Method for characterizing a symmetrical object, particularly a blood vessel, using ultrasound imaging

Abstract

Method for characterizing an object by ultrasonic imaging, the method comprising the steps of: Acquiring (201), by means of an ultrasonic transducer, an ultrasonic signal resulting from the reflection of an ultrasonic field emitted by the transducer on a region of interest including the object; Selecting (202) a first predetermined portion of the signal comprising at least two local extrema; Determining (203) a descriptor of the first portion; Searching (203, 204), in the ultrasonic signal, for a second portion of the ultrasonic signal whose descriptor, determined for this second portion inverted temporally, is closest to the descriptor of the first portion; Deducing that the first and second portions of the signal correspond to two symmetrical parts of the object to be characterized. Figure 2

Description

Title of the invention: Method for characterizing a symmetrical object, in particular a blood vessel, by ultrasound imaging

[0001] The invention relates to the field of ultrasound imaging, in particular medical imaging and more specifically to a method for detecting points of interest in anatomical objects exhibiting symmetry in an imaging plane such as a blood vessel.

[0002] In the field of medical imaging, there is a need to accurately characterize anatomical structures, particularly blood vessels and especially arteries. Indeed, arteries are a true indicator of health, as they provide essential information about the state of the human body. Real-time arterial monitoring allows for 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 and artery localization to the extraction of relevant information, be fully automated.

[0003] In particular, it is useful to be able to precisely locate the center of an artery and to measure its diameter.

[0004] However, ultrasound signal measurements can be affected by noise and subject to interference. In such cases, the imprint of the arterial walls in the signal may be subject to distortions such as attenuation or amplification. In particular, the signatures of the two symmetrical walls may no longer necessarily appear exactly symmetrical in the measured ultrasound signal.

[0005] There is therefore a need to precisely locate certain points of interest of a symmetrical anatomical object, from an ultrasonic signal.

[0006] Ultrasound imaging methods that offer solutions for characterizing blood vessels are most often focused on the longitudinal dimension of the vessel, for example the carotid artery.

[0007] These methods are diverse but can be grouped into three non-exhaustive categories which include manual, semi-automatic and automatic methods.

[0008] Manual methods rely on human expertise to manually determine the position of arteries, for example using graphic tools applied to ultrasound images.

[0009] In the case of semi-automatic methods, assistance algorithms operate in collaboration with a human. For example, the algorithm can continue to automatically locate the artery, relying on the human as a guide, who validates, corrects, and indicates reference information to help the algorithm find the correct location.

[0010] In the category of fully automatic methods, the algorithm automatically determines artery localization. Most of these automatic methods specifically address the problem of carotid artery localization and do not function optimally with other arteries. Indeed, the carotid artery is a less demanding case than other arteries such as the radial artery for several reasons. The carotid artery is larger with thicker walls and exhibits 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 exhibit better contrast. It is thus 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 show fewer anatomical features that could be mistaken for the artery itself. The larger size of the carotid artery also contributes to this, as it limits the fields of view of the ultrasound images, resulting in images with fewer noisy objects. Most methods developed for the carotid artery focus on the longitudinal dimension, further simplifying localization by simply identifying the horizontal trace with the lowest intensity profile. Other anatomical features have a less noisy footprint in this dimension. Most algorithms vertically locate the area with the lowest intensity by scanning the entire image; subsequently, the artery is located based on a consensus of the identified points after removing outliers.These methods, based on the longitudinal dimension, are sensitive to probe orientation and all require precise probe positioning on the skin during data acquisition. Uncertainty in orientation will result in inaccurate measurements of diameter and other properties. This problem can be circumvented by using multidimensional probes, but these are more expensive.

[0011] Although the transverse dimension is more difficult to treat, because the shape of anatomical objects in this dimension appears frontal and disrupts 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 against probe orientation errors.

[0012] Existing solutions for automatic localization along the transverse dimension of arteries, or more generally blood vessels, are scarce 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, are complex to implement, and require enormous resources. Achieving real-time performance is difficult, especially on a portable computer. These methods are found integrated into assistive interfaces, for which time and resource requirements are not limited. Similarly, neural networks, characterized by their black-box nature, suffer from the problem of unpredictability. It has been shown that a single pixel in an image can lead a neural network to provide aberrant data.This problem is, in fact, the main reason slowing down the adoption of artificial intelligence in the medical field, and in areas with serious consequences.

[0013] Doppler-based solutions allow localization through frequency changes related to blood flow. These methods are limited to vertical localization, where the depth of the artery is determined. Horizontal localization is performed manually. Another drawback of these methods is that Doppler localization requires monitoring ultrasound frequencies over time; this implies that all these methods require the temporal dimension to localize the artery.

[0014] Other limitations are evident in several aspects, such as, for example, execution speed and compliance with real-time standards. Iterative search algorithms, for instance, are slow and require considerable resources to achieve real-time operation.

[0015] Algorithms requiring a reference model and prior information are limiting. Another aspect relates 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 computational effort.

[0016] Other methods are applied to other technological solutions that fall outside the scope of ultrasound, such as intravascular ultrasound (IVUS) and coherence tomography (CTT) methods. Optical coherence tomography (OCT) is used in both methods. Although these methods process images of arteries, they do not address the same problem because the profile of the processed data differs in terms of intensity, noise, and complexity. Furthermore, these methods inherit the drawbacks of these technologies, such as invasiveness in the case of IVUS methods and depth limitations in the case of non-invasive OCT methods.

[0017] Patent application WO2016057233 describes 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 the inflation and / or deflation of the cuff to iteratively estimate the subject's blood pressure. The ultrasound component allows for the estimation of the artery's diameter and the calculation of its elasticity.

[0018] The localization of the artery in this solution is limited by several factors: first, the transducer arrays are fixed by a qualified operator at an approximate location, and the artery is localized in three dimensions using an iterative search method based on assumed geometric characteristics and ultrasound response. The search is not only iterative, which is computationally intensive, but it is also performed in a 3D search space, thus involving a large amount of data. This limits the execution speed, particularly for devices with limited resources and power consumption. Furthermore, this 3D search is performed by comparing the results at each iteration with a reference artery model constructed using personal information about the subject, such as their age, height, etc.Besides requiring a reference model and prior information, this solution also necessitates a two-dimensional probe to enable acquisition in the 3D space on which the iterative search algorithm runs. With slow localization, refreshing the artery's position imposes a compromise on speed, accuracy, and the person's movement.

[0019] Document [1] presents a method for characterizing the carotid artery that can be applied to its longitudinal and transverse dimensions. 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 deform in scale, translation, and rotation. The method converges to the carotid artery contours through an optimization algorithm. The convergence criterion is ensured by a sum of the absolute differences between the model and the image at each point in the image. The model parameters are optimized. The model is iteratively analyzed using an energy minimization algorithm, and then the sum of 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 the precise segmentation of the detected artery contours.

[0020] In this document, the carotid artery is depicted as quite large and circular, with a relatively thick wall, making 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, the radial artery often appears neither perfectly circular nor perfectly rectangular due to its small size. Second, the radial artery wall is thin, making it impossible to distinguish its layers with standard probes at low center frequencies; this requires high-quality probes at higher ultrasound frequencies.For this reason, and given that the reference method [1] relies on differences in local contrast between 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, as is the case with the radial artery.

[0021] Also, anatomical objects in transverse images of the carotid artery, such as the jugular vein, have shapes that are not very circular and are easily distinguishable from the carotid artery. For this reason, the algorithm is unlikely to converge on 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 on other objects instead of the radial artery, because the criterion of the sum of absolute differences will also be satisfied, for example, by the veins neighboring the artery.

[0022] The method requires image enhancement preprocessing based on adaptive histograms to reduce noise. This is because noise can significantly increase the convergence time 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 function correctly within the required timeframe.

[0023] Also, the model may not be flexible enough to handle these variations, resulting in misalignment. For this reason, this method is more suitable for applications that do not require strict real-time constraints, and a Regular time-of-flight measurements are taken at fixed intervals. The method compares image pixels with a model at each image point, calculating the sum of absolute differences criterion for each pixel at each time. This operation is computationally intensive, necessitating the use of optimization algorithms to reduce computation time. The drawback 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 image complexity and vulnerable to convergence problems such as local minima. Furthermore, the optimization may converge towards veins rather than the artery in transverse scans of the radial artery.

[0024] 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 used to detect lines in images that 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 powerful Gaussian filter reduces noise. Although several fine morphological structures are destroyed in this process, the carotid artery appears clearer. Next, the Hough transform detects all dark circles by considering all rays that fall within a reasonable range. The coordinates of the detected centers (xi, yi) and the rays (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) along with a radius (rci) are stored in a matrix of potential candidates. Once all the 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.

[0025] This third method is also unsuitable for the radial artery for the following reasons. The carotid artery has a fairly circular shape; it stands out as a rather dark area and is easily distinguished from other anatomical objects. The shape of the radial artery on ultrasound images does not appear perfectly circular. Moreover, there are several anatomical objects similar to the artery, for example, veins, which are nearby, sometimes darker than the radial artery, and also have a circular shape. The Hough transform can be adapted to other, more complex shapes to apply it to the radial artery; however, this considerably increases the computational intensity, because the circular shape as presented in document [2] already performs a search in 3D space, which requires much more computation than with the Hough transform. standard. Indeed, for each circle diameter, the method calculates a new accumulator across the entire image. The radial artery can easily deform, for example, into an elliptical shape, which implies an additional parameter to be considered by the Hough transform. The shape of the radial artery is much more distorted than that of the carotid artery because its size is smaller, resulting in smaller contours that are therefore more distorted by the noise observed in ultrasound images. The Hough transform provides multiple detections, and choosing based on the darkest area will not work in the case of the radial artery because neighboring veins may be darker. These multiple detections cause a large number of false detections, which necessitates either the use of more complex criteria or consideration of the temporal dimension to improve localization robustness.For this reason, the method requires five successive images. The circular Hough transform requires an edge detection step and threshold adjustments. Edge detection is vulnerable to noise in the image, which implies the need for preprocessing enhancement and noise filtering. For this reason, the method in document [2] specifies that strong Gaussian filtering is necessary.

[0026] In general, prior art methods do not offer a sufficiently robust solution to the influence of noise and interference in ultrasonic signals.

[0027] A new method is proposed based on the characterization of symmetrical structures in an ultrasound signal, corresponding to symmetrical parts of the object to be characterized. For example, in the case of a blood vessel, the symmetrical parts correspond to the distal and proximal walls of the vessel.

[0028] The invention offers the advantage of improved robustness to noise and interference through the use of descriptors adapted to the structure of the signal to be characterized and the search for a symmetrical structure using these descriptors. In this way, the invention allows for more precise localization of points of interest such as the walls of a blood vessel.

[0029] The invention thus relates to a method for characterizing an object by ultrasonic imaging, the method comprising the steps of: - To acquire, using an ultrasonic transducer, an ultrasonic signal resulting from the reflection of an ultrasonic field emitted by the transducer on an area of ​​interest including the object, - Select a first predetermined portion of the signal comprising at least two local extrema, - Determine a descriptor for the first portion, - Search, in the ultrasonic signal, for a second portion of the ultrasonic signal whose same descriptor, determined for this second portion reversed temporally, is closest to the descriptor of the first portion. - Deduce that the first and second portions of the signal correspond to two symmetrical parts of the object to be characterized.

[0030] According to a particular aspect of the invention, the descriptor is taken from at least one or a combination of the following operations: - A set of respective time distances between the local extrema of the signal portion, - A set of ordering relationships between the amplitudes of the local extrema of the signal portion, - A set of absolute or relative amplitude differences between local extrema of the signal portion, - A set of ranks of the amplitudes of the local extrema of the signal portion, - A set of skewness coefficients for the local extrema of the signal portion, - The highest amplitude among the local extrema of the signal portion, - The number of local extrema whose amplitude exceeds, in absolute value, a predefined threshold,

[0031] According to a particular aspect of the invention, the step of searching for a second portion of the signal comprises the substeps of: - Calculate the descriptor for several different portions of the signal, each portion being time-reversed with respect to the first portion of the signal. - Calculate an error between each calculated descriptor and the descriptor determined for the first portion of the signal, - Select the second portion of the signal as having the lowest error.

[0032] According to a particular aspect of the invention, the object is an anatomical object exhibiting symmetry in the plane of acquisition of the ultrasonic signal.

[0033] According to a particular aspect of the invention, the object is a blood vessel, the first portion of the signal corresponds to an area comprising a wall of the vessel in a transverse plane and the second portion of the signal corresponds to an area comprising the opposite wall.

[0034] According to a particular aspect of the invention, the method further includes determining the diameter of the blood vessel from the time distance between the two walls of the vessel.

[0035] The invention also relates to an ultrasonic imaging device comprising an ultrasonic transducer and a processing unit configured to carry out the steps of the method according to the invention.

[0036] Other features and advantages of the present invention will become more apparent from the following description in relation to the following accompanying drawings.

[0037] [Fig. 1] represents a diagram of a multi-element ultrasonic probe capable of performing an ultrasonic signal acquisition sequence to image an anatomical object,

[0038] [Fig.2] represents a flowchart describing the steps for implementing a method for characterizing an anatomical object according to an embodiment of the invention,

[0039] [Fig.3] illustrates a first example of an ultrasound signal measured for an artery radial,

[0040] [Fig.4] represents a second example of an ultrasound signal measured for an artery radial,

[0041] [Fig.5] represents an area of ​​interest corresponding to the distal wall of an artery in the signal of [Fig.4],

[0042] [Fig.6] represents a first hypothesis of the position of the distal wall in the area of interest of [Fig.5],

[0043] [Fig.7] represents a second hypothesis of the position of the distal wall in the area of ​​interest of [Fig.5],

[0044] [Fig.8] represents a third hypothesis for the position of the distal wall in the area of interest of [Fig.5],

[0045] Figure 2 represents, on a flowchart, the main steps of implementation of a method for characterizing an anatomical object, according to an embodiment of the invention.

[0046] The invention is described for a non-limiting example where the anatomical object is a radial artery, but it extends to any type of anatomical object, in particular any type of blood vessel.

[0047] The method begins in step 201 with the acquisition of at least one ultrasonic signal.

[0048] This acquisition is carried out using a probe shown in [Fig.1].

[0049] The acquisition probe is advantageously a linear multi-element probe 101, that is, one which comprises several ultrasonic elements 102, for example piezoelectric elements, aligned in a row. Alternatively, a probe in two dimensions, that is to say including an array of ultrasonic elements can also be used.

[0050] In the example of [Fig.1], the probe 101 is positioned in contact with the skin 103 so that the row of elements 102 is substantially in a transverse plane of an artery 104 to be imaged.

[0051] Advantageously, the acquisition carried out in step 101 is done in such a way that the active ultrasonic elements of the probe 101 are centered on the artery 104 so as to measure a signal corresponding to a path crossing the artery 104 in its center.

[0052] More generally, the acquisition is carried out in such a way that the ultrasound signal intercepts the anatomical object at one or more points of interest that one seeks to characterize. In the case of artery 104, these points of interest are, for example, diametrically opposed points on the inner wall of the artery.

[0053] In one embodiment of the invention, the acquisition in step 101 is not limited to a single time-domain signal but aims to determine an ultrasound image of the artery. In this case, the acquisition 101 is performed by means of a successive scan during which, at each step, an ultrasound 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 ultrasound signal corresponding to a predefined acquisition time and to an axis substantially perpendicular to the group of elements 102.

[0054] This step allows the acquisition of an ultrasound signal. It is then iterated by shifting the active ultrasound elements by one element according to a sliding window, and then performing a new acquisition. Thus, by performing several successive acquisitions using a group of elements of fixed or variable size that scans all the elements of the probe, several ultrasound signals are obtained which together form an ultrasound image of a transverse plane of artery 104.

[0055] This acquisition makes it possible to obtain in two dimensions a transverse ultrasonic imprint of the targeted radial artery as well as any other anatomical objects present around it such as veins.

[0056] Figure 3 shows an example of an ultrasound signal obtained for a radial artery. The signal corresponds substantially to a path passing through the center of the artery and successively intercepting a proximal and a distal wall of the artery.

[0057] In theory, that is to say in the absence of noise and interference, the signal obtained should be symmetrical with a first set of extrema 301 corresponding to the reflections of the signal on the different layers of the proximal wall, a zone 302 of low amplitude corresponding to the zone of the blood and a second set of extrema 303 corresponding to the reflections of the signal on the different layers of the distal wall.

[0058] However, in the presence of noise, the symmetry of the signal can be partially affected as illustrated in [Fig. 3]. Indeed, although each set of extrema 301,303 has the same number of extrema (amplitude peaks), in this case three local maxima, these maxima do not have the same amplitudes in the two areas 301,303.

[0059] However, the inventors observed that the intra-maximal distances in the first zone 301 are of the same order of magnitude as in the second zone 303. The distances are expressed as a number of time samples. Indeed, in the first zone 301, the distances between the first and second peaks and between the second and third peaks are 15 and 18 respectively, while they are 19 and 15 in the second zone 303. The distances between the first and third peaks are equivalent (33 versus 34).

[0060] The structure comprising three local maxima corresponds to the reflections of the signal on three consecutive interfaces of the artery wall.

[0061] Although amplitude distortions or slight position differences exist due to the influence of noise and interference, a certain level of symmetry exists in the signal due to the symmetry of the body traversed by Fonde ultrasonore.

[0062] An objective of the invention is to seek this symmetry in the signal in order to better position the points of interest of the body to be imaged, for example the positions of the proximal and distal walls of an artery.

[0063] The invention is now described by way of a non-limiting example illustrated in support of Figures 4 to 8.

[0064] Figure 4 represents another example of an ultrasound signal obtained for a path passing through the center of a radial artery.

[0065] On this signal, a central area of ​​low amplitude 401 is identified, corresponding to the area of ​​blood inside the artery.

[0066] To the right of this zone 401, four local maxima A, B, C, D are identified which correspond to a zone of the distal wall of the artery.

[0067] In step 202 of the method, a region of interest is identified in the signal comprising several extrema. In the example of [Fig. 4], this region of interest corresponds to the set of four local maxima A, B, C, D that characterize the distal wall of the artery. It is assumed that peak A corresponds to the interface between the distal wall and the blood, and peak D corresponds to the external interface of the wall. The detection of this region of interest can be performed by any suitable signal processing technique.

[0068] One objective of the invention is to search in the signal for the symmetrical area of ​​interest which corresponds to the proximal wall of the artery.

[0069] In step 203, a descriptor of this area of ​​interest is then calculated, and more precisely a descriptor of the set of local extrema.

[0070] In general, a signal descriptor is a digital data vector that synthetically and unambiguously describes a signal.

[0071] A descriptor is extracted or deduced from the signal and allows certain properties of the signal to be summarized or quantified in a more compact way for the purpose of analyzing this signal.

[0072] The distal wall is characterized by the sequence of local maxima A, B, C, D which correspond to reflections of the signal on different interfaces constituting this wall.

[0073] The invention aims to use descriptors of this sequence which make it possible to preserve the symmetrical structure of the signal while being robust to noise and interference.

[0074] Different types of descriptor examples can be considered.

[0075] According to a first embodiment, the descriptor is defined by a set of relations linking two extrema of the sequence. For example, these relations characterize the distance between two extrema or the amplitude evolution between two extrema.

[0076] The relationship between the extrema A and B can be noted Rel (A, B)= (dxAB, dyAB).

[0077] dxAB is the distance between extrema A and B,

[0078] dyAB is the amplitude evolution between the extrema A and B, dyAB is 1 if the amplitude of B is greater than that of A and is -1 in the opposite case.

[0079] The signal structure in the area of ​​interest corresponding to the distal wall is thus defined by the set of Rel relations between extrema:

[0080] Rel(A,B), Rel(B,C), Rel(C,D).

[0081] Optionally, the relations between non-neighboring extrema, for example between A and C or between A and D, can also be integrated into the descriptor.

[0082] More generally, the above example can be replaced by any order relation between several extrema. An order relation is a binary relation defined on a set E that allows the elements of this set to be compared with each other according to a given criterion.

[0083] An example of a Desc descriptor of the area of ​​interest associated with the distal wall is therefore:

[0084] Desc(dist)= {(dxAB, dyAB), (dxBC, dyBC), (dxCD, dyCD)}

[0085] Figure 5 represents the signal interest area corresponding to the distal wall. The descriptor calculated for this interest area is:

[0086] Desc(dist)={(19,1),(14,1),(15,-1)}

[0087] In step 204 of the method, the symmetrical area of ​​interest is then sought in the signal, corresponding to the proximal wall, by calculating the same descriptor for several hypotheses of position of the proximal area of ​​interest.

[0088] Figures 6, 7 and 8 represent three different hypotheses for the proximal area of ​​interest.

[0089] Indeed, the left part of the signal has several amplitude extrema.

[0090] Fig. 6 represents a hypothesis in which the proximal area of ​​interest includes the local maxima PI, P2, P3, P4, which are the four local maxima located immediately to the left of the blood zone.

[0091] The same descriptor is calculated for the sequence of maxima, in reverse order with respect to the distal area of ​​interest (signal symmetry).

[0092] Thus, the descriptor calculated for the hypothesis of [Fig.6] is:

[0093] Desc(proxi!)= {(dxPlP2, dyPlP2), (dxP2P3, dyP2P3), (dxP3P4, dyP3P4)}

[0094] Desc(proxii)={ (16,1),(14,1),(19,-1)}

[0095] Fig. 7 represents a second position hypothesis for the proximal area of ​​interest which this time includes the local maxima P2, P3, P4 and P5.

[0096] The descriptor calculated for this second hypothesis is:

[0097] Desc(proxi2) = {(14,1),(19,-1),(15,1)}

[0098] Fig. 8 represents a third positional hypothesis for the proximal area of ​​interest which this time includes the local maxima P3, P4, P5 and P6.

[0099] The descriptor calculated for this third hypothesis is:

[0100] Desc(proxi3) = {(19,-1),(15,1),(15,-1)}

[0101] In step 205, for each proximal zone hypothesis, an error is calculated between the proximal zone descriptor and the distal zone descriptor.

[0102] The error is calculated, for example, using the following relationship:

[0103] jg»«*>*imo£(|dxAB. dxPIP2l + IdxBC- dxP2P3i + IdxCD- dxP»4l)+Æ(|dyAB- dyP1P2| + |dyBC- dyP2P3| + |dyCD- dyP3P4|).

[0104] a and P are weighting coefficients chosen so as to give more or less weight to the distances between extrema or to the evolutions of amplitudes.

[0105] Alternatively, any other distance can be used to calculate an error between two descriptors.

[0106] For the example of Figures 6, 7 and 8, the following respective results are obtained for the three hypotheses regarding the position of the proximal interest zone:

[0107] ^proximal l _ y, ^proximal! _ j^proxinudei _ 3

[0108] We then retain the hypothesis which gives the smallest error, in this case the third hypothesis of [Fig.8], which gives the proximal area of ​​interest which has the highest degree of symmetrical resemblance with the distal area of ​​interest.

[0109] This result leads to the selection of the local maximum P3 as corresponding to the proximal inner wall although other local maxima (PI and P2) are closer to the blood zone.

[0110] Without departing from the scope of the invention, other descriptors may be used in combination or as a replacement for the descriptor described above, in particular the descriptors below for which y denotes the amplitude of the extrema: - an absolute difference in amplitude between two extrema: ly(A)-y(B)l - a relative difference in amplitude between two extrema: I y(A) - y(B) I / (y(A) + y(B)) - a transition direction of amplitude between two extrema: sign (y(A)-y(B), - the rank of the extrema, that is to say the ranking of the extrema in the sequence according to their amplitude: rank(A)=rank(B)+l if y(A)>y(B) - the asymmetry coefficient of an extremum, which is an indicator of signal distortion, for example given by the formula THAT -

[0111] where x(n) are the signal samples, N its size, μ its mean, and 0 its standard deviation. This indicator allows us to evaluate the asymmetry of the signal's distribution of values ​​relative to its mean. - the maximum of greatest amplitude within the considered sequence, - the number of prominent maxima, that is to say, those whose amplitude exceeds a given threshold, - the distance between a maximum and the beginning of the blood zone: IX(A)-X(blood)l

[0112] Taking up again the example of figures 5 to 8, another possible descriptor consists of determining all the amplitude transitions between all the pairs of local maxima.

[0113] These transitions can be formalized in a transition matrix of amplitudes.

[0114] The transition matrix for the distal region of interest comprising the four local maxima A, B, C, D (illustrated in [Fig. 5]) is given by: Dist al transition matrix Maxim to ABCD

[0115] (corresponding respectively to figures 6, 7 and 8) are given by: Proximal zone 1 Maxim a PI P2 P3 P4 PI 0 1 1 1 P2 -1 0 1 1 P3 -1 -1 0 -1 P4 -1 -1 1 0 Proximal zone 2 Maxim a P2 P3 P4 P5 P2 0 1 1 1 P3 -1 0 -1 1 P4 -1 1 0 1 P5 -1 1 1 0 Proximal zone 3 Maxim a P3 P4 P5 P6 P3 0 -1 1 -1 P4 1 0 1 1 P5 -1 -1 0 -1 P6 1 -1 1 0

[0116] Thus, Hypothesis No. 1 ([Fig. 6]) shows 8 / 12 transitions identical to the distal zone descriptor. Hypothesis No. 2 ([Fig. 7]) shows 6 / 12 transitions identical to the distal zone descriptor. Hypothesis No. 3 ([Fig. 8]) shows 8 / 12 transitions identical to the distal zone descriptor.

[0117] The third hypothesis has the highest similarity score, equal to that of the first hypothesis.

[0118] This example shows that the single descriptor based on amplitude transitions is not sufficient to discriminate between two hypotheses and must be combined with at least one other descriptor.

[0119] The invention makes it possible to locate precisely the two proximal and distal walls of a blood vessel which are by nature symmetrical.

[0120] Once these two walls are located, the diameter of the vessel can be deduced if the path of the signal passes through the center of the vessel. References

[0121] [1] JH Gagan et al., “Automated Segmentation of Common Carotid Artery in Ultrasound Images," in IEEE Access, vol. 10, pp. 58419-58430, 2022

[0122] [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

1.

2. Demands Method for characterizing an object by ultrasonic imaging, the method comprising the steps of: - Acquire (201), by means of an ultrasonic transducer, an ultrasonic signal resulting from the reflection of an ultrasonic field emitted by the transducer on an area of ​​interest including the object, - Select (202) a first predetermined portion of the signal comprising at least two local extrema, - Determine (203) a descriptor of the first portion, - Search (203,204), in the ultrasonic signal, for a second portion of the ultrasonic signal whose same descriptor, determined for this second portion reversed temporally, is closest to the descriptor of the first portion. - Deduce that the first and second portions of the signal correspond to two symmetrical parts of the object to be characterized. Method for characterizing an object by ultrasonic imaging according to claim 1, wherein the descriptor is taken from at least one or a combination of the following operations: - A set of respective time distances between the local extrema of the signal portion, - A set of ordering relationships between the amplitudes of the local extrema of the signal portion, - A set of absolute or relative amplitude differences between local extrema of the signal portion, - A set of ranks of the amplitudes of the local extrema of the signal portion, - A set of skewness coefficients for the local extrema of the signal portion, - The highest amplitude among the local extrema of the signal portion, - The number of local extrema whose amplitude exceeds, in absolute value, a predefined threshold,

3. Method of characterizing an object by ultrasonic imaging according to any one of the preceding claims wherein the step of searching for a second portion of the signal comprises the substeps of: - Calculating (204) the descriptor for several different portions of the signal, each portion being time-reversed with respect to the first portion of the signal, - Calculating (205) an error between each calculated descriptor and the descriptor determined for the first portion of the signal, - Selecting the second portion of the signal as having the lowest error.

4. Method of characterizing an object by ultrasonic imaging according to any one of the preceding claims wherein the object is an anatomical object exhibiting symmetry in the plane of acquisition of the ultrasonic signal.

5. Method of characterizing an object by ultrasound imaging according to any one of the preceding claims wherein the object is a blood vessel, the first portion of the signal corresponds to an area comprising a vessel wall in a transverse plane and the second portion of the signal corresponds to an area comprising the opposite wall.

6. Method of characterizing an object by ultrasonic imaging according to claim 5 further comprising determining the diameter of the blood vessel from the temporal distance between the two walls of the vessel.

7. Ultrasonic imaging device comprising an ultrasonic transducer and a processing unit configured to perform the steps of the method according to any one of the preceding claims.