slowing down the adoption of artificial intelligence in the medical field, and areas with serious consequences.

[0012] Doppler-based solutions allow localization through the change in frequency related to blood flow. These methods are limited to vertical localization, where the depth of the artery is located. Horizontal localization is performed manually. Another disadvantage of these methods is that Doppler localization requires monitoring of ultrasound frequencies over time, this implies that all these methods need the time dimension in order to locate the artery.

[0013] Other limitations are manifested in several aspects such as, for example, the speed of execution aspect and the satisfaction of real-time standards. Iterative search algorithms, for example, are slow and require considerable resources to achieve real-time operation.

[0014] Algorithms requiring a reference model and prior information are disabling. Another aspect is related to algorithms that are sensitive to noise and require preprocessing to condition the received data before executing the localization method. Some existing solutions rely on landmarks such as contours, which makes them vulnerable to data imperfections. These methods often use preprocessing, consensus, and outlier removal algorithms to ensure robustness, which adds complexity and computation.

[0015] Other methods are applied to other technological solutions that go beyond the scope of ultrasound, such as the so-called intravascular ultrasound or IVUS methods and the so-called optical coherence tomography or OCT methods. Although these methods process images of arteries, they do not address the same problem, because the profile of the processed data is different, in terms of intensity, noise and difficulties. Also, these methods inherit the disadvantages of these technologies, such as invasiveness in the case of IVUS methods and depth limitation in the case of non-invasive OCT methods.

[0016] Patent application WO2016057233 presents a method for estimating a non-invasive continuous blood pressure waveform using ultrasound and an automated cuff. It belongs to the category of non-invasive continuous blood pressure measurements. The proposed system measures physical characteristics such as the geometry, elasticity, and deformation of a blood vessel, as well as other external physical parameters. Computer modeling and processing of the measured signals are used during cuff inflation and / or deflation to iteratively estimate the subject's blood pressure. The ultrasound portion allows the diameter of the artery to be estimated and its elasticity to be calculated.

[0017] The localization of the artery in this solution is limited by several points: first, the transducer arrays are fixed by a qualified operator at an approximate location and the artery is located in three dimensions by an iterative search method of the assumed geometric characteristics and ultrasonic response. The search is not only iterative, which is computationally intensive, but it is done in a 3D search space, therefore on a large amount of data. This limits the speed of execution, particularly for devices limited in resources and energy consumption. In addition, this search in the 3D space is carried out by comparing at each iteration, with a reference artery model constructed through prior personal information about the subject, such as his age, his height, etc.Besides the fact that this solution requires a reference model and prior information, it also requires a two-dimensional probe to allow acquisition in the 3D space on which the iterative search algorithm is executed. With slow localization, refreshing the artery position imposes a compromise for speed, accuracy and the movement of the person.

[0018] Document [1] presents a method for characterizing the carotid artery that can be applied to the longitudinal and transverse dimension of this artery. It consists of a contour detection algorithm based on the "Snake" method and equipped with a mathematical artery reference model in the form of B-splines that deforms in scale, translation and rotation. The method converges to the carotid artery contours using an optimization algorithm. The convergence criterion is ensured by a sum of the absolute differences between the model and the image at each point of the image. The model parameters are optimized iteratively using the energy minimization algorithm, then the sum of the absolute differences is calculated at each point. The circular shape of the reference model is used to simplify the calculation.A gradient-based optimization algorithm is applied to refine an accurate segmentation of the detected artery contours.

[0019] In this document, the shape of the carotid artery is quite large and circular, and the thickness of its wall is quite large, which makes it easy to distinguish the contours of the layers, on which the algorithm is based. This solution is not applicable to other types of arteries such as the radial artery for several reasons. First of all, the radial artery often appears with a shape that is neither perfectly circular nor perfectly rectangular, because its size is small. The radial artery wall is small, and does not allow its layers to be distinguished with standard probes at low central frequency, this requires high quality probes at higher ultrasound frequencies. For this reason and since the method in reference [1] relies on the differences in local contrast between the inner and outer contours, it will be difficult for the algorithm to accurately locate the region of interest in cases where the artery or surrounding tissue lacks texture or contrast, which is the case with the radial artery.

[0020] Also, the anatomical objects on the transverse images of the carotid artery, such as the jugular vein, have shapes that are not very circular and easily discriminable compared to the carotid artery. For this reason, the algorithm is not likely to converge towards other objects. In the case of the radial artery, several anatomical objects have a shape and positioning very similar to the radial artery, which means that the method is likely to converge towards the other objects instead of the radial artery, because the criterion of the sum of the absolute differences will also be satisfied, for example, by the veins neighboring the artery.

[0021] The method requires image enhancement preprocessing based on adaptive histograms to reduce noise. Because noise may significantly increase the time required for convergence of the optimization algorithms on which the method is based. The convergence time is variable and depends on the intensity profiles of the acquired image, its complexity, the position of the artery and its similarity to the model. It also depends on the initialization parameters integrated into the model. If the image contains complex anatomical structures or if the artery itself has irregular shapes, the comparison approach with the reference model may not work properly within the time limits.

[0022] Also, the model may not be flexible enough to handle these variations, resulting in poor alignment. For this reason, this method is more suitable for applications that do not require strict real-time constraints, and regular time-of-flight measurement at fixed intervals. The method compares the image pixels with a model at each point of the image while calculating each time the absolute sum of differences criterion on the pixels. This operation is computationally intensive, which requires the method to use optimization algorithms to reduce the computation time. The disadvantage of this technique is that it makes the solution entirely dependent on the convergence time of the optimization algorithm, which is an iterative algorithm dependent on the complexity of the image and vulnerable to convergence problems such as local minima.Additionally, optimization may converge on veins rather than artery on radial artery cross-sectional scans.

[0023] The method presented in document [2] concerns the localization of the carotid artery in transverse images. The method is based on the circular Hough transform, a method that allows detecting lines in the image and has been adapted for detecting circles. The method processes sequences of 5 to 15 consecutive images. Each image is preprocessed to optimize brightness and contrast. Then, a strong Gaussian filter reduces noise. Although several fine morphological structures have been destroyed in this process, the carotid artery appears brighter. Then, the Hough transform detects all dark circles by considering all radii that are within a reasonable range. The coordinates of the detected centers (xi, yi) and radii (ri) are collected. For each of the detected dark circles, the brightness values ​​of their pixels are evaluated.The circle with the darkest values, i.e., the minimum brightness, is elected as the "candidate circle", and its center coordinates (xci, yci) with a radius (rci) are recorded in a matrix of potential candidates. Once all B-mode images have been processed, the matrix contains the results obtained from each image. The most frequent triplet (xc, yc, rc) appearing in the matrix is ​​selected as the final choice.

[0024] This third method is also not suitable for the radial artery for the following reasons. The carotid artery has a fairly circular shape, it stands out as a fairly dark area and is easy to discriminate from other anatomical objects. The shape of the radial artery on ultrasound images does not appear perfectly circular. In addition, there are several anatomical objects similar to the artery, for example veins, which are nearby, sometimes darker than the radial artery and also with a circular shape. The Hough transform can be adapted to other more complex shapes to apply it to the radial artery, moreover, this considerably increases the intensity of the calculations, because the circular shape as presented in document [2] already performs a search in a 3D space which requires a much more intense calculation than with the standard Hough transform.Indeed, for each circle diameter, the method performs the calculation of a new accumulator on the whole image. The radial artery can be easily deformed, for example towards an ellipse shape which implies an additional parameter to be taken into account by the Hough transform. The shape of the radial artery is much more deformed than that of the carotid artery, because the size is smaller which makes the contours smaller and therefore more deformed by the noise observed on the ultrasound images. The Hough transform provides multiple detections and the choice according to the darkest area will not work in the case of the radial artery, because the neighboring veins can be darker. These multiple detections cause a large number of false detections, which requires either the use of more criteria. complex, or to consider the temporal dimension in order to improve the robustness of localization. For this reason, the method requires 5 successive images. The circular Hough transform requires an edge detection step and thresholding adjustments. Edge detection is vulnerable to noise in the image, which implies the need for preprocessing for enhancement and noise filtering. For this reason, the method in the document specifies that strong Gaussian filtering is necessary.

[0025] All of the aforementioned methods have the disadvantages described. There is then a need for a new method which is applicable to the characterization of the cross-section of an artery, in particular the radial artery, which does not require a two-dimensional probe and which is adapted to the acquisition environment of the artery to be characterized, in particular the noise and the presence of other biological objects.

[0026] The invention allows automatic localization and characterization of a radial artery in its cross-section. It involves a first phase of localization of the center of the artery from several ultrasound acquisitions forming a 2D image of the cross-section comprising the section of the artery, then a second phase of determination of the diameter of the artery from the ultrasound measurement corresponding to the path passing through the center of the artery.

[0027] The invention does not require precise positioning of the probe on the skin by an expert, and tolerates angular positioning uncertainties. This allows for reliable measurements even in the presence of movement of the subject and the probe relative to the subject. The diameter measurement is made at the most relevant horizontal point in the center of the artery, which is the point that provides the most accurate diameter measurement.

[0028] The principle of the invention is based on a convolution filter approach and comprises a basic filter specially dedicated for the detection of an artery or more generally of a tubular object. Once the center of the artery is located, methods for extracting the properties of the artery are applied locally on the result of the filter.

[0029] The proposed method has low complexity and can be implemented easily, since almost all the localization operations are performed as simple operations, which constitutes a considerable simplicity advantage and allows the method to be embedded on portable systems with limited resources and to perform time-of-flight measurements. The proposed method does not require any comparison with a reference model, nor calculation of a similarity criterion at each point, nor any intensive iterative calculation, nor risk of iterative convergence and local problems from which optimization algorithms suffer. Another advantage of the invention is that it can be executed in synchronization with the acquisition in an incremental manner as the signals are received, without needing to wait for the end of acquisition. This allows for rapid refreshment of the artery location. Unlike existing methods, the proposed method does not need to store a large number of parameters, as it gradually updates the summation and the coordinates of the extremum point as the calculation progresses. Some existing solutions rely on landmarks such as contours, which makes them vulnerable. Some of these solutions use noise enhancement or outlier removal methods, which increases complexity and computation. The proposed solution is based on global information, which makes it robust against noise and does not require image enhancement preprocessing or outlier removal algorithms.

[0030] The proposed solution performs the localization directly on the spatial dimension of a single transverse acquisition and does not need multiple acquisitions on the temporal dimension unlike Doppler-based methods. The proposed method does not need prior information on the person, nor a reference artery model. With rapid localization, the refreshment of the artery position is done at high speed and offers the possibility of having both a higher measurement frequency, more precision and more freedom of movement of the person.

[0031] Although the invention is described in the particular context of the characterization of a blood vessel for medical imaging applications, it can also be applied in the field of non-destructive testing by ultrasound to characterize any type of tubular-shaped object.

[0032] The subject of the invention is a method for characterizing a tubular-shaped object by ultrasound imaging, the method comprising the steps of: - Acquiring, by means of an ultrasound transducer, several ultrasound signals originating from the reflection of an ultrasound field emitted by the transducer on an area of ​​interest in a cross-sectional plane of the object, for different positions of the transducer relative to said area, all of the ultrasound signals forming an ultrasound image of the area, - Choose a dimension of the ultrasound image and for each signal corresponding to a vector of the image according to the chosen dimension, apply a first predetermined filter to the signal, the filter being configured so as to transform a first signal comprising two extrema of the same sign into a second signal comprising an extremum of opposite sign located between the two extrema of the first signal, - Select, from all the signals, the signal for which the filter result has the highest absolute value extremum and note the abscissa of this extremum, - Determine the center of the object from the abscissa taken and the speed of the ultrasonic signal.

[0033] According to a particular aspect of the invention, the filter is applied to the envelope of the ultrasonic signal or to the absolute value of the ultrasonic signal.

[0034] According to a particular aspect of the invention, the filter is applied to the signal over a sliding window of predefined size as a function of the size of the signal and / or a priori information on the dimension of the object, the filter being defined over at least three consecutive time intervals by three respective functions each weighted by a coefficient, the coefficients associated with two consecutive time intervals being of opposite signs.

[0035] According to a particular aspect of the invention, the dimension of the second time interval is chosen so as to be strictly less than the minimum diameter of the object to be characterized.

[0036] According to a particular aspect of the invention, the filter is defined over at least two additional time intervals.

[0037] According to a particular aspect of the invention, each of the functions is taken from: a sum, a maximum value, an average or a combination of these functions.

[0038] In an alternative embodiment, the method according to the invention further comprises the steps of: - Select the acquired ultrasonic signal for which the center of the object was determined, - Apply a predetermined threshold to said selected ultrasonic signal, - Detect at least two extrema of said signal above the threshold, - Select the pair of extrema, comprising a first extremum and a second extremum, closest to the center of the object and located on either side of the center of the object, note their respective time abscissas and deduce the internal diameter of the object from the difference between the two abscissas and the speed of the ultrasonic signal.

[0039] In an alternative embodiment, the method according to the invention comprises the steps of: - Select a third extremum greater than the threshold and located immediately before the first extremum, - Select a fourth extremum greater than the threshold and located immediately after the second extremum, - Record the time abscissas of the third and fourth extremums and deduce the external diameter of the object from the difference between the two abscissas and the speed of the ultrasonic signal.

[0040] According to a particular aspect of the invention, the object is a blood vessel, for example an artery.

[0041] According to a particular aspect of the invention, the step of acquiring several ultrasonic signals comprises the sub-steps of: - Position a transducer comprising several aligned elements, on an area of ​​the skin so as to image a cross section of the blood vessel, - Carry out several successive ultrasonic acquisitions from different emission points located on the alignment axis of the elements, each ultrasonic emission being carried out in a direction substantially perpendicular to the alignment axis.

[0042] The invention also relates to an ultrasound imaging device comprising an ultrasound transducer and a processing unit configured to carry out the steps of the method according to the invention.

[0043] Other characteristics and advantages of the present invention will appear more clearly on reading the description which follows in relation to the following appended drawings.

[0044] [Fig.l] represents a diagram of an ultrasonic multi-element probe capable of carrying out an ultrasonic signal acquisition sequence to image a blood vessel,

[0045] [Fig.2] represents a flowchart describing the steps of implementing a method for detecting the center of a blood vessel according to one embodiment of the invention,

[0046] [Fig.3a] represents an ultrasound image of an area of ​​a transverse plane including a radial artery,

[0047] [Fig.3b] represents the image of [Fig.3a] filtered by means of a first filter defined according to the invention,

[0048] [Fig.3c] represents an image of [Fig.3b] filtered by means of a second filter defined according to the invention, so as to allow the detection of the radial artery,

[0049] [Fig.4a] represents a time diagram of an ultrasonic signal acquired by means of of the probe of [Fig.l],

[0050] [Fig.4b] represents an example of a filter intended to be applied to the signal of the [Fig.4a],

[0051] [Fig.4c] represents the result of applying the filter of [Fig.4b] to the signal of the [Fig.4a]

[0052] [Fig.4d] represents the result of an additional filtering step applied to the signal of [Fig.4c],

[0053] [Fig.5] represents a flowchart describing the steps of implementing a method for determining the diameter of a blood vessel according to one embodiment of the invention,

[0054] [Fig.6] represents an example of an ultrasonic signal used to determine the diameter of a blood vessel according to the method of [Fig.5],

[0055] [Fig.2] represents, on a flowchart, the main stages of implementation of a method for determining the center of a blood vessel, for example a radial artery, according to one embodiment of the invention.

[0056] The method begins at step 201 with an acquisition of ultrasound signals in a transverse plane of the artery or vessel.

[0057] This acquisition is carried out by means of a probe shown in [Fig. 1].

[0058] The acquisition probe is advantageously a linear multi-element probe 101, i.e. one which comprises several ultrasonic elements 102, for example piezoelectric elements, aligned in a row. Alternatively, a two-dimensional probe, i.e. one comprising a matrix of ultrasonic elements, can also be used.

[0059] In the example of [Fig.l], the probe 101 is positioned in contact with the skin 103 so that the row of elements 102 is located substantially in a transverse plane of an artery 104 to be imaged. The probe 101 is not necessarily centered on the artery 104; it is sufficient for the artery to be covered in transmission and reception by the ultrasound beam generated by the probe so as to obtain a transverse imprint of the artery.

[0060] The acquisition 101 is carried out by means of a successive scan during which, at each step, an ultrasonic beam is emitted by a group of elements 102 comprising at least one element, in a direction perpendicular to the axis of the alignment of the elements. The same elements are active in reception to generate an ultrasonic signal corresponding to a predefined acquisition duration and to an axis substantially perpendicular to the group of elements 102.

[0061] This step makes it possible to acquire an ultrasound signal. It is then iterated by shifting, according to a sliding window, the active ultrasound elements, by one element and then by carrying out a new acquisition. Thus, by carrying out several successive acquisitions by means of a group of elements of fixed size which scans all of the elements of the probe, several ultrasound signals are obtained which together form an ultrasound image of a transverse plane of the artery 104.

[0062] This acquisition makes it possible to obtain in two dimensions a transverse ultrasound imprint of the targeted radial artery as well as any other anatomical objects present around it such as veins.

[0063] Without departing from the scope of the invention, other ultrasonic acquisition methods may be envisaged insofar as they allow a 2D image of said transverse plane to be obtained. On the image obtained, which corresponds to a matrix of signal samples, the vertical position represents the depth, i.e. the distance between the probe and an anatomical element located under the probe. The horizontal position rhizotantal represents a point on the skin 103 corresponding to the emission point of the ultrasound beam.

[0064] [Fig.3a] represents an example of an ultrasound image obtained with identification of the radial artery 104. It can be seen that in this image, the imprint of the artery 104 is difficult to detect because it is drowned in noise and polluted by the imprints of other anatomical objects.

[0065] An objective of the invention is to identify the point on the skin located directly above the center of the artery in order to determine the ultrasound measurement which corresponds to a path passing through this center.

[0066] [Fig.4a] represents an example of an ultrasound signal acquired during a simple acquisition. The signal of [Fig.4a] corresponds to a column of the matrix of [Fig.3a]. Indeed, each column of this matrix corresponds to an acquisition from a point on the surface of the skin. More precisely, the signal of [Fig.4a] corresponds to the envelope or the absolute value of the acquired ultrasound signal.

[0067] In step 202, one or more filtering steps specifically adapted to identify the center of the artery are then applied for each of the acquired signals (each column of the 2D matrix).

[0068] As can be seen in [Fig.4a], an acquisition in the transverse plane of the artery is characterized by two peaks or groups of amplitude peaks which correspond to the echoes on the walls of the artery. Between these two peaks is the interior of the artery.

[0069] A first filter is applied to the signal of [Fig.4a]. This filter is defined so as to transform the signal of [Fig.4a] which comprises two extrema of the same sign into another signal which comprises an extremum of the opposite sign located substantially halfway between the two extrema of the first signal.

[0070] An example of a filter is shown in [Fig.4b]. This filter corresponds to a time slot. It has a total size greater than the diameter of the artery that one wishes to detect and is composed of three successive parts. The central part 401 is set to a positive value, for example equal to 1, over a duration corresponding to a distance less than the internal diameter of the artery.

[0071] The other two parts 402, 403 are set to a negative value, for example equal to -1.

[0072] More generally it is possible to replace the value 1 with another positive value and the value -1 with another negative value.

[0073] By applying a convolution of this filter with the acquired signal, the outer parts of the filter 402, 403 contribute negatively to the convolution results, while the central area 401 contributes positively to the convolution results.

[0074] When this filter is applied to the signal acquired at the level of the corresponding zone to the artery, the amplitude peaks corresponding to the walls of the artery will be aligned with the outer zones of the filter 402, 403, their contribution will therefore be summed negatively. Conversely, when the central part of the filter does not coincide with the central zone between the two amplitude peaks but coincides with one of the amplitude peaks, then the result of the convolution with the filter will be a positive value.

[0075] [Fig.4c] shows the result of applying the filter of [Fig.4b] to the signal of [Fig.4a]. It can be seen that the resulting filtered signal has positive amplitudes everywhere except in the area corresponding to the inside of the artery.

[0076] [Fig.4d] shows the final result obtained by setting all positive values ​​to zero. The extremum of the signal in [Fig.4d] corresponds to a point in the inner zone of the artery for which there are only negative contributions, i.e. a situation where the central zone 401 of the filter is applied between the two amplitude peaks corresponding to the walls of the artery.

[0077] Thus, by analyzing the filtered signals of the type in [Fig.4d] for all the acquisitions, it is possible to locate the center of the artery.

[0078] The filter described in [Fig.4b] is a non-limiting example and can be adapted to the situation that one wishes to analyze. In particular, the dimensions of the three parts 401, 402, 403 of the filter can be adapted according to the maximum dimensions of the blood vessels that one wishes to detect.

[0079] In particular, the dimension of the central part 402 can be chosen to be strictly less than the minimum diameter of a vessel that one wishes to characterize. Alternatively, this dimension can be chosen to be of the order of the average diameter of the vessels to be characterized. The choice of dimensioning also depends on the type of basic function applied by the filter (maximum, minimum, average or sum in particular).

[0080] Other filter variants may be envisaged as now described.

[0081] If we denote 5 the envelope or the absolute value of the acquired ultrasonic signal, this signal being composed of Nx samples:

[0082] 5 — {i(x) |1 < x < , where i(x) is the ultrasonic intensity (amplitude) at point x of the S signal.

[0083] We note ROIr ={i(^), i(a,.+ 1), i(«r+ 2)..., i( / ;r), < x < br; (ar, br)& S}, where, ROIr is a region of interest r represented by the set of points of the 5' signal located in the interval [a, , £7]. The filter is defined by applying functions to several regions of interest. In the example of [Fig.4b] the number of regions of interest is equal to three but it can be greater than three as will be described later.

[0084] The general form of the filter applied to the signal is given by:

[0085] p _ 1^ra )' °where f is a function of strictly positive values ​​applied to the signal, such as, for example, the maximum, the sum or the average over

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[0099] the set of points belonging to the ROIr region. ar is a predefined weighting coefficient. In the example of Figure 4b, there are three areas of interest, so Nr =3 and the functions f are all the sum function with coefficients equal to -1 for the outer areas of interest and +1 for the central area of ​​interest of the filter. More generally, the coefficients can have an absolute value other than 1, the filter F is then written: F- aj\ROI S+aJ^ROI \-aJ3(ROI ) ■- Maximum wall! « Distal wall / ' v Valley' The ROLaiiée area of ​​interest corresponds to the central area of ​​the filter and the ROIparoiftoximaie and ROIparoiDistaie areas of interest correspond to the outer areas of the filter. For the example in [Fig.4b], the filter can be expressed as: F - vertex ROL, sumAROI - vertexROI„, Suitcase! \ Near Wall) Distal Wall} Other forms of filter can be considered, either calculated directly or calculated locally on the points detected by a first filtering step. In particular, filters composed of five areas of interest can be considered in order to take into account the external and internal walls of a vessel. Indeed, the external wall and the internal wall are separated by a non-echogenic middle layer. The acquired signal can therefore present, for each of the proximal and distal walls, two amplitude peaks separated by a zone close to zero, the two peaks corresponding to the external and internal walls. + a,f\ROI . .}-0^(ROI ) k Proximal wall) K Proximal Medic Layer' F = +aoJ2(ROI}-a5f(ROI ') v DistalWall' ' MedianDistalLayer / -a^ROI} ' Suitcase A particular example of a filter comprising five areas of interest is: + Max(R ■„ . , ) -moytJF, - - pa king Proximal / •• x LayerMedi anP roxunale) + MaxtRn J -avgOï , ,, , ParmDistale! ■ v LayerMedianDtstaue -moy(Ru ... ) • v Vaille / Max is the maximum value of the signal over the area of ​​interest and avg() is the average of the signal over the area of ​​interest. Another example implementation is to normalize the initial filter by the mean of the central region of interest or valley. F' =----£---T moVROI ) In another embodiment, in the presence of anatomical objects if miliaries, it is possible to increase the filter to consider extrinsic properties of the targeted object, for example if the radial artery has a vein on each side, the following form will distinguish it:

[0100] = F- a ^(ROI ) -a7f(ROI . ) 'leftvein' 'rightvein'

[0101] In this embodiment, the filter is composed of seven zones.

[0102] Generally, the filter is defined over at least three consecutive time intervals by three respective functions each weighted by a coefficient, the coefficients associated with the first time interval and the third time interval being of the same sign and the coefficient associated with the second time interval being of opposite sign.

[0103] When the filter is defined over five time intervals, it is then defined over at least a first additional time interval located before the first time interval and a second additional time interval located after the third time interval, the coefficients associated with the two additional time intervals being of the same sign as the coefficient associated with the second time interval.

[0104] In other words, the filter coefficients corresponding to two consecutive intervals are of opposite signs.

[0105] Generally speaking, alternating the signs of the filter coefficients over two consecutive intervals makes it possible to obtain a filtered signal which has an extrema when the filter coincides temporally with the center of the interval delimited by two amplitude peaks of the initial signal, these peaks corresponding to the echoes of the signal on the walls of the blood vessel.

[0106] The implementation of the filter can be carried out in cascade, in an iterative manner, for example by applying the filter to the entire signal during a first iteration then by applying another filter to the result of the first filtering, centered on a reduced time zone, during the following iterations.

[0107] The filtering step 202 is applied to all the acquired ultrasonic signals corresponding to all the column vectors of the ultrasonic image of [Fig.3a].

[0108] Alternatively or in addition, it is also possible to apply the same processing to each line vector of the ultrasound image in order to identify the center of the artery according to the dimension parallel to the axis of the linear probe.

[0109] [Fig.3b] represents the result of applying the filter of [Fig.4b] for all the signals (equivalent to [Fig.4c] for one signal).

[0110] [Fig.3c] represents the final result after setting the positive values ​​to zero. Artery 104 can be precisely identified in the image of [Fig.3c].

[0111] The result of each filtering can be stored in memory and refreshed incrementally. tably keeping the optimum each time. It is also possible to partially store the optimal result of each signal, or to fully store all the filtering results, depending on the available resources then, at step 203 of the method, we then search, among all the filtered signals, for the one which includes the highest amplitude extremum (in absolute value). The corresponding signal selected will be the one which corresponds to the path which passes through the center of the artery. Indeed, this signal must have the largest valley zone (between the two walls of the artery) and therefore generate the largest negative contribution among all the signals. This is also reinforced by the wall which often has a maximum amplitude in the center.

[0112] In step 204, the center of the blood vessel is finally determined by recording the abscissa of the extremum measured on the selected signal.

[0113] This time abscissa t is then converted into distance d from the speed v of the ultrasonic signal in the medium: £) — 1 y.

[0114] A second embodiment of the invention is now described, which consists of subsequently determining the diameter of the blood vessel whose center has been detected.

[0115] This second embodiment is described in [Fig.5].

[0116] It begins at step 501 by determining the center of the vessel using the method described previously in support of [Fig.2].

[0117] In step 502, the signal previously selected in step 203 is selected, which corresponds to the ultrasound path which passes through the center of the vessel.

[0118] [Fig.6] shows an example of such a signal on which several peaks have been identified. of extremum.

[0119] In step 503, a threshold is applied to the signal in order to retain only the points corresponding to the extrema zones.

[0120] In step 504, the two points corresponding to the two extrema closest to the center of the vessel determined in step 501 are detected.

[0121] In step 505, the internal diameter of the vessel is deduced. Indeed, the two detected extrema correspond to the internal walls of the vessel. The internal diameter of the vessel is then equal to — ^y where t2, ti are the abscissas of the two extrema identified in [Fig.6].

[0122] In an optional step 506, the external diameter of the vessel is further determined using the same relationship D — - (t^- °where L, t4 are the abscissas of two other extrema exceeding the threshold of step 503 and located immediately after a low intensity zone which has low echogenicity and which is intermediate between the internal wall and the external wall of each of the proximal and distal sides. In other words, the extrema detected in step 506 are the extrema located immediately immediately on either side of the first extrema detected at step 505 as shown in [Fig.6].

[0123] The different abscissas of the four extrema considered are identified in [Fig.6].

[0124] As explained previously, the method of [Fig.5] can also be applied to the horizontal dimension of the ultrasound image, in order to determine the diameter of the vessel according to this dimension.

[0125] Although the invention has been described preferentially for medical applications consisting of characterizing a blood vessel and more precisely a radial artery, it can be applied more generally for any application involving ultrasound images of tubular-shaped objects. References

[0126] [1] JH Gagan et al., “Automated Segmentation of Common Carotid Artery in Ul- trasound Images," in IEEE Access, vol. 10, pp. 58419-58430, 2022

[0127] [2] “System A feasibility study of a PMUT-based wearable sensor for the automatic monitoring of carotid artery parameters”, 2021 IEEE International Ultrasonics Symposium (IUS)

Claims

Claims

1. Method for characterizing a tubular-shaped object by ultrasound imaging, the method comprising the steps of: - Acquiring (201), by means of an ultrasound transducer, several ultrasound signals originating from the reflection of an ultrasound field emitted by the transducer on an area of ​​interest in a cross-sectional plane of the object, for different positions of the transducer relative to said area, all of the ultrasound signals forming an ultrasound image of the area, - Choosing a dimension of the ultrasound image and for each signal corresponding to a vector of the image according to the chosen dimension, applying (202) a first predetermined filter to the signal, the filter being configured so as to transform a first signal comprising two extrema of the same sign into a second signal comprising an extremum of opposite sign located between the two extrema of the first signal, - Selecting (203), from all of the signals,the signal for which the filter result has the highest absolute value extremum and note the abscissa of this extremum, - Determine (204) the center of the object from the abscissa noted and the speed of the ultrasonic signal.,

2. A method of characterizing a tubular-shaped object according to claim 1 wherein the filter is applied to the envelope of the ultrasonic signal or to the absolute value of the ultrasonic signal.

3. Method for characterizing a tubular-shaped object according to claim 2 in which the filter is applied to the signal over a sliding window of predefined size as a function of the size of the signal and / or a priori information on the dimension of the object, the filter being defined over at least three consecutive time intervals by three respective functions each weighted by a coefficient, the coefficients associated with two consecutive time intervals being of opposite signs.

4. Method for characterizing a tubular-shaped object according to claim 3, in which the dimension of the second time interval is chosen so as to be strictly less than the minimum diameter of the object to be characterized.

5. Method of characterizing a tubular-shaped object according to any one of claims 3 or 4 in which the filter is defined over at least two additional time intervals.

6. Method for characterizing a tubular-shaped object according to any one of claims 3 to 5 in which each of the functions is taken from: a sum, a maximum value, an average or a combination of these functions.

7. A method for characterizing a tubular-shaped object according to any one of the preceding claims, further comprising the steps of: - Selecting (502) the acquired ultrasonic signal for which the center of the object has been determined, - Applying (503) a predetermined threshold to said selected ultrasonic signal, - Detecting (504) at least two extrema of said signal greater than the threshold, - Selecting the pair of extrema, comprising a first extremum and a second extremum, closest to the center of the object and located on either side of the center of the object, recording their respective time abscissas and deducing (505) the internal diameter of the object from the difference between the two abscissas and the speed of the ultrasonic signal.

8. Method for characterizing a tubular-shaped object according to claim 7 comprising the steps of: - Selecting a third extremum greater than the threshold and located immediately before the first extremum, - Selecting a fourth extremum greater than the threshold and located immediately after the second extremum, - Recording the time abscissas of the third extremum and the fourth extremum and deducing (506) the external diameter of the object from the difference between the two abscissas and the speed of the ultrasonic signal.

9. A method of characterizing a tubular-shaped object according to any preceding claim wherein the object is a blood vessel, for example an artery.

10. Method for characterizing a blood vessel according to claim 9 in which the step of acquiring (201) several ultrasound signals comprises the sub-steps of: - Positioning a transducer comprising several aligned elements, on an area of ​​the skin so as to image a cross-section of the blood vessel, - Carrying out several successive ultrasound acquisitions from different emission points located on the alignment axis of the elements, each ultrasound emission being carried out in a direction substantially perpendicular to the alignment axis.

11. An ultrasound imaging device comprising an ultrasound transducer (101) and a processing unit configured to perform the steps of the method according to any one of the preceding claims.

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