Ultrasonic image processing method and ultrasonic imaging device
The method addresses the challenge of distinguishing echoes in ultrasonic imaging by using SVD and spatial/temporal similarity-based clustering to separate eigenvectors, resulting in clearer images of blood flow and vascular structures.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- ID4US
- Filing Date
- 2025-10-01
- Publication Date
- 2026-06-18
Smart Images
Figure EP2025078137_18062026_PF_FP_ABST
Abstract
Description
B24004PCT - Clustering DESCRIPTION TITLE: Ultrasonic image processing method and ultrasonic imaging device The present application claims priority from French patent application number 2413896, filed on December 11, 2024, entitled "Ultrasonic image processing method and ultrasonic imaging device", which is incorporated by reference to the fullest extent permitted by law. technical field
[0001] This description relates to the field of ultrasound imaging, and more specifically to the processing of ultrasound images, i.e., images obtained by an ultrasound imaging device. Previous technique
[0002] In the field of ultrasound imaging, based on the Doppler effect, the implementation of singular value decomposition (SVD) of an ultrasound image sequence allows for the differentiation of various types of echoes present in the images. This decomposition enables the generation of images containing only the desired echoes. For example, in the medical field, applying SVD to an ultrasound image sequence can be used to distinguish signals reflected by tissues, those representing blood flow, and those related to noise. It then becomes possible to isolate the blood flow signals from the other signals.
[0003] To achieve this distinction, the space-time eigenvectors resulting from a SVD are ordered by energy B24004PCT - Decreasing clustering and separated into several groups, or batches. For example, in the vascular imaging example, the spatiotemporal eigenvectors can be divided into three batches to distinguish tissues, blood flows and noise, because tissues generate the most energetic signals, blood flows generate intermediate energy signals, and finally noise generates the lowest energy signals.
[0004] The distribution of these eigenvectors within different batches is achieved by first defining one or more energy thresholds corresponding to the values separating the different batches. These thresholds can be determined arbitrarily or algorithmically. The choice of these thresholds is crucial to the quality of the final image sequence. In the example described above, only the average energy vectors related to blood flow can be retained so that the final image sequence shows only this blood flow.
[0005] Adaptive methods exist for determining optimal threshold values, for example, by correlating spatial or temporal vectors with similar energy levels, or by analyzing the energy profile, as described in the paper by J. Baranger et al., "Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors." IEEE Trans Med Imaging. 2018 Jul; 37(7). However, with such a method, if eigenvectors belonging to the same set are separated, from an energetic point of view, by other eigenvectors from a different set, it is not possible to group them correctly.
[0006] In the case of blood flow imaging, the ranking and distribution of eigenvectors according to their energy are adapted when the blood flow signal is B24004PCT - Clustering is quite distinct from that related to tissues, as is the case, for example, when this flow is that observed in an artery. However, in the case of extensive vascularization with slow flows, or heterogeneous vascularization including, for example, arteries and venules, the information related to tissues and flows may have comparable energies, and it is then no longer possible to distinguish the corresponding sets of eigenvectors.
[0007] Similar problems also arise in fields other than medical imaging, such as fluid detection by ultrasonic imaging applied to industry. Summary of the invention
[0008] There is a need to propose a method for processing ultrasonic images, as well as an ultrasonic imaging device implementing such a method, which does not present the disadvantages previously described.
[0009] One embodiment overcomes all or part of the drawbacks of known solutions and proposes an ultrasound image processing method comprising at least: - decomposition into singular values of each image of at least one initial sequence of ultrasonic images, calculating spatial eigenvectors and / or temporal eigenvectors of each of the images; - partitioning of said spatial eigenvectors and / or said temporal eigenvectors, distributing said spatial eigenvectors and / or said temporal eigenvectors into several groups of spatial eigenvectors and / or several groups of temporal eigenvectors on the basis of spatial and / or temporal similarities respectively; B24004PCT - Clustering - calculation of at least one final sequence of images by concatenation of said spatial eigenvectors and / or said temporal eigenvectors of one of the groups of vectors.
[0010] According to a particular embodiment, the partitioning of spatial eigenvectors and / or temporal eigenvectors is implemented independently of the order in which the spatial eigenvectors and / or temporal eigenvectors are obtained during the singular value decomposition of the images.
[0011] According to a particular embodiment: - the initial sequence comprises Ni ultrasonic images, each comprising Np pixels; - the singular value decomposition calculates Ni unitary spatial eigenvectors of Np values each, and Ni temporal eigenvectors of Ni values each; with Ni and Np corresponding to non-nuis positive integers.
[0012] According to a particular embodiment, the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is between 2 and 4.
[0013] According to a particular embodiment, spatial and / or temporal similarities are representative of correlations between pixel values of images from the initial sequence of ultrasonic images.
[0014] According to a particular embodiment, the ultrasonic image processing method further comprises, before the implementation of the decomposition into singular values, at least one acquisition of the initial sequence of ultrasonic images. B24004PCT - Clustering
[0015] According to a particular embodiment, the initial sequence of ultrasound images corresponds to a sequence of vascular ultrasound images.
[0016] According to a particular embodiment, the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is equal to 2 or 3 and such that the final sequence of images corresponds to a sequence of blood flow images, or the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is equal to 3 and such that a first final sequence of images corresponds to a sequence of images of a first blood flow and a second final sequence of images corresponds to a sequence of images of a second blood flow faster than the first blood flow.
[0017] According to a particular embodiment, the ultrasonic images of the initial sequence and / or the final sequence are in two dimensions or in three dimensions.
[0018] According to a particular embodiment, the ultrasonic image processing method further comprises, after the calculation of the final image sequence by concatenation of the spatial eigenvectors and / or temporal eigenvectors of one of the vector groups, the implementation of an algorithm for recognizing geometric shapes within the images of the final sequence.
[0019] According to a particular embodiment, the geometric shape recognition algorithm corresponds to a tubular shape recognition algorithm.
[0020] Also proposed is an ultrasonic imaging device configured to implement an ultrasonic image processing method according to a particular embodiment. B24004PCT - Clustering
[0021] According to a particular embodiment, the ultrasound imaging device includes an ultrasound transducer circuit configured to perform an acquisition of the initial ultrasound image sequence by pulsed Doppler imaging.
[0022] According to a particular embodiment, the ultrasound imaging device further comprises: - a data processing circuit configured to implement the ultrasonic image processing method; - a control circuit electrically coupled to the ultrasonic transducer circuit and the data processing circuit, configured to transmit ultrasonic emission control signals to the ultrasonic transducer circuit, to receive electrical measurement signals transmitted by the ultrasonic transducer circuit and to transmit electrical measurement signals to the data processing circuit. Brief description of the drawings
[0023] These features and advantages, as well as others, will be described in detail in the following description of particular embodiments, given by way of non-limiting example, in relation to the attached figures, among which:
[0024] - Figure 1 schematically represents an ultrasound imaging device according to a particular embodiment;
[0025] - Figure 2 represents, in the form of a schematic diagram, the steps of an ultrasonic image processing method according to a particular embodiment;
[0026] - Figures 3 to 5 represent images obtained by implementing an ultrasonic image processing method according to a particular embodiment; B24004PCT - Clustering
[0027] - Figures 6 and 7 represent MIP images obtained by implementing an ultrasonic image processing method according to a particular embodiment. Description of the implementation methods
[0028] The same elements have been designated by the same reference numerals in the different figures. In particular, structural and / or functional elements common to the different embodiments may have the same reference numerals and may have identical structural, dimensional and material properties.
[0029] For the sake of clarity, only the steps and elements necessary for understanding the described embodiments have been shown and are detailed. In particular, various components (ultrasonic transducer circuit, control circuit, processing circuit, etc.), different steps implemented (processing of acquired images, calculation of final images, details of the calculations performed, etc.), and the coding required to implement the various steps of the process are not detailed. Those skilled in the art will be able to implement these elements in detail based on the functional description provided here.
[0030] Unless otherwise specified, when referring to two elements connected together, this means directly connected without any intermediate elements other than conductors, and when referring to two elements linked or coupled together, this means that these two elements can be connected or linked through one or more other elements.
[0031] In the description that follows, when referring to absolute positional qualifiers, such as the terms "front", "back", "top", "bottom", "left", B24004PCT - Clustering "Right," etc., or relative terms such as "above," "below," "superior," "inferior," etc., or orientation qualifiers such as "horizontal," "vertical," etc., refer, unless otherwise specified, to the orientation of the figures in a normal operating position. However, these terms do not imply the actual position and orientation of the device during use.
[0032] Unless otherwise specified, the expressions "approximately", "roughly", "about", and "on the order of" mean within 10%, preferably within 5%.
[0033] Similarly, unless otherwise indicated, the ranges of values shown include the limits of those ranges.
[0034] An ultrasonic imaging device 100 according to a particular embodiment is described below in relation to figure 1.
[0035] The device 100 comprises at least one ultrasonic transducer circuit 102, a control circuit 104, and a data processing circuit 106. In the schematic example shown in Figure 1, the control circuit 104 is electrically coupled to the ultrasonic transducer circuit 102 and is thus capable of transmitting signals to the ultrasonic transducer circuit 102, commanding the emission of ultrasound for the measurements to be performed, and also capable of receiving electrical measurement signals transmitted by the ultrasonic transducer circuit 102. The control circuit 104 is also coupled to the data processing circuit 106 in order, in particular, to transmit to the data processing circuit 106 the measurement results obtained from the ultrasonic transducer circuit 102.
[0036] The circuit 102 can include a plurality of ultrasonic transducers allowing the acquisition of at least B24004PCT - Clustering an initial sequence of ultrasonic images. The ultrasonic transducers are arranged, for example, in a matrix or in another way suitable for the captures to be performed. The number of ultrasonic transducers in the circuit 102 may depend on the dimensions of the capture surface of the device 100.
[0037] In the described embodiment, device 100 is a medical imaging device designed to acquire images of blood flow. Alternatively, device 100 could be configured to perform other types of ultrasonic image acquisition, for example, for flow detection in pipes or any other application, such as industrial or domestic.
[0038] Circuit 102 is configured here to perform pulsed-wave Doppler (PWD) imaging. In this configuration, transducer circuit 102 emits a series of ultrasonic pulses. The responses obtained in this configuration do not correspond to a change in the frequency of the received wave, but rather to a pseudo-Doppler effect, that is, a change in the time interval between the received echoes relative to the time interval between the emitted waves. In the example device 100 described, pulsed-wave Doppler imaging is used to determine the spatial location of blood flow in biological tissues.
[0039] The ultrasonic transducer circuit 102 may, for example, include CMUT-type transducers (in English, "Capacitive Micromachined Ultrasonic Transducer" or PMUT type ("Piezoelectric Micromachined Ultrasonic Transducer" or other type. B24004PCT - Clustering
[0040] In the example embodiment described here, the control circuit 104 can be configured to provide the ultrasonic transducer circuit 102 with electrical excitation signals causing the ultrasonic transducer circuit 102 to emit ultrasonic waves, and to receive electrical response signals generated by the ultrasonic transducer circuit 102 under the effect of receiving reflected ultrasonic waves.
[0041] In the described embodiment, the data processing circuit 106 can be configured to analyze and process the electrical response signals received by the control circuit 104 and sent by the ultrasonic transducer circuit 102. The processing circuit 106 may include, for example, a microprocessor coupled with a memory to perform the processing of the received data.
[0042] The control circuit 104 and the data processing circuit 106 are configured to perform ultrasonic image processing by implementing the steps described below and represented in diagram form in Figure 2.
[0043] The example of a processing method described below concerns the processing of vascular ultrasound images in order to isolate images of at least one blood flow from the biological tissues in which the blood flow occurs and from the noise. However, this method could be applied to other applications such as the detection of flow in industrial pipes or pipelines.
[0044] In the example described, the captured ultrasound images of the initial sequence and the calculated images of the final sequence correspond to raster images, that is, two-dimensional images. Alternatively, these images could correspond to three-dimensional, or 3D, images. B24004PCT -Clustering for example constructed from several two-dimensional images.
[0045] In a first step 202, the device 100 can acquire at least one initial sequence of ultrasonic images. For example, this acquisition may include sending an acquisition command, or a sequence of instructions, to the control circuit 104 for the successive acquisition of images, then the emission of a series of ultrasonic signals in the form of pulses by the transducer circuit 102, then the reception of the echoes by the transducer circuit 102 and the processing of the responses obtained to obtain the initial sequence of ultrasonic images. The processing of the responses obtained to obtain the initial sequence of ultrasonic images is not described in detail, as this processing may involve various steps such as filtering, envelope detection, logarithmic compression, etc.The data relating to the acquired images is for example stored in a memory of device 100, or in an external memory of device 100 for example connected to device 100 by a communication link.
[0046] In a subsequent step 204, the initial sequence of ultrasonic images is subjected to singular value decomposition, or SVD, calculating spatial and / or temporal eigenvectors for each of the images in the initial sequence. In the described embodiment, the singular value decomposition is implemented such that spatial and temporal eigenvectors are calculated for each of the ultrasonic images in the initial sequence.
[0047] In a particular example, the initial sequence comprises Ni ultrasonic images, each containing Np pixels, B24004PCT - Clustering and singular value decomposition computes Ni unit spatial eigenvectors of Np values each, and Ni time eigenvectors of Ni values each, with Ni and Np corresponding to non-nuisance positive integers. In such an example, the Ni eigenvalues of the diagonalization of these vectors represent the energies associated with these vectors.
[0048] Such an SVD step may involve the implementation of an algorithm as described in the paper "Spatiotemporal Clutter Filtering of Ultrafast Ultrasound Data Highly Increases Doppler and fUltrasound Sensitivity" by C. Demené et al., IEEE Transactions on Medical Imaging, vol. 34, no. 11, November 2015.
[0049] In a subsequent step 206, a partitioning, or clustering, of the previously calculated spatial and / or temporal eigenvectors is implemented, distributing said vectors into several groups of spatial and / or temporal eigenvectors based on spatial and / or temporal similarities, respectively. This partitioning allows the spatial and / or temporal eigenvectors to be separated and distributed into a defined number of sets, based on the spatial and / or temporal similarities between the vector values and not on their energy values. The spatial and / or temporal similarities can, in particular, represent correlations between pixel values in the initial sequence of ultrasonic images.The spatial and / or temporal similarities between the values of the spatial eigenvectors and / or the temporal eigenvectors may depend on the correlations between pixel values of the images in the initial sequence of ultrasonic images. B24004PCT - Clustering
[0050] According to one particular implementation, only spatial eigenvectors can be used for partitioning, with these spatial eigenvectors being distributed into groups of spatial eigenvectors based on spatial similarities between the vectors. According to a first variant, only temporal eigenvectors can be used for partitioning, with these temporal eigenvectors being distributed into groups of temporal eigenvectors based on temporal similarities between the vectors.According to a second variant, spatial eigenvectors and temporal eigenvectors can be used for the implementation of partitioning, with spatial eigenvectors being distributed into groups of spatial eigenvectors according to spatial similarities between them, and temporal eigenvectors being distributed into groups of temporal eigenvectors according to temporal similarities between them.
[0051] In the example of a processing method described, which concerns the processing of vascular ultrasound images with the aim of isolating images of at least one blood flow from biological tissues and noise, partitioning can be implemented on the spatial eigenvectors in order to distribute them, for example:
[0052] - in two groups of spatial eigenvectors, one representing the biological tissues present in the images and the other representing the blood flow in the veins and / or arteries present in the images, or
[0053] - in three groups of spatial eigenvectors, a first group being representative of the biological tissues present in the images, a second group being representative of the blood flows in the veins and / or arteries present in the images, and a third group being representative of the noise present in the images, or B24004PCT - Clustering
[0054] - in three groups of spatial eigenvectors, a first group being representative of the biological tissues present in the images, a second group being representative of the rapid blood flows in the veins and / or arteries present in the images, and a third group being representative of the slow blood flows in the blood vessels present in the images.
[0055] As an example, step 206 of partitioning can be implemented using a "clustering" function of a computer calculation software, such as the "Clusterdata" function of the Matlab software (registered trademark) marketed by the company Mathworks (registered trademark).
[0056] Partitioning step 206 is implemented here independently of the order in which the spatial and / or temporal eigenvectors were obtained during the singular value decomposition of the images. Thus, the resulting groups of spatial and / or temporal eigenvectors do not necessarily contain neighboring eigenvectors. Partitioning step 206 is implemented here without regard to the initial order of the eigenvectors, as this step goes beyond simply determining thresholds between groups of neighboring eigenvectors.
[0057] A step 208 is then performed to calculate at least one final image sequence by concatenating the spatial and / or temporal eigenvectors of one of the vector groups. Considering the examples described previously, this step 208 can output either a final image sequence representing blood flow, or two final image sequences, one representing fast blood flow and the other representing slow blood flow.
[0058] As an example, figures 3 to 5 represent final 3D images obtained by implementing the process B24004PCT - Clustering previously described such that partitioning is performed by dividing the spatial eigenvectors into three groups. The first image shown in Figure 3 is calculated from the spatial eigenvectors that have been grouped into the first of the three groups, and in which the smallest blood vessels (with the lowest blood flow rates) are visible. The second image shown in Figure 4 is calculated from the spatial eigenvectors that have been grouped into the second of the three groups, and in which an arteriole with a high blood flow rate is visible. The third image shown in Figure 5 is calculated from the spatial eigenvectors that have been grouped into the third group, and in which the tissues are visible.It is possible to calculate only the image(s) of interest to the user, i.e. the first and second images in the example above.
[0059] For comparison, if a spatial eigenvector distribution were implemented based on the energy of these eigenvectors, those relating to the arteriole and those relating to smaller blood vessels would have been distributed in the same group of eigenvectors, which would have led to obtaining a single sequence of images in which the arteriole and the blood vessels would be visible on each of them.
[0060] As another example, Figure 6 shows final 3D images obtained by implementing the previously described process, where partitioning is performed by distributing the spatial eigenvectors into three groups. These images are of the "Maximum Intensity Projection" (MIP) type. Views a) to c) correspond to such images along a first axis, and views d) to f) correspond to the same data viewed along a second axis perpendicular to the first. B24004PCT - Clustering axis. Images in views a) and d) are calculated from spatial eigenvectors grouped into the first of three groups, in which vertical arterioles and venules, as well as a larger vessel at greater depth (~5 mm in this example), are visible. Images in views b) and e) are calculated from spatial eigenvectors grouped into the second of three groups, in which superficial horizontal vessels are visible. Images in views c) and f) are calculated from spatial eigenvectors grouped into the third group, in which tissues are visible. It is possible to calculate only the image(s) of interest to the user, i.e., the first and second images in the example above.
[0061] For comparison, using a conventional method to determine an optimal threshold leads to a single image containing both types of flow visible in views a), b), d), and e) of Figure 6. However, in such a single image, it is more difficult to distinguish certain vessels, especially the deep one and the finest vessels.
[0062] In the various examples above, it is possible that the final image sequence retained at the end of the process corresponds to a single sequence of blood flow images, or to two distinct image sequences, one representing slow blood flow and the other representing fast blood flow. Furthermore, compared to techniques based on eigenvector energies, eigenvector partitioning based on spatial and / or temporal similarities between eigenvectors allows for better isolation of tissue-related information. B24004PCT - Biological clustering and noise in relation to the information sought concerning blood flow.
[0063] In general, the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is, for example, between 2 and 4. The number of eigenvector groups is chosen in particular according to the desired classification granularity for the eigenvectors determined by the SVD.
[0064] As an example, the process may involve implementing, after the computational step 208, a step 210 that implements a shape recognition algorithm within the images of the final sequence obtained, in order to automatically identify specific shapes. For example, it is possible to perform tubular structure recognition to locate one or more blood vessels and / or arterioles and / or arteries. Such recognition may involve applying filters to the images obtained from the final sequence, for example, Frangi or Jerman filters when the recognition concerns tubular structures. For example, when a Jerman filter is applied, it is possible to define an index (for example, the proportion of pixels whose amplitude is greater than an arbitrary threshold) whose value allows the selection of pixel groups describing vessels.
[0065] Figure 7 represents the same images as those previously described in relation to Figure 6, but with a Jerman filter applied to them.
[0066] In the described method, the energies of the eigenvectors are not considered, and only the spatial and / or temporal similarities between the eigenvectors are evaluated, independently of their energy. This method allows, for example, the reconstruction of images of blood vessels. B24004PCT -Clustering without the presence of static elements such as artifacts, interface echoes, or biological tissue movement.
[0067] Advantageously, the ultrasound image processing method and the device 100 can be used for the purpose of detecting blood vessels, for example in the field of medicine or ultrasound.
[0068] Various embodiments and variations have been described. A person skilled in the art will understand that some features of these various embodiments and variations could be combined, and other variations will become apparent to a person skilled in the art.
[0069] Finally, the practical implementation of the described methods and variants is within the reach of the person in the trade, based on the functional indications given above.
Claims
B24004PCT - Clustering DEMANDS 1. A method for processing ultrasonic images comprising at least: - decomposition ( 204 ) into singular values of each image of at least one initial sequence of ultrasonic images , calculating spatial eigenvectors and / or temporal eigenvectors of each of the images; - partitioning ( 206 ) of said spatial eigenvectors and / or of said temporal eigenvectors, distributing said spatial eigenvectors and / or of said temporal eigenvectors into several groups of spatial eigenvectors and / or several groups of temporal eigenvectors on the basis respectively of spatial and / or temporal similarities and independently of the order of obtaining the spatial eigenvectors and / or of the temporal eigenvectors during the singular value decomposition of the images; - calculation ( 208 ) of at least one final sequence of images by concatenation of said spatial eigenvectors and / or said temporal eigenvectors of one of the groups of vectors.
2. A method for processing ultrasonic images according to claim 1, wherein: - the initial sequence comprises Ni ultrasonic images, each comprising Np pixels; B24004PCT - Clustering - the singular value decomposition (204) calculates Ni unitary spatial eigenvectors of Np values each, and Ni temporal eigenvectors of Ni values each; with Ni and Np corresponding to non-null positive integers.
3. Method for processing ultrasonic images according to any one of the preceding claims, wherein the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is between 2 and 4.
4. A method for processing ultrasonic images according to any one of the preceding claims, wherein the spatial and / or temporal similarities are representative of correlations between pixel values of the images from the initial sequence of ultrasonic images.
5. Method for processing ultrasonic images according to any one of the preceding claims, further comprising, before implementation of the decomposition (204) into singular values, at least one acquisition (202) of the initial sequence of ultrasonic images.
6. Method for processing ultrasound images according to any one of the preceding claims, wherein the initial sequence of ultrasound images corresponds to a sequence of vascular ultrasound images. B24004PCT - Clustering 7. Method for processing ultrasonic images according to claim 6, wherein the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is equal to 2 or 3 and such that the final image sequence corresponds to a blood flow image sequence, or wherein the number of spatial eigenvector groups and / or the number of temporal eigenvector groups is equal to 3 and such that a first final image sequence corresponds to an image sequence of a first blood flow and a second final image sequence corresponds to an image sequence of a second blood flow faster than the first blood flow.
8. Method for processing ultrasonic images according to any one of the preceding claims, wherein the ultrasonic images of the initial sequence and / or the final sequence are in two dimensions or in three dimensions.
9. Method for processing ultrasonic images according to any one of the preceding claims, further comprising, after the calculation (208) of the final sequence of images by concatenation of the spatial eigenvectors and / or the temporal eigenvectors of one of the groups of vectors, the implementation of a recognition algorithm (210), within the images of the final sequence, of geometric shapes.
10. A method for processing ultrasonic images according to claim 9, wherein the recognition algorithm B24004PCT - Clustering (210) of geometric shapes corresponds to a tubular shape recognition algorithm.
11. Ultrasonic imaging device (100) configured to implement an ultrasonic image processing method according to any one of the preceding claims.
12. Ultrasonic imaging device (100) according to claim 11, comprising an ultrasonic transducer circuit (102) configured to perform an acquisition of the initial sequence of ultrasonic images by pulsed Doppler imaging.
13. Ultrasonic imaging device (100) according to claim 12, further comprising: - a data processing circuit (106) configured to implement the ultrasonic image processing method; - a control circuit (104) electrically coupled to the ultrasonic transducer circuit (102) and the data processing circuit (106), configured to transmit ultrasonic emission control signals to the ultrasonic transducer circuit (102), to receive electrical measurement signals transmitted by the ultrasonic transducer circuit (102) and to transmit electrical measurement signals to the data processing circuit (106).