Imaging method, apparatus and associated computer program product

Abstract

The present invention relates to an imaging method, an imaging apparatus, and an associated computer program product for creating a three-dimensional image by microwave imaging of the brain of a patient using target detection in the brain to reduce the imaged region in subsequent scans in order to enable real-time monitoring of the history of the target in the patient's brain.

Description

Imaging method, apparatus and associated computer program product

[0001] The present invention relates to the technical field of medical imaging, and relates more particularly to an imaging method, an apparatus and an associated computer program product.

[0002] Brain diseases are the subject of intense research, both from the point of view of their treatment and their diagnosis.

[0003] Once diagnosed, some brain conditions require monitoring to prevent more serious conditions in patients. Thus, clinical study results suggest that in patients with primary intracerebral hemorrhage, the risk of rebleeding is not negligible: 24% of patients experienced one or more episodes of rebleeding during a mean follow-up period of 84.1 months. This risk appears to be highest during the first year following the first hemorrhage. The frequency of a second hemorrhage was higher in those whose blood pressure was not controlled, according to these same studies. In fact, the higher the patient's blood pressure, the greater their risk of recurrence.

[0004] It is therefore important to be able to find modalities for monitoring the patient's condition, either on a periodic model or on a one-off model for patients for whom a risk of imminent stroke has been determined. These modalities can be anatomical, biological, physiological, but essentially call upon cerebral imaging, with the problem that cerebral imaging, although it can be effective, requires expensive equipment, which is difficult to transport and requires calculations which are difficult to reconcile with a rapid diagnosis.

[0005] In approaches that focus on monitoring a condition, e.g., stroke progression, Alzheimer's disease, epilepsy, Parkinson's disease (PD), and Tourette's syndrome, signals must be based on anatomical and physiological information extracted during tissue scanning and be comparatively selected to improve the extraction speed of the produced images in terms of focusing on changes in target position and structure. Current imaging methods, such as magnetic resonance imaging (MRI) and computed tomography (CT), are thus difficult to apply.Furthermore, functional MRI (fMRI) and electroencephalogram (EEG), which are currently the most widely used techniques because they can provide excellent detailed anatomical and physiological information related to the monitoring of brain disorders, have disadvantages: the temporal resolution of conventional fMRI is rather low and takes a few seconds to reach its peak, while its spatial resolution is excellent, in the order of a square millimeter, while EEG has a temporal resolution in the order of a millisecond, but its spatial resolution is rather low and covers a few square centimeters.

[0006] In support of these imaging methods, computational brain imaging is one of the largest and most challenging research areas in recent years. Due to the complex nature of brain diseases, various approaches have been explored over the past few decades, each with its advantages and disadvantages. Microwave brain imaging based on the electromagnetic approach has attracted considerable attention in recent years due to its advantages, such as non-invasiveness, non-contact, low cost, and portability. Extracting brain information from these systems still presents many challenges. Various processing methods have been proposed to increase the capacity of these devices, as well as the usefulness of the extracted images for detecting and diagnosing brain diseases, including strokes.These conventional approaches require a large number of antennas to collect the scattered inverse field and thus solve the inverse scattering problem, resulting in high processing time.

[0007] The invention aims to enable continuous monitoring of strokes, applied to monitoring bleeding and blood clot proliferation in stroke patients, using an emerging brain imaging system based on an electromagnetic approach, and enables an ultra-fast and super-resolution monitoring methodology. In this regard, unlike other conventional static microwave imaging systems that can be considered as performing blind scanning, the dynamic microwave imaging system based on a multi-static array according to the invention has great potential to provide useful tools for monitoring strokes.The main objective is to develop ultra-fast destructive resolution methods to pave the way for cerebrovascular monitoring by performing closed-loop cognitive scans based on a common algorithm / design.

[0008] The hypothesis is that, unlike differential imaging where time-varying activities are detected by subtracting static information, closed-loop cognitive functional imaging is modulated in real time by feedback from backscatter recordings of intelligent irradiation which is faster, and can extract more information. To achieve this, according to the dynamic changes in brain activities, the transmitted waveforms, and thus the activated structures of subnetworks and focusing networks, will be cognitively modified and continuously adapted to the changing targets. The proposed radical vision, based on cognitive scanning (CS), uses an information-based approach for the tested scenario.The phenomenon behind this idea is that for monitoring and functional imaging, it is not necessary to detect the entire brain volume, but only to save / exploit data that contain relevant information about a predefined set of brain activities. To transform the problem into an informational problem, it is necessary to obtain the basic parameters of knowledge-based information synthesis, one of which is the existence of prior knowledge or a library to create a knowledge development system and online decision autonomy. The main argument for the superiority of cognitive scanning for functional brain imaging is that it is more flexible for unknown patterns of brain activities than multi-static imaging and can therefore be proposed more widely as a monitoring option with fewer contraindications.By identifying an activated region as a target of interest, the microwave imaging scenario is transformed into a microwave detection scenario.

[0009] The present invention therefore relates to a method for three-dimensional imaging of the brain by microwave imaging, characterized in that it comprises the following steps:

[0010] a - producing on a patient on whose head is placed a network of microwave antennas controlled by a switching network connected to a controllable signal transmitter-receiver device, a three-dimensional global image of the brain by microwave imaging using all the antennas of the microwave antenna network;

[0011] b - detecting at least one target in the global image of the brain produced in step a;

[0012] c - calculating a position of the at least one target in the global image of the brain produced in step a;

[0013] d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;

[0014] e - fusing the global image and the local processing image into a three-dimensional global intermediate image;

[0015] f - detect at least one target in the global intermediate image;

[0016] g - calculate a position of the at least one target in the global intermediate image;

[0017] h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;

[0018] i – merge the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;

[0019] j - repeat steps f to i a predetermined number of times.

[0020] Advantageously, the antenna array is formed on a helmet, the antennas being uniformly distributed on the helmet in a hemispherical manner. Advantageously, the array comprises 24 antennas, although the invention is not limited in this respect.

[0021] Antennas are transmitting and receiving antennas, used to transmit and receive microwave signals.

[0022] Thus, thanks to the invention, due to the reduced number of antennas and / or samples when producing a local image due to the location of the target, fewer calculations are necessary and therefore the image is obtained more quickly, allowing for more real-time tracking. Since the global image is not recalculated at each iteration, we are therefore only interested in tracking and imaging the target, which reduces the acquisition and calculation of the image. However, at each iteration, a target is searched for in the global image, which ensures that no target is missed, particularly in the context of functional imaging where we wish to understand the evolution of the size, location and nature of the target depending on the patient's condition, even if the entire image is not recalculated at each iteration.The fact that the system includes all antennas, but that these are only activated depending on the target location allows for great flexibility, reduced computational complexity and high target detection efficiency. In addition, less powerful computing equipment is required compared to the state of the art. Finally, the image can be taken by a simple helmet equipped with microwave antennas connected to a computing device, allowing portability that is not possible in current methods and systems.

[0023] Advantageously, for better efficiency, we use both a subgroup of the antenna array and a reduction in the number of samples, which reduces the computational load.

[0024] The invention also relates to a device for hyperfrequency brain imaging, characterized in that it comprises a headset equipped with a hyperfrequency antenna array, a switching network controlling the hyperfrequency antenna array, a vector network analyzer connected to the switching network, a processing unit connected to the switching network and to the vector network analyzer and controlling the switching network and the vector network analyzer, a human-machine interface connected to the processing unit, the processing unit comprising calculation means and memory for carrying out the following steps:

[0025] a - produce a three-dimensional global image of the brain by microwave imaging using all the antennas in the microwave antenna network;

[0026] b - detecting at least one target in the global image of the brain produced in step a;

[0027] c - calculating a position of the at least one target in the global image of the brain produced in step a;

[0028] d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;

[0029] e - fusing the global image and the local processing image into a three-dimensional global intermediate image;

[0030] f - detect at least one target in the global intermediate image;

[0031] g - calculate a position of the at least one target in the global intermediate image;

[0032] h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;

[0033] i – merge the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;

[0034] j - repeat steps f to i a predetermined number of times.

[0035] The antennas are preferably arranged on the helmet in a hemispherical manner, distributed evenly.

[0036] The processing unit enables the transmission of signals to the headset and the reception of signals from the headset.

[0037] According to one embodiment, the antenna array comprises 24 antennas, preferably butterfly antenna type antennas.

[0038] Advantageously, the switching network is a 2*24 switching matrix in which the 24 ports are connected to the antennas and the two ports are connected to the vector network analyzer.

[0039] The invention also relates to a computer program product comprising instructions which, when executed by a computer, carry out the following steps:

[0040] a - produce a three-dimensional global image of the brain by microwave imaging using all the antennas in the microwave antenna network;

[0041] b - detecting at least one target in the global image of the brain produced in step a;

[0042] c - calculating a position of the at least one target in the global image of the brain produced in step a;

[0043] d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;

[0044] e - fusing the global image and the local processing image into a three-dimensional global intermediate image;

[0045] f - detect at least one target in the global intermediate image;

[0046] g - calculate a position of the at least one target in the global intermediate image;

[0047] h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;

[0048] i – merge the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;

[0049] j - repeat steps f to i a predetermined number of times.

[0050] According to one embodiment, each step of producing a three-dimensional image by microwave imaging comprises collecting diffusion parameter data representing microwaves diffused by the patient's brain in a diffusion tensor, generating differential diffusion parameter data by an adaptive connectome calibration method to remove spurious signals, and processing the differential diffusion parameter data by a confocal image reconstruction method to obtain a three-dimensional image.

[0051] The adaptive connectome calibration method consists of calculating the geometric connectome between the hemispherical antenna array and the signals passing through the patient's brain, the term connectome being defined in this application as the set of geometric connections / links between the antennas and the signals. Depending on the symmetry of the medium traversed by the microwave signals, it is possible to categorize the signals into differential pairs that pass through symmetrical paths. By performing a subtraction between these symmetrical signals, all non-target-related information is removed. In practice, the signals are grouped using a cross-correlation matrix, in which the signals of the channels that follow the same path through the patient's brain and have a high correlation value are classified into identical groups.The differential scattering parameter data are obtained by subtracting the signals of each group from each other. In subsequent scans, where the signals whose differential pair has been removed due to the abandonment of a certain number of antennas in the process of optimizing the antenna layout, an adaptive mode is used to determine the differential pair: the correlation of each signal is calculated with the rest of the signals, and the signal that gives the largest correlation value is selected as the differential pair.

[0052] The confocal image reconstruction method uses delayed-multiply-sum (DMAS) beamforming by processing differential scattering parameter data. The maximum value of coherently focused energy in the reconstructed image refers to the brain region of functional interest, for example, a stroke region. The location of these areas is considered the position of a possible target. Such a technique is described, for example, in the following publications:

[0053] - Ahadi, M., Isa, M., Saripan, MI and Hasan, WZW (2015), Three dimensions localization of tumors in confocal microwave imaging for breast cancer detection, Microw. Opt. Technol. Lett., 57: 2917-2929;

[0054] - Salvador, Sara M., and Giuseppe Vecchi, Experimental tests of microwave breast cancer detection on phantoms, IEEE Transactions on Antennas and Propagation 57, no. 6 (2009): 1705-1712;

[0055] - Babarinde, OJ, Jamlos, MF, Soh, PJ, Schreurs, DP and Beyer, A., Microwave imaging technique for lung tumor detection, 2016 German Microwave Conference (GeMiC), 2016, pp. 100-103.

[0056] According to one embodiment, the at least one target is detected by calculating the signal to clutter ratio (SCR) and the signal to mean ratio (SMR) in two-dimensional planes of the three-dimensional image, the at least one target being detected on the surfaces of each two-dimensional plane on which the values ​​of the signal to clutter ratio and the signal to mean ratio are simultaneously maximum. Such a technique is for example described in the following publications:

[0057] - Reimer, T., Solis-Nepote, M. and Pistorius, S., 2020, The application of an iterative structure to the delay-and-sum and the delay-multiply-and-sum beamformers in breast microwave imaging, Diagnostics, 17 / 06 / 2022, 10(6), p.411;

[0058] - Babarinde, OJ, Jamlos, MF, Soh, PJ, Schreurs, DP and Beyer, A., Microwave imaging technique for lung tumor detection, 2016 German Microwave Conference (GeMiC), 2016, pp. 100-103.

[0059] According to one embodiment, the position of the at least one target in the three-dimensional image is calculated as the position of the surfaces of each two-dimensional plane on which the values ​​of the signal-to-interference ratio and the signal-to-average ratio are simultaneously maximum. The probability that the target is detected is thus maximum.

[0060] Thus, the location of the target region is approximated by superimposing the quantitative metric maps such as the maximum value for signal-to-interference ratio (SCR) and signal-to-average ratio (SMR) in a two-dimensional plane, and the spatial dimensions of the target region correspond to the selected SCR and SMR windows.

[0061] According to one embodiment, the subgroup of the microwave antenna array associated with the position of the at least one target is constituted by the antennas having the shortest distance to the position of the at least one target.

[0062] According to one embodiment, compressive sampling is applied to the microwave signals emitted by the antennas of the antenna array. The computing power to be implemented is thus reduced. Compressive sampling is advantageously implemented by a convex optimization method of the space L 1 (which in the present application designates the space of functions with values ​​in ℝ whose absolute value (or the space of functions with values ​​in ℂ whose module) is integrable in the sense of Lebesgue).

[0063] According to one embodiment, each step of merging two three-dimensional images consists of replacing, in the global image, the part corresponding to the local image, with the local image to obtain the merged image.

[0064] Since the position of the target is known, we can know the region of the global image corresponding to the target, and replace this region in the global image with the local image of the target.

[0065] Only additional information obtained during processing is added to the overall 3D image.

[0066] The predetermined number of times steps f to i are repeated depends on the choice of the operator using the apparatus according to the invention. The predetermined number of times may in particular be correlated to a duration, in particular a duration of patient monitoring, or be correlated to a lack of evolution of a target over time (imaging by the apparatus is stopped if the target no longer evolves after a certain duration) or to an overly rapid evolution of the target (significant spatial development of the target over a predetermined duration).

[0067] After step j, the image obtained is an image of the patient's brain on which the target is positioned in space. An evolution of the target over time is also obtained, from step a to step j by storing and comparing the images.

[0068] The invention therefore uses an intelligent cognitive scanning program. The imaging apparatus of the invention uses a dynamic intelligent structure to create high-resolution images in the shortest possible time. The cognitive scanning paradigm proposed in this invention is the result of a combination of information theory and electromagnetic theory. In the dynamic multi-scan mode, feedback is used to augment the input data for subsequent scans. The selection of the antenna array arrangement that determines the desired signals in the processing stage depends on the information from the previous scan and is selected so as to extract new information in the next scan. The brain is irradiated by an antenna array that propagate the electromagnetic wave into the head. These waves are then converted into measured reflected signals.After passing through the receiver, they are converted into data depending on the type of scan. From this path, information is extracted from the captured data.

[0069] Unlike conventional multi-static microwave imaging systems, the proposed cognitive scanning also provides an additional information extraction / integration process. The main objective presented here is to create closed-loop cognitive scanning as an alternative to differential imaging that is faster and allows for more information extraction. Leveraging compression measurement and cognitive sampling techniques, the information flow and output of the proposed device create highly innovative decision-making schemes for ultrafast dynamic microwave imaging systems. Based on this capability, the simultaneous design of a cognitive scanning-based multi-scan algorithm architecture, which includes antenna selection and sub-Nyquist sampling, is implemented.Compression-based sampling enables ultra-fast sampling and complete reconstruction of certain classes of unlimited signals. In information-based scanning, temporal and spatial changes in the monitoring scenario are encountered. These temporal and spatial changes must be detected and tracked intelligently. In addition, only variant samples must be extracted per scan. The extracted data also adds new information to the Knowledge Growing System (KGS) library to estimate the future state of the Target of Interest (TOI). To this end, the invention combines algorithms with architectures for cognitive scanning, data acquisition, and information integration required for very fast tracking to optimize performance.

[0070] The main argument for the superiority of cognitive scanning for static brain imaging is that it is faster and more accurate for multi-static brain imaging and can therefore be more widely offered as a monitoring option with fewer contraindications. By identifying the target area as the target of interest, the microwave imaging scenario is transformed into a detection scenario.

[0071] To better illustrate the object of the present invention, a preferred embodiment will be described below, in conjunction with the accompanying drawings.

[0072] On these drawings:

[0073] is a block diagram of the microwave imaging apparatus according to the present invention;

[0074] is an exemplary representation of a headset used by the device of the;

[0075] is a flowchart of the imaging method implemented by the microwave imaging apparatus according to the present invention;

[0076] represents a two-dimensional imaging domain of the human head;

[0077] represents a first step of image reconstruction by a confocal image reconstruction method;

[0078] represents a second step of image reconstruction by a confocal image reconstruction method;

[0079] represents a third step of image reconstruction by a confocal image reconstruction method;

[0080] represents a fourth step of image reconstruction by a confocal image reconstruction method;

[0081] represents a 2D image extracted according to the invention; and

[0082] represents the 2D image obtained from the image of the based on the SCR metric.

[0083] Such a method is for example described in the publication O'Loughlin, D., Elahi, MA, Lavoie, BR, Fear, EC and O'Halloran, M., 2021, Assessing Patient-Specific Microwave Breast Imaging in Clinical Case Studies, Sensors, 21(23), p.8048.

[0084] Referring to the, it can be seen that a microwave imaging apparatus 1 according to the present invention has been schematically represented.

[0085] The apparatus 1 comprises a processing unit 2, comprising calculation means of the microprocessor, microcontroller, digital signal processor (DSP), processor, field programmable gate array (FPGA) or application-specific integrated circuit (ASIC) type, associated with memory (of the ROM, EEPROM, RAM, flash memory type), as well as input / output ports and / or wired or wireless communication means with the other elements of the apparatus 1, to implement the steps described below of hyperfrequency imaging of the brain and to transmit signals to the other elements of the apparatus 1 and to receive signals from the other elements of the apparatus 1.

[0086] The apparatus 1 further comprises a switching network 3 connected to a headset 4 carrying a plurality of antennas 5, the antennas 5 being controlled by the switching network 3 which also collects the signals coming from the antennas 5, a vector network analyzer 6, connected to the switching network 3 and processing the signals coming from the switching network 3 to send them to the processing unit 2, and a human-machine interface 7, comprising a screen and input means of the keyboard and / or mouse type.

[0087] The processing unit 2 and the human-machine interface 7 can for example be implemented using a conventional desktop computer or a tablet or even a smartphone and is configured to implement the steps described below.

[0088] If we refer to the, we can see that a helmet 4 is shown there equipped with its antennas 5 according to a preferred embodiment.

[0089] The helmet 4 is hemispherical in shape, the antennas 5 being 24 in number in this embodiment by way of example and not limitation, distributed uniformly on the surface of the helmet 4. The numbers on the antennas are used to reference the antennas 5 to control them. The number assigned to each antenna 5 on the is of course illustrative and not limiting. The invention is not limited to the number of 24 antennas indicated in this example of implementation of the invention, also illustrative and not limiting.

[0090] The antennas 5 are preferably butterfly antennas, and advantageously have a bandwidth of approximately 3 GHz.

[0091] The processing unit 2 is configured in the microwave imaging device 1 to implement the following steps, shown schematically in, when the headset 4 is placed on the head of a patient:

[0092] a - producing a three-dimensional global image of the brain by microwave imaging using all of the antennas 5 of the microwave antenna network 4;

[0093] b - detecting at least one target in the global image of the brain produced in step a;

[0094] c - calculating a position of the at least one target in the global image of the brain produced in step a;

[0095] d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array 4 controlled by the switching network 3 associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas 5;

[0096] e - fusing the global image and the local processing image into a three-dimensional global intermediate image;

[0097] f - detect at least one target in the global intermediate image;

[0098] g - calculate a position of the at least one target in the global intermediate image;

[0099] h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array 4 controlled by the switching network 3 associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas 5;

[0100] i – merge the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;

[0101] j - repeat steps f to i a predetermined number of times.

[0102] After step j, the image obtained is an image of the patient's brain on which the target is positioned in space. An evolution of the target over time is also obtained, from step a to step j.

[0103] The SCR and SMR metrics described below are used to detect the target in steps b and f.

[0104] The objective is to increase the speed and accuracy of image reconstruction by reducing the complexity of the hardware to a minimum, provided that most of the information can be extracted in the software of the processing unit 2. In the present invention, a full brain scan is performed and at each scan only the necessary samples are selected in order to reduce the processing speed in the software of the processing unit 2.

[0105] A complete scan of the patient's brain is therefore first carried out.

[0106] A full scan related diffusion tensor is created in the processing unit 2, the tensor being a matrix representing the signals transmitted and received by each antenna 5 of the headset 4.

[0107] By applying the adaptive connectome calibration method detailed below to the diffusion tensor, the antennas 5 required for scanning are determined based on the information about the target area.

[0108] A reduction in the number of samples (compressive sampling) is applied to the result of the adaptive connectome calibration process applied to the diffusion tensor to obtain a sparse matrix from the diffusion tensor. An improved sparse matrix is ​​then created from the full scan.

[0109] Compressive sampling is advantageously implemented on the sparse matrix enhanced by a convex optimization method of L1 space.

[0110] To generate sparse signals, assume that the length of a signal x is M×1. If the signal is sparse with the factor K (K< <M), il peut être représenté comme x=ψs, où ψ est une matrice de dictionnaire orthogonale et complète. La matrice de dictionnaire s’utilise comme un coefficient dans la méthode détection compressive. La détection compressive, qui est considérée comme une reconstruction inverse du signal original à partir de signaux épars, utilise l'opération linéaire de la matrice de mesure (Φ) et du signal d'entrée comme suit : y=ϕx. Cette théorie est basée sur l'hypothèse que la matrice de mesure Φ est incohérente avec la matrice de dictionnaire ψ. Par cette condition, le vecteur-matrice, s, peut être reconstruit à partir de G=O(K*log Nt). Pour résoudre cette équation, il faut résoudre le problème d'optimisation convexe suivant .

[0111]

[0112] In the present invention, a convex optimization based on the L1 norm is proposed to solve this equation. Furthermore, the matrix operations in compressed sensing can be written as: y=∅ψs=As, where ∅ is a random Gaussian measurement matrix and ψ is the discrete cosine transform (DCT) matrix: A=∅ψ. The compressive method is first applied to generate the sparse signals. Then, from these sparse signals, images are obtained by the confocal image reconstruction method to compare the new SCR. If the SCR is still high, it means the sparse signal is correct. Otherwise, the sparsity factor should be decreased.

[0113] Optimal sparsity is applied so that fewer non-zero elements are used in the range where there is no signal change. In general, from a temporal point of view, the initial part of the signal is often omitted because it has high values ​​due to reflection from the surface. However, useful information can be extracted from these signals to be used in the estimation of certain parameters, such as effective permittivity, etc.

[0114] After collecting the received signals, all signals are reduced in size using sparse signals. Since most natural signals in one or more domains (time, frequency, wavelets, etc.) have a sparse display, this means that the information they contain can be used using a small number of coefficients in a special domain expressed. For example, although images appear very dense or rich in space and with a lot of information, in the frequency domain, they have compact and so-called sparse information. Thus, most of their frequency coefficients are zero or close to zero. The same is true for many other types of signals. Therefore, a very large signal of length N can only be represented by the coefficient K, which is K << M, this signal is called a sparse signal of order K.

[0115] From the sparse matrix, an image is created using the confocal image reconstruction method.

[0116] Meanwhile, in order to extract the anatomical information, existing images related to the patient's head scan, such as CT and / or MRI, are transmitted to the device 1, preferably wirelessly.

[0117] These existing images are then processed and segmented by a machine learning machine to process geometric and position information of the target in the brain image.

[0118] The existing images are then merged with the image created by the confocal image reconstruction process and a complete image of the brain is created.

[0119] Since this scenario is intended for brain monitoring, it has a multi-scan mode to reveal developments / changes in the target.

[0120] In radar imaging, after receiving the return signals using a data collection method in the form of multistatic scanning, a diffusion tensor is created. This step, which is formed from hardware to software, converts the electromagnetic waves into complex data recorded in a tensor called the diffusion tensor. In this tensor, which is a matrix, depending on the useful information, the part below the diagonal is eliminated first, as well as the diagonal elements, which correspond to the signals reflected by each antenna, due to the much higher values ​​than the other signals. In general, before converting the signals into data and the data into information, a calibration must be performed in the hardware to ensure the accuracy of the received signals.Calibration includes eliminating coupling between antennas by adjusting their distance, creating a plane wave instead of a spherical wave by adjusting the distance from the measurement medium, and eliminating unwanted reflections by adding an absorber or metal backplate to suppress scattering behind the antenna. Once all steps have been taken to transfer the correct data from the hardware to the software, the software is calibrated.

[0121] Due to the reciprocity property of electromagnetic theory, it is not necessary to scan the entire 24 antenna × 24 antenna state. In other words, the information stored from the 3D simulation model includes the diagonal elements and upper triangular elements of the diffusion tensor. The diagonal elements are the antenna return losses. The other signals are the transmission losses between the different antenna pairs.

[0122] The main objective of the software part of the processing unit 2 is to reconstruct the precise image of the received signals. First, in order to map the information, a coordinate hemisphere containing the focal points must be created. To create this hemisphere, physical information such as the ambient radius, the ambient material, and the number of sampling points must be entered into the processing unit 2 program by means of the human-machine interface 7. The ambient radius determines the boundary of the image. The number of points can also be determined based on the signal bandwidth. Another important parameter is the material of the environment, which is defined based on the dielectric permittivity and electrical conductivity. The dielectric permittivity is more important due to changes in wave speed.In the next step of entering parameters into the program, the information of device 1 is entered such as the number of antennas 5, their location and the channels related to the signals, which show the relationship between the signals and the path between two respective antennas 5 in each signal.

[0123] After entering all the physical parameters, the first preprocessing section includes the algorithm for suppressing unwanted signals with in situ calibration. In radar brain imaging, since there are several unknown parameters to reconstruct the overall image, it must be extracted from the return signals. Thus, methods based on information theory can be very useful, both for increasing the processing speed and for the device to detect certain targets. In this invention, we propose the adaptive form of the in situ calibration algorithm which is based on the dynamic arrangement of the connectome (set of connections between antennas and signals) in the feedback processing path. In this method, signals are selected from the diffusion tensor based on geometric information of the imaged medium (brain) and wave propagation channels in the environment.To this end, we perform processing from different paths that lead to the extraction of more information. In general, all information is extracted from the diffusion tensor in addition to the physical information of the system to reconstruct the image.

[0124] The in situ adaptive connectome calibration method uses the symmetrical property of the right and left sides of the brain's elliptical structure.

[0125] All connections between antennas are identified.

[0126] The connections are then grouped by distance between a pair of antennas considered. Thus, a first group consists of the connections between adjacent antennas, a second group consists of the antennas separated by one antenna, a third group consists of the antennas separated by two antennas, etc., the last group being constituted by the antennas separated by the greatest number of antennas 5 possible on the headset 4. These groups represent signals which travel along symmetrical paths.

[0127] For the helmet 4 shown in the, the last group would therefore be made up of the antennas 5 separated by five antennas (for example the antennas 5 diametrically opposed on the helmet 4).

[0128] For each group of signals, the first signal is subtracted from the next, and the resulting signal is subtracted from the next up to the last signal in the group. This gives a differential signal for the group of signals whose characteristics not related to the target to be detected are removed.

[0129] The differential signal obtained for a group is then subtracted from the average signal value for the group.

[0130] These two steps allow the elimination of parasitic signals, in particular the background and skin effects, to retain only the useful information relating to the target.

[0131] Once this processing is done, the processing unit 2 performs an image reconstruction by a confocal image reconstruction method to obtain a three-dimensional image from the calibrated signals. Basically, the confocal image reconstruction method consists of coherently integrating the energy of each signal reflected at each focal point.

[0132] A two-dimensional imaging domain of the human head, as shown in, is considered. An antenna array where the antennas are placed at equal distances from each other around the head is used. The positions of each of the antennas, represented in spherical coordinates (in, r) correspond to Cartesian coordinates given by an = [xn, yn], where n is the number of the n-th antenna. The imaging area inside the head is represented by I, where the imaging points (focal points) inside are denoted by im = [xm, ym], where m is the number of the m-th point in the imaging area.

[0133] To ensure consistent signal integration, the effects of delays between focal points and antenna positions must be compensated. In this case, it is necessary to find the phase shift between each antenna and the other antennas. This delay time is equal to the direct distance between the transmitter and receiver divided by the wave speed in the propagation medium. The next step is to extract the target location from the reflected signals. For this, all values ​​are set to zero before their calculated delay. The signal propagation delay from the nth antenna in the array to the mth point in the imaging area, I, is calculated based on the following equation:

[0134]

[0135] where ε eff is the effective dielectric constant of the head. For the model studied, we calculate the value of ε effequal to 38, which corresponds to the average of the dielectric constant values ​​measured on artificial brains. In order to reconstruct an image, a focused beamforming algorithm such as the Delay and Sum (DAS) beamforming algorithm is implemented. For this, the first step is to identify the focal points to calculate the energy pattern of the signal reflected at these points, which, for the multi-static imaging algorithm, will be done by coherent integration of the signals. A Delay and Sum (DAS) beamforming algorithm creates a coherent integration of the energy of the signals at each focal point, by summing the phase-corrected signals with respect to each other, according to the following equation:

[0136]

[0137] Where An is the signal from the antenna at location n (focal point of n).

[0138] The person in the profession knows how to convert from Cartesian coordinates to spherical coordinates.

[0139] Figures 5A-5D illustrate the confocal image reconstruction process. It shows the focal points within the hemisphere with the size of the antenna distances.

[0140] The spatial accuracy between these points is about 3 mm. Then, by calculating the delay, these points are calculated from each pair of antennas corresponding to each signal, and the energy accumulation at each point is obtained. L shows a three-dimensional diagram of a confocal image with DMAS beamforming. L shows a 2D image in coronal view, and l finally shows a two-dimensional image containing the target.

[0141] A combined method based on machine learning has been proposed for segmentation and classification of computed tomography (CT scan) / MRI images. The main steps of the proposed method are presented below.

[0142] A noise removal method can be used to reduce the noise level in images.

[0143] Additionally, to reduce image dimensions, dimensionality reduction methods such as wavelet transform and Principal Components Analysis (PCA) can be used. Finally, K-means methods, fuzzy image segmentation method, and multiclass SVM (support vector machine) segmentation method can be used to classify abnormality types (strokes).

[0144] After creating the overall three-dimensional image based on microwave imaging thanks to the availability of the patient's MRI / CT image, these two images are combined in the post-processing program in processing unit 2 and, after extracting the necessary information, a decision-making is created for feedback to the switching network to determine the parameters of the next scan. Since this is a feedback loop method, the image must be displayed in several time frames.

[0145] In order to realize the ultra-fast processing technique, signal reduction and sampling reduction are applied at two levels.

[0146] The main question is which antennas should be selected to have the maximum information capacity. The elements of the diffusion tensor show all possible connections, and the goal is to select the necessary signals based on the connectome from the diffusion tensor. In cognitive scanning, it is not necessary to input all elements of the diffusion tensor into the image reconstruction algorithm. For example, among the different signals that pass through the same propagation channel, only one is selected and the others are eliminated to obtain a sparse matrix. This means that the lower triangular elements of the sparse matrix must be removed. Furthermore, due to the quasi-elliptical symmetry of the human head, the return signals from antennas facing each other can be used for in situ calibration.It should be noted that the return signals from each port are significantly different from the diagonal transmit signals, namely, the bi-static diagonal transmit signals are much weaker than the return signals in each monostatic port. This phenomenon is of considerable importance, as it governs key imaging metrics such as the correlation and contiguity of encoded information from the scene when different channels are scanned.

[0147] A quantitative way to analyze the information capacity (and thus the orthogonality of the spatio-temporal resolution) of the selected signals is to analyze the signal-to-interference ratio (SCR) and the signal-to-mean ratio (SMR) of the reconstructed images and the location of the target in each scan. According to the invention, an SCR map and an SMR map are used as a means of detection and of the target, in steps b and f. Then, depending on the maximum value of these criteria (SCR and SMR), the position of the target is revealed. After creating the image by the confocal image reconstruction method in the first complete scan, the information about the position of the target and other parameters such as SCR, SMR are extracted from the image of the current scan. Furthermore, the cross-correlation matrix of the diffusion tensor must be compared with the SCR and SMR maps.All this information is then adapted to determine the target position and the area occupied by the target and is presented with regard to the connectome as a decision factor to determine the optimal selection of signals. Then, by comparing this position and the cross-correlation matrix, the signals that have the main effect on the target position are selected by examining the connectome connection and compressive sensing, implemented by the processing unit 2, is applied to these selected signals for the generation of sparse signals. In this case, the next digitized image is made with the fastest possible mode and the least number of samples required and preserving the required information related to the detected target. Compressive sampling is advantageously implemented by a convex optimization method of the L1 space.

[0148] After extracting the information from the patient's brain scan images, at this stage, using the image obtained by the microwave imaging method, new useful information is extracted to match the target location with the information extracted from the scan images and provide the necessary information in the feedback phase to decide the second scan.

[0149] Due to the nature of microwave images, which are obtained by accumulating energy in focal points and present target-like points in the image or blurred targets, target position detection uses data-driven metrics. Useful metrics derived from this perspective include SCR and SMR. The region with the highest value of these two metrics is identified as the target region and compared to the target region obtained in the scan image, and as long as this comparison has the same response, instead of the entire diffusion tensor signals, selective signals are used for the next scan.

[0150] Due to the nature of microwave images, which are obtained by accumulating energy in focal points that have target-like points in the image or blurred targets, detecting the target position with artificial intelligence methods has a high error. Thus, to detect the target and extract its position from the image, techniques based on quantitative metrics are proposed. The useful metrics derived from this point of view are SCR and SMR. For this purpose, the image region has been divided into several 2D windows as shown in. The central location of the windows that has the highest value of these two metrics is identified as the target region.

[0151] The first parameter calculated in this invention is the SCR ratio, which is used to assess how much the area energy is greater than the energy of the interfering signals in each 2D S-window. Therefore, the SCR value quantifies the presence of an artifact at the target location in the brain. The SCR can be identified as follows:

[0152]

[0153] where [F(n)] Stroke is the energy value in the S 2D window in the presence of a target, and [F(n)] Clutter is the energy value over the same region S when the target is not present. The spurious signals are due to residual artifacts, and the average energy value of the spurious signals is calculated in a background model, which is based on a healthy head model without stroke.

[0154] The second parameter calculated in the context of this invention is the SMR, which allows to evaluate to what extent the energy of the target sound is higher than the average energy of the interfering signals in the head area. The SMR metric is specified as the ratio between the average value of the backscattered energy in the target area and the average value in the whole brain. The SMR can be identified as follows:

[0155]

[0156] where [F(n)] Stroke is the intensity of all points inside the window S and mean[F(n)] Clutter is the intensity of all points inside the window H, where H represents the entire head region.

[0157] La shows the results of generating SCR metric maps from a 2D microwave image of the, where the SCR value in ordinates varies from 0 to 5.

[0158] The ultrafast processing technique for a super-resolution microwave brain imaging system based on maximizing the extracted information capacity is now described. After locating the target, the signals related to the target area are selected. Depending on the target position, the relevant signals are selected in the target area to apply adaptive calibration. The adaptive connectome calibration method can be used for calibration. The hemispherical area of ​​the headset 4 can be divided into horizontal or vertical planes, each plane corresponding to a subset of the antenna array 5. The horizontal arrangement of signal selection can be applied depending on the height of the target area.In other words, if the detected target has the same level as one of the circular antenna arrangements, only the signals related to this circular arrangement will be used to create the image in subsequent scans. Another arrangement in the vertical direction is possible, which, due to the lack of antennas, can only be effective to help with better calibration.

[0159] The apparatus 1 of the invention therefore performs a first scan of the patient's brain using the helmet 4 carrying the antennas 5, by microwave imaging. A diffusion tensor is obtained, which is processed by the processing unit 2, which processes the image obtained by a SCR and SMR detection method to detect and position the detected target in the brain. Once the target is detected, a three-dimensional image is constructed by the adaptive connectome calibration method and compressive sampling, making it possible to select a subgroup of the antenna array 5 which will be the most suitable for producing a new image of the brain limited to the region of the target.Once the image has been limited to the target constructed, also by the adaptive connectome calibration and compressive sampling method, this limited image is merged with the previous image or even with a more precise image obtained by fMRI or others, then the steps are repeated to obtain a sequence of images making it possible to follow the evolution of the target, without having to recalculate the entire image each time. The power of the device according to the invention lies in the fact that the target is detected in each global image by an algorithm that is inexpensive in computation, the image capture then being limited to the region of the detected target to obtain a global image resulting from the fusion of a more precise image with the image limited to the imaged area.

Claims

– Method for three-dimensional imaging of the brain by microwave imaging, characterized in that it comprises the following steps: a - producing on a patient on whose head is arranged a microwave antenna array controlled by a switching network connected to a controllable signal transmitter-receiver device, a three-dimensional global image of the brain by microwave imaging using all the antennas of the microwave antenna array; b - detecting at least one target in the global image of the brain produced in step a; c - calculating a position of the at least one target in the global image of the brain produced in step a;d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;e - merging the global image and the local processing image into a three-dimensional global intermediate image;f - detecting at least one target in the global intermediate image;g - calculating a position of the at least one target in the global intermediate image;h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;i – merging the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;j – repeating steps f to i a predetermined number of times,the at least one target being detected by calculating the signal-to-interference ratio and the signal-to-average ratio in two-dimensional planes of the three-dimensional image, the at least one target being detected on the surfaces of each two-dimensional plane on which the values ​​of the signal-to-interference ratio and the signal-to-average ratio are simultaneously maximum.; – Method according to claim 1, characterized in that each step of producing a three-dimensional image by microwave imaging comprises collecting diffusion parameter data representing microwaves diffused by the patient's brain in a diffusion tensor, generating differential diffusion parameter data by an adaptive connectome calibration method in order to remove spurious signals, and processing the differential diffusion parameter data by a confocal image reconstruction method to obtain a three-dimensional image. – Method according to claim 1, characterized in that the position of the at least one target in the three-dimensional image is calculated as being the position of the surfaces of each two-dimensional plane on which the values ​​of the signal-to-interference signal ratio and the signal-to-average ratio are simultaneously maximum. – Method according to one of claims 1 to 3, characterized in that the subgroup of the microwave antenna network associated with the position of the at least one target is constituted by the antennas having the shortest distance to the position of the at least one target. – Method according to one of claims 1 to 4, characterized in that compressive sampling is applied to the microwave signals emitted by the antennas of the antenna network. - Method according to one of claims 1 to 5, characterized in that each step of merging two three-dimensional images consists of replacing, in the global image, the part corresponding to the local image, by the local image to obtain the merged image. – A device (1) for hyperfrequency brain imaging, characterized in that it comprises a headset (4) equipped with a hyperfrequency antenna array (5), a switching network (3) controlling the hyperfrequency antenna array (5), a vector network analyzer (6) connected to the switching network (3), a processing unit (2) connected to the switching network (3) and to the vector network analyzer (6) and controlling the switching network (3) and the vector network analyzer (6), a human-machine interface (7) connected to the processing unit (3), the processing unit (3) comprising computing means and memory for carrying out the following steps: a - producing a three-dimensional global image of the brain by hyperfrequency imaging using all the antennas (5) of the hyperfrequency antenna array; b - detecting at least one target in the global image of the brain produced in step a;c - calculating a position of the at least one target in the global image of the brain produced in step a;d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array (5) controlled by the switching network (3) associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;e - merging the global image and the local processing image into a three-dimensional global intermediate image;f - detecting at least one target in the global intermediate image;g - calculating a position of the at least one target in the global intermediate image;h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array (5) controlled by the switching network (3) associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;i - merging the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;j - repeating steps f to i a predetermined number of times, the at least one target being detected by calculating the signal-to-interference ratio and the signal-to-average ratio in two-dimensional planes of the three-dimensional image, the at least one target being detected on the surfaces of each two-dimensional plane on which the values ​​of the signal-to-interference ratio and the signal-to-average ratio are simultaneously maximum. ; – Apparatus (1) according to claim 7, characterized in that each step of producing a three-dimensional image by microwave imaging comprises collecting diffusion parameter data representing microwaves diffused by the patient's brain in a diffusion tensor, generating differential diffusion parameter data by an adaptive connectome calibration method in order to remove spurious signals, and processing the differential diffusion parameter data by a confocal image reconstruction method to obtain a three-dimensional image. – Apparatus (1) according to claim 8, characterized in that the position of the at least one target in the three-dimensional image is calculated as being the position of the surfaces of each two-dimensional plane on which the values ​​of the signal-to-interference signal ratio and the signal-to-average ratio are simultaneously maximum. – Apparatus (1) according to one of claims 7 to 9, characterized in that the subgroup of the microwave antenna network (5) associated with the position of the at least one target is constituted by the antennas (5) having the shortest distance to the position of the at least one target. – Apparatus (1) according to one of claims 7 to 10, characterized in that compressive sampling is applied to the microwave signals emitted by the antennas (5) of the antenna network. - Apparatus (1) according to one of claims 7 to 11, characterized in that each step of merging two three-dimensional images consists of replacing, in the global image, the part corresponding to the local image, by the local image to obtain the merged image. – Apparatus (1) for hyperfrequency imaging of the brain according to one of claims 7 to 12, characterized in that the antenna network (5) comprises 24 antennas, preferably butterfly antenna type antennas. – Computer program product comprising instructions which, when executed by a microwave imaging apparatus according to one of claims 7 to 13, carry out the following steps: a - producing a three-dimensional global image of the brain by microwave imaging using all of the antennas of the microwave antenna array; b - detecting at least one target in the global image of the brain produced in step a; c - calculating a position of the at least one target in the global image of the brain produced in step a; d - producing a local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step a and a reduction in the number of samples produced by the antennas;e - merging the global image and the local processing image into a three-dimensional global intermediate image;f - detecting at least one target in the global intermediate image;g - calculating a position of the at least one target in the global intermediate image;h - producing a new local three-dimensional processing image of the target by microwave imaging using at least one of a subgroup of the microwave antenna array controlled by the switching network associated with the position of the at least one target detected in step f and a reduction in the number of samples produced by the antennas;i - merging the global intermediate image and the new local processing image into a new global three-dimensional image also called an intermediate image;j - repeating steps f to i a predetermined number of times. ;

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