Method for characterizing a tubular-shaped object such as a blood vessel using ultrasound imaging

Abstract

Method for characterizing a tubular object by ultrasonic imaging, the method comprising the steps of: Acquiring (201) several ultrasonic signals from the reflection of an ultrasonic field emitted by the transducer on a region of interest in a cross-sectional plane of the object, for different positions of the transducer relative to said region, the set of ultrasonic signals forming an ultrasonic image of the region; for each signal corresponding to a vector of the image, applying (202) a first predetermined filter to the signal; Selecting (203), from the set of signals, the signal for which the result of the filter has the highest absolute value and recording the abscissa of this extremum; Determining (204) the center of the object from the recorded abscissa and the velocity of the ultrasonic 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 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 of the human body in order 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 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.

[0004] In this general field, there is a particular need to characterize arteries by detecting their center and measuring their diameter.

[0005] 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.

[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 graphic tools applied to ultrasound images.

[0008] 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.

[0009] In the category of fully automatic methods, the algorithm determines at Automatically localizing arteries is not the primary method. Most of these automated methods specifically address the problem of carotid artery localization and do not function optimally with other arteries. Indeed, the carotid artery is less challenging 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 carotid artery has a larger diameter than the radial artery, ultrasound images exhibit 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 show fewer anatomical structures that could be mistaken for the artery itself.The larger size of the carotid artery also contributes to this advantage, 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 structures 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 longitudinal-dimensional-based methods are sensitive to probe orientation and all require precise probe positioning on the skin during acquisition.Uncertainty in orientation 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 deal with, because the shape of anatomical objects on 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 diameter measurements remain robust and reliable with respect to probe orientation errors.

[0011] 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. This is especially true for portable computers. These methods are found integrated into assistive interfaces where 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 produce aberrant data. This problem is, in fact, the main reason slowing the adoption of artificial intelligence in the medical field and in areas with serious consequences.

[0012] 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.

[0013] 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.

[0014] 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.

[0015] Other methods are applied to other technological solutions that fall outside the scope of ultrasound, such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Although these methods process images of arteries, they do not address the same problem, as the profile of the processed data differs in terms of intensity, noise, and complexity. These methods also inherit the drawbacks of these technologies, such as invasiveness in the case of IVUS and depth limitations in the case of non-invasive OCT.

[0016] 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.

[0017] 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.

[0018] 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 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 in the image. The model parameters are iteratively optimized using the energy minimization algorithm, and 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 a precise segmentation of the detected artery contours.

[0019] In this document, the carotid artery is quite large and circular, and its wall is quite thick, 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 with a shape that is neither perfectly circular nor The radial artery is perfectly rectangular due to its small size. Its thin wall makes it impossible to distinguish its layers with standard probes at low center frequencies, necessitating 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, the algorithm will struggle 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.

[0020] 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.

[0021] 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.

[0022] Also, the model may not be flexible enough to handle these variations, leading to poor alignment. Therefore, this method is better suited to applications that do not require strict real-time constraints and regular time-of-flight measurements at fixed intervals. The method compares the 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, requiring the method to use 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, optimization may converge towards the veins rather than the artery on transverse scans of the radial artery.

[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 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.

[0024] 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 standard Hough transform.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 smaller size results in smaller contours, which 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 the use of more stringent criteria. Complex, it is necessary to consider the temporal dimension to improve localization robustness. For this reason, the method requires 5 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 enhancement preprocessing and noise filtering. For this reason, the method described in the document specifies that strong Gaussian filtering is required.

[0025] All the aforementioned methods have the drawbacks described. There is therefore a need for a new method that 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 noise and the presence of other biological objects.

[0026] The invention allows for the automatic localization and characterization of a radial artery in its cross-section. It involves a first phase of localizing the center of the artery from several ultrasonic acquisitions forming a 2D image of the cross-section including the section of the artery, then a second phase of determining the diameter of the artery from the ultrasonic 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 uncertainties in angular positioning. 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 taken 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 specifically designed for detecting an artery or, more generally, a tubular object. Once the center of the artery is located, methods for extracting the artery's properties are applied locally to the filter output.

[0029] The proposed method has low complexity and can be easily implemented, since almost all localization operations are performed as simple operations. This constitutes a considerable advantage of simplicity and allows the method to be embedded on portable systems with limited resources and to perform time-of-flight measurements. The proposed method requires no comparison with a reference model, no calculation of a similarity criterion at each point, no intensive iterative computation, and no risk of iterative convergence or local problems that plague optimization algorithms. Another advantage of the invention is that it can run synchronously with the acquisition incrementally as signals are received, without needing to wait for the completion of The acquisition process allows for rapid updating of the artery's location. Unlike existing methods, the proposed method does not need to store a large number of parameters, as it gradually updates the summation and extremum point coordinates as the calculation progresses. Some existing solutions rely on landmarks such as contours, making them vulnerable. Some of these solutions use noise reduction or outlier removal methods, increasing complexity and computational requirements. The proposed solution is based on whole-image information, making it robust to noise and eliminating the need for image enhancement preprocessing or outlier removal algorithms.

[0030] The proposed solution performs localization directly in the spatial dimension with a single transverse acquisition and does not require multiple acquisitions in the temporal dimension, unlike Doppler-based methods. The proposed method does not require prior information about the individual or a reference artery model. With rapid localization, the artery position is updated quickly, offering the possibility of a higher measurement frequency, greater accuracy, and greater freedom of movement for the individual.

[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 ultrasonic testing to characterize any type of tubular object.

[0032] The invention relates to a method for characterizing a tubular object by ultrasonic imaging, the method comprising the steps of: - Acquiring, by means of an ultrasonic transducer, several ultrasonic signals resulting from the reflection of an ultrasonic 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, the set of ultrasonic signals forming an ultrasonic image of the area, - Choose a dimension for the ultrasonic 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 and record the x-coordinate of this extremum, - Determine the center of the object from the measured abscissa 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 on a sliding window of predefined size depending on the size of the signal and / or prior information on the dimension of the object, the filter being defined on 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 one 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 the selected ultrasonic signal, - Detect at least two extrema of said signal that are above the threshold, - Select the pair of extrema, including 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, record 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 one embodiment, the method according to the invention comprises the steps of: - Select a third extremity that is higher than the threshold and located immediately before the first extremity. - Select a fourth extremity that is higher than the threshold and located immediately after the second extremity. - Record the time abscissas of the third extremum and the fourth extremum 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 substeps of: - Position a transducer comprising several aligned elements on an area of ​​the skin in order to image a cross-section of the blood vessel, - Perform 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 ultrasonic imaging device comprising an ultrasonic transducer and a processing unit configured to carry out the steps of the method according to the invention.

[0043] Other features and advantages of the present invention will become more apparent from the following description in relation to the following accompanying drawings.

[0044] [Fig. 1] shows a diagram of a multi-element ultrasonic probe capable of performing an ultrasonic signal acquisition sequence to image a blood vessel,

[0045] [Fig. 2] shows a flowchart describing the implementation steps of a method for detecting the center of a blood vessel according to an embodiment of the invention,

[0046] [Fig.3a] represents an ultrasonic 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.1],

[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 for implementing a method for determining the diameter of a blood vessel according to an 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] Figure 2 shows, in a flowchart, the main implementation steps of a method for determining the center of a blood vessel, for example a radial artery, according to an embodiment of the invention.

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

[0057] This acquisition is carried out using a probe shown in [Fig. 1].

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

[0059] In the example of [Fig. 1], the probe 101 is positioned in contact with the skin 103 so that the row of elements 102 lies 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 that the artery be covered in both 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 performed 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 time and to an axis substantially perpendicular to the group of elements 102.

[0061] This step acquires 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 fixed-size group of elements 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.

[0062] 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.

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

[0064] Fig. 3a represents an example of an ultrasonic image obtained with identification of the radial artery 104. It can be seen that on this image, the impression of the artery 104 is difficult to detect because it is drowned out by the noise and polluted by the impressions 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 ultrasonic measurement that corresponds to a path passing through this center.

[0066] Figure 4a shows an example of an ultrasound signal acquired during a single acquisition. The signal in Figure 4a corresponds to a column of the matrix in Figure 3a. Indeed, each column of this matrix corresponds to an acquisition from a point on the skin surface. More precisely, the signal in Figure 4a corresponds to the envelope or 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 to 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 peaks of amplitude which correspond to the echoes on the walls of the artery. Between these two peaks lies 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 includes two extrema of the same sign into another signal which includes an extremum of 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 to be detected and is composed of three successive parts. The central part 401 is fixed at a positive value, for example equal to 1, for a duration corresponding to a distance less than the internal diameter of the artery.

[0071] The other two parts 402, 403 are fixed 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 acquired signal at the corresponding zone At the artery, the amplitude peaks corresponding to the artery walls will be aligned with the outer regions of filter 402, 403, so their contribution will be summed negatively. Conversely, when the central part of the filter does not coincide with the central region between the two amplitude peaks but does coincide with one of the amplitude peaks, then the result of the convolution with the filter will be a positive value.

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

[0076] Figure 4d shows the final result obtained by setting all positive values ​​to zero. The extremum of the signal in Figure 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 of [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 to be analyzed. 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 to be detected.

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

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

[0081] If we denote 5 as the envelope or 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 signal 5' located in the interval [a, , ∞[. The filter is defined by applying functions to several regions of interest. In the example in [Fig. 4b], the number of regions of interest is 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

[0086]

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[0099] the set of points belonging to the ROIr region. ar is a predefined weighting coefficient. In the example in Figure 4b, there are three interest regions, so Nr = 3 and the functions f are all the sum function with coefficients equal to -1 for the outer interest regions and +1 for the central interest region of the filter. More generally, the coefficients can have an absolute value other than 1; the filter F can then be written as: F- aj\ROI S+aJ^ROI \-aJ3(ROI ) ■- Maximum wall! « Distal wall / ' v Valley' The ROLaiiée interest zone corresponds to the central zone of the filter and the ROIparoiftoximaie and ROIparoiDistaie interest zones correspond to the outer zones of the filter. For the example in [Fig.4b], the filter can be expressed as: F - vertex ROL, sumAROI - vertexROI„, Suitcase! (Proximity wall) Distal wall Other forms of filtering can be considered, either calculated directly or calculated locally on the points detected by a first filtering step. In particular, filters composed of five regions of interest can be considered to account for the external and internal walls of a vessel. Indeed, the external and internal walls are separated by a non-echoic medial layer. The acquired signal can therefore exhibit, for each of the proximal and distal walls, two amplitude peaks separated by a near-zero region, 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' ' MedialDistalLayer / -a^ROI} ' Suitcase A specific example of a filter comprising five areas of interest is: + Max(R ■„ . , ) -moytJF, - - pa king Proximal / •• x LayerMedi anP roxunale) + MaxtRn J -moyOï , ,, , ParmDistale! ■ v LayerMedianDtstaue -moy(Ru ... ) • v Vaille / Max is the maximum value of the signal over the area of ​​interest and moy() is the average of the signal over the area of ​​interest. Another example of 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 For miliary arteries, 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 shape will allow it to be distinguished:

[0100] = F- a ^(ROI ) -a7f(ROI . ) 'Left vein' 'Right vein'

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

[0102] In general, the filter is defined on 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 the opposite sign.

[0103] When the filter is defined on five time intervals, it is then defined on at least one additional first 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 have opposite signs.

[0105] In general, alternating the signs of the filter coefficients over two consecutive intervals makes it possible to obtain a filtered signal which has an extremum 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, iteratively, for example by applying the filter to the entire signal during a first iteration and then applying another filter to the result of the first filtering, centered on a reduced time area, during the following iterations.

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

[0108] Alternatively or in addition, it is also possible to apply the same treatments 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 to all signals (equivalent to Fig. 4c for one signal).

[0110] Figure 3c represents the final result after zeroing the positive values. Artery 104 can be precisely identified in the image of Figure 3c.

[0111] The result of each filtering operation can be stored in memory and refreshed incrementally. This method maintains the optimum each time. It is also possible to partially store the optimal result of each signal, or to store all the filtering results, depending on the available resources. Then, in step 203 of the method, the filtered signals with the highest absolute amplitude are searched for among all the signals. The corresponding signal selected will be the one that passes through the center of the artery. Indeed, this signal must have the largest valley area (between the two walls of the artery) and therefore generate the largest negative contribution among all the signals. This is also reinforced by the fact that the wall often has a maximum amplitude at its 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 a distance d from the velocity 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 with the determination of the center of the vessel using the method described previously in support of [Fig.2].

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

[0118] Figure 6 shows an example of such a signal on which several peaks have been identified. d'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] At step 504, the two points corresponding to the two closest extrema of the center of the vessel determined at 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, t1 are the abscissas of the two extrema identified on [Fig.6].

[0122] In an optional step 506, the external diameter of the vessel is further determined using the same relation D — - (t^- °ù L, t4 are the abscissas of two other extrema exceeding the threshold of step 503 and located immediately after a low-intensity zone that exhibits low echogenicity and is intermediate between the inner and outer walls of each of the proximal and distal sides. In other words, the extrema detected at step 506 are the extrema located immediately diament 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 on [Fig.6].

[0124] As explained previously, the method of [Fig.5] can also be applied to the horizontal dimension of the ultrasonic 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 specifically a radial artery, it can be applied more generally to any application involving ultrasonic 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

Demands

1. A method for characterizing a tubular object by ultrasonic imaging, the method comprising the steps of: - Acquiring (201), by means of an ultrasonic transducer, several ultrasonic signals resulting from the reflection of an ultrasonic field emitted by the transducer onto a region of interest in a cross-sectional plane of the object, for different positions of the transducer relative to said region, the set of ultrasonic signals forming an ultrasonic image of the region, - Choosing a dimension of the ultrasonic 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), on the set of signals,the signal for which the filter output has the highest absolute value and record the abscissa of this extremum, - Determine (204) the center of the object from the recorded abscissa and the speed of the ultrasonic signal.

2. Method of characterizing a tubular 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 of characterizing a tubular object according to claim 2 wherein the filter is applied to the signal on a sliding window of predefined size depending on the size of the signal and / or prior information on the dimension of the object, the filter being defined on 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. A method for characterizing a tubular object according to claim 3, wherein 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 object according to any one of claims 3 or 4 wherein the filter is defined over at least two additional time intervals.

6. Method of characterizing a tubular object according to any one of claims 3 to 5 wherein 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 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 above 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 velocity of the ultrasonic signal.

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

9. Method of characterizing a tubular-shaped object according to any one of the preceding claims wherein the object is a blood vessel, for example an artery.

10. Method for characterizing a blood vessel according to claim 9 wherein the step of acquiring (201) several ultrasonic signals comprises the substeps 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, - Performing several successive ultrasonic acquisitions from different emission points located on the alignment axis of the elements, each ultrasonic emission being performed in a direction substantially perpendicular to the alignment axis.

11. Ultrasonic imaging device comprising an ultrasonic 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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