PERFUSION IMAGING METHOD AND SYSTEM

Abstract

PERFUSION IMAGING METHOD AND SYSTEM A perfusion imaging method in a region of interest of an individual, the method comprising a computer-implemented processing method, the processing method comprising: Generating intensity-time curves from pixel / voxel intensities of N blocks of J elementary ultrasound images of the region of interest generated from raw ultrasound data; Identifying a reference intensity-time curve of a predetermined reference area of ​​the region of interest; Generating at least one perfusion indicator from the reference intensity-time curve. Figure for abstract: [FIG.1]

Description

Title of the invention: METHOD AND SYSTEM FOR PERFUSION IMAGING technical field

[0001] The technical field is that of parametric ultrasound perfusion imaging.

[0002] Perfusion is an essential physiological parameter in medicine because of its fundamental role in maintaining tissue health and function. In medical practice, perfusion assessment is an essential tool in various specialties such as cardiology, neurology, oncology, and intensive care. Monitoring perfusion levels makes it possible to identify tissues that are at risk of damage or dysfunction. State of the art

[0003] Methods for generating perfusion indicators by contrast ultrasound imaging are known.

[0004] By ultrasound imaging, we mean a type of ultrasound imaging in which ultrasound imaging data of a region of interest are acquired in the presence of a contrast agent, for example in the form of microbubbles, previously injected into the blood vessels.

[0005] From the article “Imaging of perfusion using ultrasound”, Cosgrove et al., Eur J Nucl Med Mol Imaging (2010) 37 (Suppl 1):S65-S85, a method for generating perfusion indicators such as arrival times or durations to peak, classically called “time to peak” or TTP in Anglo-Saxon terminology, is known. These indicators are calculated from curves representing the intensity of the pixels of the ultrasound images as a function of time. These curves are called time-intensity curves or TICs, referring to the Anglo-Saxon expression “Time Intensity Curve”.

[0006] However, the measurements obtained are not very precise.

[0007] One aim is to propose a solution enabling the generation of relatively accurate perfusion indicators. Summary

[0008] To this end, the invention relates to a perfusion imaging method in a region of interest of an individual, the method comprising a computer-implemented processing method, the processing method comprising: • Generate intensity-time curves from pixel / voxel intensities of N blocks of J elementary ultrasonic images of the region of interest generated from raw ultrasonic data, • Determine, from the intensity-time curves, a reference intensity-time curve for a predetermined reference area of ​​the region of interest, • Generate at least one perfusion indicator from the reference intensity-time curve.

[0009] According to one embodiment, the treatment process comprises: • display representative information from a perfusion indicator on a screen.

[0010] According to one embodiment, the J elementary images of each block are generated from J sub-blocks of raw ultrasonic data acquired sequentially, each elementary image being generated from a unique sub-block of raw ultrasonic data and the N blocks being generated from N blocks of J sub-blocks of raw data, the N blocks of J sub-blocks of raw data being acquired sequentially and each block of J elementary images being generated from a unique block of sub-blocks of raw data.

[0011] According to one embodiment, generating the intensity-time curves comprises, for each of a plurality of N blocks of J elementary images, generating a combined image of the region of interest by combining J images representative of the J elementary images of said block.

[0012] According to one embodiment, the combination includes the calculation of a temporal and / or spatial cross combination of the J representative images of the J elementary images.

[0013] According to one embodiment, generating at least one perfusion indicator includes deconvolving at least one of the intensity-time curves from the reference intensity-time curve.

[0014] According to one embodiment, generating the intensity-time curves includes: • improving the signals from the contrast agent relative to the signals from the surrounding tissues so as to generate N blocks of J enhanced images from the N blocks of J elementary images such that the images representing the J elementary images are representative of the J enhanced images.

[0015] According to one embodiment, generating the intensity-time curves includes: • generating N blocks of J refined images from the N blocks of J enhanced images such that the refined images have a less wide point spread function than the enhanced images so that the representative images of the J elementary images are representative of the J refined images.

[0016] According to one embodiment, the process comprises: • Acquire, using an acquisition system comprising a transducer array, the raw ultrasonic data, • Generate N blocks of J elementary images from the raw ultrasonic data.

[0017] According to one embodiment, the J sub-blocks of raw data are acquired at a frequency between 5 Hz and 500 Hz and the sub-blocks of raw data are acquired at a frequency between 200 and 20,000 Hz.

[0018] According to one aspect, the invention relates to a perfusion imaging system configured to implement the method according to the invention, the system comprising a processing system including a processing unit comprising a set of at least one processor, the processing unit being configured to implement the processing method.

[0019] According to one embodiment, the system includes an acquisition system comprising a transducer array, the acquisition system being configured to acquire the raw ultrasonic data and to generate the elementary images from the raw ultrasonic data.

[0020] According to one embodiment, the acquisition system is configured to acquire the N blocks of J sub-blocks of raw ultrasonic data, the processing system being configured to generate the N blocks of elementary images from the N blocks of J sub-blocks of raw ultrasonic data.

[0021] The invention also relates to a computer program product comprising instructions which lead the system according to the invention to execute the steps of the process according to the invention.

[0022] The invention also relates to a computer-readable medium on which the computer program is recorded. Brief description of the drawings

[0023] Other features and advantages of the invention will become apparent from the following detailed description, with reference to the accompanying figures, which illustrate: - [Fig. 1]: a flowchart of the steps of an example of a treatment process according to the invention, - [Fig.2]: N blocks of J elementary images, - [Fig. 3]: examples of intensity-time curves, - [Fig. 4]: in (a), an AIF and a TIC, and in (b) and (c) examples perfusion indicators, - [Fig. 5]: on the left an example of an elementary image, in the middle an example of an enhanced image, on the right an example of a combined image. - [Fig. 6]: an example of an individual acquisition sequence, - [Fig. 7]: an example of elementary acquisition, - [Fig. 8]: an example of acquiring a sub-block of data, - [Fig. 9]: in block form, the material elements of an example of system configured to implement the method according to the invention, Detailed description

[0024] A parametric ultrasound perfusion imaging method is proposed.

[0025] This imaging method includes the generation of an indicator or parameter of blood perfusion in a region of interest R of an individual from ultrasound images of the region of interest R generated from raw data acquired in the presence of an ultrasound contrast agent in the region of interest R.

[0026] The individual can be human or animal.

[0027] The region of interest is, for example, an area of ​​the individual's brain, but it can be any area of ​​the body, preferably vascularized.

[0028] The invention also relates to an ultrasound imaging system configured to acquire raw ultrasound data and to generate said at least one perfusion indicator.

[0029] The method comprises, as shown in [Fig.1], the acquisition 10 of raw ultrasonic data of the region of interest R by an ultrasonic probe S, visible in [Fig.6], comprising an array RE of TR transducers.

[0030] The acquisition 10 of the raw ultrasonic data is implemented in the presence of contrast agents in the region of interest R.

[0031] According to one embodiment, the acquisition 10 of the raw ultrasound data is implemented after intravenous injection into the individual of a contrast agent comprising individual contrast agents, for example, in the form of gas microbubbles so that contrast agents are present in the region of interest R during the acquisition 10. These contrast agents injected into the body are of the exogenous type.

[0032] Alternatively, acquisition 10 is carried out without prior injection of contrast agent. The contrast agents present in the region of interest are then endogenous contrast agents, for example, red blood cells.

[0033] In the rest of the text, the term microbubbles will be used, knowing that the process applies to all types of contrast agents.

[0034] The method then includes a processing step T comprising in particular the generation 20 of ultrasound images of the region of interest R from the raw ultrasound data acquired during the acquisition 10 and the generation 100 of at least one perfusion indicator from the ultrasound images generated during step 20.

[0035] During generation step 20, N blocks B; are generated, with i = 1 to N where i is an integer and N is an integer greater than 1, each formed of J elementary ultrasonic images lE^ with j = 1 to J, j being an integer and J being an integer greater than 1, of a predetermined region of interest of an individual. Blocks B; of J elementary images IEij are represented in [Fig.2].

[0036] Thus, during generation 20, N*J elementary images IEij, j (with i = 1 to N and j = 1 to J) of the region of interest R are generated. According to a non-limiting embodiment, the elementary images IEij are B-mode images. Other types of ultrasonic elementary images can of course be used.

[0037] By elementary image IEij, we mean a mesh of pixels or voxels whose respective intensities are representative of acoustic properties of the medium surrounding the probe at respective coordinates. The coordinates of a pixel or a voxel represent a position relative to the probe of a point in the region of interest.

[0038] In the following text, each image is considered to be a grid of pixels, but the invention obviously applies where the images are made up of voxels.

[0039] The T treatment then includes the generation of at least one perfusion indicator from the elementary images. Generation of intensity-time curves

[0040] Generation step 100 of the perfusion indicator includes the generation 110 of intensity-time curves also called TIC “acronym for the Anglo-Saxon expression Time - Intensity Curves” or intensity-time curves also called intensity-time curves from the elementary images IEij with i= 1 to N and j= 1 to J.

[0041] This step is implemented automatically, i.e., by computer.

[0042] The intensity-time curves TIC(x,y,t) represent the dynamics of the microbubbles. The shapes of these curves provide valuable information about the health of the tissues, including the different blood vessels.

[0043] The intensity, for example, can take a set of M values ​​(M being a finite integer greater than 1) corresponding to M gray levels ranging from 0 to M1. For example, this value can take 256 values ​​between 0 and 255, but M is not limited to 256.

[0044] Intensity-time curves generated for pixels with different coordinates TIC (xl, yl, t), TIC (x2, y2, t), TIC (x3, y3, t) and TIC (x4, y4, t) are shown in [Fig.3].

[0045] This step 110 includes the generation, for each pixel of predetermined spatial coordinates (x, y), of a TIC intensity-time curve (x, y, t) representing the evolution of pixel intensity as a function of time. This curve is generated from the elementary images lE^ with i= 1 to N and j= 1 to J.

[0046] The time t is defined as a function of i and the acquisition frequency, denoted FB, of the blocks B; so that for each i, t= FB* (i-1).

[0047] We thus obtain Z TIC (x,y,t) where Z is the number of pixels of the elementary images lEij.

[0048] Identification of the reference intensity-time curve

[0049] According to the invention, the process then comprises a determination step 120 automatic, i.e. by computer, of a reference intensity-time curve corresponding to an intensity-time curve of a predetermined reference zone of the reference region R.

[0050] This step is implemented from the Z TIC (x, y, t).

[0051] In one embodiment, the reference intensity-time curve is the arterial input function FAI, also called AIF, an acronym for the Anglo-Saxon expression "Arterial Input Function".

[0052] Beneficially, the reference area is chosen as the main artery of the region, that is to say, the one which supplies blood to the other vessels (veins / arteries) of the region of interest R.

[0053] In another embodiment, the reference intensity-time curve is the venous output function or VOF, referring to the Anglo-Saxon expression "Venous output function".

[0054] The reference area is then, for example, chosen to be the largest vein in the region of interest.

[0055] In one embodiment, the determination of the arterial input function AIF consists of automatically selecting the intensity-time curve which, among the Z TIC (x,y,t), is the one whose intensity increases the fastest.

[0056] For this purpose, we determine for example the TIC which among the Z TIC (x, y, t) has the highest positive slope when its intensity level reaches a predetermined intensity ID.

[0057] For example, the predetermined intensity ID is given by:

[0058] ID = c*IB

[0059] Where c is a predetermined positive coefficient and IB is an average intensity of the image noise.

[0060] For example, c is between 3 and 10.

[0061] c is, for example, equal to 5.

[0062] Alternatively, the intensity-time curve whose intensity increases the fastest is identified by selecting the intensity-time curve exhibiting the earliest absolute maximum among the Z TIC (x,y,t).

[0063] In one embodiment, the VOF is determined by automatically selecting the intensity-time curve which, among the Z TIC (x,y,t), has the latest peak with an intensity greater than a predetermined threshold.

[0064] In another embodiment, the method includes, prior to the determination step 120, a step of selecting the predetermined reference area on a representative image of at least one elementary image 1¾.

[0065] According to one embodiment, the representative image of one or more elementary images lE^ is a combined image ICi, generated from elementary images lEij as we will describe in the rest of the text.

[0066] According to one embodiment, the step of selecting the reference area is, for example, implemented automatically by computer.

[0067] It is, for example, carried out by morphological segmentation of the image based on prior knowledge of the morphology or anatomy of the area. The segmentation is, for example, implemented using a machine learning model trained to make this selection on an image representative of at least one elementary image of the region of interest R.

[0068] Alternatively, the segmentation is implemented by correlating the image with a predetermined model function.

[0069] Alternatively, the selection of the reference area is made manually by a user via an INTE input user interface of the system.

[0070] By way of non-limitation, the selection is carried out by a pointing device of the INTE input interface.

[0071] In this variant, step 100 includes displaying a representative image of one or more of the elementary images IEij, for example displaying a combined image IC;, on a screen of an output interface INTS of the system user interface.

[0072] The user then manually segments the reference area using the pointing device on the image displayed on the INTS screen.

[0073] According to one embodiment, the data relating to the reference area are then stored in a memory of a processing system of the system so that the processing system can implement the identification step.

[0074] This data is, for example, a set of pixels forming the reference area.

[0075] The method then includes the step of determining the arterial input function, or more generally the reference intensity-time curve, from the TIC (x,y,t) of the pixels included in the reference area.

[0076] This step consists, for example, of automatically selecting the intensity-time curve which, among the TIC (x,y,t) of the pixels of the reference area ZR, is the one whose intensity increases the fastest as explained previously.

[0077] Alternatively, an average of the TIC (x,y,t) of the pixels of the reference zone ZR is calculated. Generation of a perfusion indicator

[0078] According to the invention, step 100 includes the generation 130 of at least one perfusion indicator from the reference intensity-time curve.

[0079] This step is automatic. In other words, it is implemented by computer.

[0080] The use of the reference intensity-time curve to generate the perfusion indicator(s) makes it possible to normalize the intensity-time curves of the different pixels, which makes it possible to generate perfusion indicators whose values ​​are absolute and therefore comparable.

[0081] Since the AIF is related to a large artery, it is supplied directly by the heart. This makes it a good representation of the general microbubble concentration entering the tissues. Normalizing the intensity-time curves from the AIF therefore allows us to characterize only the behavior of the microbubbles in the tissues relative to their arrival in the artery corresponding to the AIF.

[0082] Thus the arrival times and the transit times in the tissues are calculated from the arrival time in the artery and not from the start of the acquisition.

[0083] Furthermore, fluctuations due to systemic effects are removed from the signal, so as to take into account only the contributions of the tissues of interest.

[0084] Furthermore, the generation of indicators obtained by normalizing the intensity-time curves from the reference intensity-time curve allows these indicators to be compared from one individual to another and to be followed by these indicators for the same individual over several days.

[0085] Indeed, these indicators do not depend on the image acquisition conditions. In particular, they do not depend on the time between the start of recording and the arrival time of microbubbles in the region of interest. Nor do they depend on potentially confounding physiological parameters, such as heart rate and cardiac output.

[0086] Thus, medical analyses can be based on the generated perfusion indicators.

[0087] In the following text, it is assumed that the reference intensity-time curve is the arterial input function AIF, but what is described also applies to the case where the reference intensity-time curve is the VOF.

[0088] Alternatively, several reference intensity-time curves are determined.

[0089] For example, the AIF and the VOF are determined.

[0090] One can then determine at least one indicator from several reference intensity-time curves.

[0091] According to one embodiment, at least one normalized perfusion indicator is determined from the AIF.

[0092] For example, the generation step includes a deconvolution step, using AIF, of the intensity-time curves TIC associated with the different pixels, that is, associated with the spatial positions (x,y) of the different pixels. Thus, the blood flow curve representing the evolution of blood flow in mL of blood per 100 g of tissue per minute at the spatial position (x,y) as a function of time is given by: BF(x, y, t) = TIC{x, y, t) ^AIF

[0093] The BF curves {x, y, t) model the relationship between the time curves pixel intensity and tissue perfusion dynamics.

[0094] An example of the curve obtained is shown in [Fig.4] in b.

[0095] Other indicators can be generated during step 130.

[0096] In the case where several reference intensity-time curves are determined, the method may include deconvolution of the intensity-time curves by each or more of these reference curves to calculate several blood flow curves and a step of calculating an indicator from several of these blood flow curves.

[0097] According to one embodiment, at least one indicator is generated from the blood flow curves BF(x, y, t).

[0098] Step 130 may include measuring or determining, for each of the spatial positions (x,y), i.e., for each pixel, a time to maximum, also called Tmax and denoted Tmax(x,y), referred to as "Time to Maximum" in Anglo-Saxon terminology, being the instant associated with the maximum concentration of microbubbles (maximum flow?) for the pixel with spatial coordinates (x, y). Each time to maximum Tmax(x,y) is calculated from the corresponding blood flow curve BF(x, y,t).

[0099] The time to maximum provides a standardized local measurement of tissue perfusion independent of temporal variations.

[0100] Step 130 may include the measurement or determination, for each of the spatial positions (x,y), i.e. for each of the pixels, of a maximum throughput.

[0101] For example, in the brain this maximum, for the pixel (x, y) is called cerebral blood flow or Cerebral Blood Flow (CBF) denoted CBF(x,y)

[0102] The Tmax (x, y) and the CBF (x, y) are, for example, determined from the corresponding blood flow curve BF ( x, y,t ).

[0103] by an algorithm for detecting a maximum of the curve BF{x, y, t) or by curve fitting.

[0104] Step 130 may include the measurement or determination, for each of the spatial positions (x,y), i.e., for each pixel, of a cerebral blood volume also called CBV and denoted CBV(x,y), referred to as "cerebral blood volume" in Anglo-Saxon terminology. Each CBV(x,y) is measured from the corresponding blood flow curve BF(x, y,t), i.e., measured for the same spatial position (x; y).

[0105] The CBV (x,y) is the area under the curve BF(x, y,t).

[0106] It should be noted that CBF and CBV are measured or determined when the region of interest is the brain or a vascularized part of the brain.

[0107] More generally applicable to different vascularized areas, a blood volume or BV (acronym for the Anglo-Saxon expression "Blood Volume") and / or a blood flow or BF (acronym for BF) are determined for different pixels.

[0108] Step 130 can thus include the measurement, for each pixel, of a time to peak also called TTP (acronym for the Anglo-Saxon expression Time to Peak) and denoted TTP(x, y). This indicator is, for example, the time between the instant of microbubble injection, which is the instant t=0, and the maximum of the TIC corresponding to the pixel and denoted TIC(x, y).

[0109] Step 130 can thus include the measurement, for each pixel, of the mean transit time also called MTT and denoted MTT(x,y) which is the average time for the blood to cross the tissue, that is to say for it to pass from the arterial side to the venous side.

[0110] This average time is given, for example, by the full width at half maximum (FWHM) of the corresponding blood flow curve BF(x, y, t'). In other words, [YES] MTT(x, y) = FWHM(BF(x, y, t))

[0112] Where FWHM(BF(x, y,t )) is the full width at half maximum of the blood flow curve BF(x, y, t)

[0113] The list of perfusion indicators above is not exhaustive. Many other perfusion indicators can be generated, such as a volume (in number of pixels or voxels) whose Tmax is greater than a predetermined value.

[0114] Generally, an indicator is determined for a pixel or for a plurality of pixels or for each pixel as described above. Display

[0115] According to one embodiment, the method comprises displaying 200 visual information representative of at least one perfusion indicator on a screen of an INTS output interface of the system.

[0116] The visual information of a perfusion indicator may include, but is not limited to, one or more numerical values, and / or a curve and / or an image representing the perfusion indicator. Numerical information includes, for example, a numerical value of an indicator or a combination of indicators.

[0117] For example, we can display a numerical value of a Tmax of a pixel and / or a numerical value of the Tmax calculated for the different pixels.

[0118] One or more curves can be displayed, for example of type TIC or BF.

[0119] A color or greyscale image can be represented having pixels / voxels whose intensities or colors depend on or are values ​​of a parameter calculated for these pixels / voxels.

[0120] For example, a Tmax map can be displayed as shown in [Fig.4] in c. This map is a coloured or greyscale image having pixels whose intensities or colours depend on the values ​​of the Tmax(x,y) calculated for the positions (x,y) of the pixels.

[0121] According to one embodiment, information representative of several indicators is displayed

[0122] According to one embodiment, this information is displayed simultaneously on one or more screens of the processing system.

[0123] According to one embodiment, information is displayed in a superimposed manner.

[0124] For example, the Tmax map can be displayed superimposed with a combined IC image; or an image reconstructed from the combined images. Improvement

[0125] According to one embodiment, step 110 includes an improvement step 111.

[0126] In the initial IE;j images, the contribution of signals from microbubbles is mixed with signals from surrounding tissues. Enhancement 111 strengthens the signals from microbubbles and weakens the signals from surrounding tissues.

[0127] Thus, improvement 111 is implemented to improve the signals from the microbubbles relative to the signals from the surrounding tissues so as to generate N blocks BA; of J improved images IA;, j from the N blocks B; of J elementary images IEij.

[0128] The surrounding tissues include, for example, at least one tissue taken from among the following: a wall of a blood vessel in which microbubbles circulate, blood circulating in the vessel, bone, fat, a tissue forming a neighboring organ.

[0129] For each i, the J enhanced images IA;, j of block BA; are generated from the J elementary images IE;, j of block B;.

[0130] The enhancement 111 is implemented so that the contrast between the pixels of the J enhanced images IA; j of block BA; representing microbubbles and the pixels of the J enhanced images LA d of block BA; representing the surrounding tissues is higher than the contrast between the pixels / voxels of the J elementary images IE; j of block B; representing the microbubbles and the pixels of the J elementary images IE; j of block B; representing the surrounding tissues.

[0131] According to one embodiment, each enhanced image LA j of block B; is generated from an elementary image IE; j of block B;.

[0132] Alternatively, each enhanced image IA; j of block BA; is generated from several elementary images IE;, j of block B;.

[0133] For example, at least one or each enhanced image IA; j of the block is generated from all elementary images IE; j of block B;.

[0134] The enhancement step 111 includes, for example, filtering each elementary image IE;, j to remove signals from the tissues in the elementary image and retain only the signals from the microbubbles. This step is, for example, known as "clutter filtering" or "background removal" in English.

[0135] According to one embodiment, improvement step 111 includes the application of temporal or spatio-temporal filters of the Singular Value Decomposition or SVD type, an acronym for the expression "Singular Value Decomposition" in Anglo-Saxon terminology, to separate the echoes from the microbubbles from the echoes from the tissues.

[0136] Alternatively, each enhanced image IAij is generated by amplitude modulation of elementary images lE^ of block B;.

[0137] For example, the intensity of each pixel with predetermined spatial coordinates of the enhanced image LAj is equal to a linear combination of the intensities of the pixels having the same spatial coordinates of a first elementary image of block B; and a second elementary image of block B;, the first and second elementary images having different order numbers in block B;. For example, the intensity IA; j(x, v) of the pixel with spatial coordinates (x, y) of the enhanced image LA j is given by the following formula:

[0138] IAi j(x,y)= a* IEL JA(x, y) + b*IEt y+1 (x, y)

[0139] Where lEj j(x, y) is the intensity of the pixel with spatial coordinates (x, y) of the elementary image lEi j and where a and b are coefficients.

[0140] Alternatively, the intensity of each pixel of the enhanced image LAj is obtained by linear combination of the intensities of pixels having the same spatial coordinates of a first elementary image of order number j of block B; and of a second elementary image of order number j-1 of block B;.

[0141] Linear combination is, for example, a subtraction.

[0142] Alternatively, the intensity of each pixel of the improved image IA^ j(x, j) is obtained by linear combination of the intensities of the pixels having the same spatial coordinates of a first elementary image of order numbers i and j and of an average image being the average of the J elementary images IE;, j of the block B;.

[0143] According to a particular embodiment, the acquisition 10 of the raw data is carried out in such a way as to allow the elementary images to be recombined advantageously. For example, one can perform a phase inversion known as "Puse Inversion" in Anglo-Saxon terminology, an amplitude modulation, for example, amplitude modulation over long ensembles (AMLE), or the beams are emitted according to CHIRP emission (Compressed High Intensity Radar Puise), emission using a Golay code, or other equivalent strategies. Thus, the enhancement step, by amplitude modulation, uses a non-linear strategy.

[0144] At the end of the improvement step 111, we obtain N blocks BA; each consisting of J improved images IA;, j with j= 1 to J.

[0145] The enhancement step 111 is a computer-implemented step. In other words, the blocks N BA; each having J enhanced images IA;, j with j= 1 to J are automatically generated from the elementary images 1¾ with i= 1 to N and j= 1 to J.

[0146] Alternatively, the process is devoid of an improvement step. Refining

[0147] According to one embodiment, step 110 includes a refinement step 112 which is a generation step of refined images IAF;, j with j= 1 to J and i= 1 to N from the improved images IA;, j with j= 1 to J and i= 1 to N so that the refined images IAF; , j with j= 1 to J and i= 1 to N have a point spread function also called PSF, an acronym for the Anglo-Saxon expression "Point Spread Function" less wide than that of the improved images IA;, j with j= 1 to J and i= 1 to N.

[0148] During this step, a refined image IAF;, j is generated from each enhanced image IA;, j. Each refined image is generated from a single enhanced image IA;, j with the same order numbers i and j.

[0149] We thus obtain N blocks denoted BAF; each being made up of J refined images IAF; , j with j= 1 to J.

[0150] The generation of each refined image IAF;, j can be carried out according to several known and equivalent methods, such as, for example, by spatial deconvolution of the enhanced image IA;, j of the same order numbers i and j with a model PSF predetermined, by applying a binary mask corresponding to the local maxima of the intensity to the improved image IA;, j of the same order numbers i and j so as to generate a refined binary image, or by filtering the image (we obtain zeros and 1s at the end), or improved IA;, j of the same order numbers i and j by applying a Jerman morphological filter to it.

[0151] These examples are not limiting and may be used alone or in combination.

[0152] When the process is devoid of an enhancement step, the refined images can be generated from elementary images IE;, j instead of enhanced images IA;, j.

[0153] The refinement step 112 improves image resolution and refines microbubbles and, ultimately, the trajectories of microbubbles visible in the combined images, which will be described later. It also reduces the contribution of residual noise to the image.

[0154] The refinement step is a step implemented by computer, i.e. automatically.

[0155] In another embodiment, the process is devoid of a refining step. Combination

[0156] According to one embodiment, step 110 includes a step 113 of combining J basic images IMi / representative of the J elementary images IE; j of each of the blocks B;, with i= 1 to N so as to generate a combined image ICi for each block B;, that is to say so as to generate N combined images IC; with i= 1 to N.

[0157] When step 110 includes improvement step 111 without refinement step 112, the basic images IM;, j are the enhanced images IA;, j of the blocks BA;.

[0158] When step 110 includes the refinement step, the base images IM;, j are the refined images IA;, j of the BAF; blocks.

[0159] When step 110 is devoid of the improvement step and the refinement step, the basic images IM;, j are the elementary images IE;, j.

[0160] The combination step 113 comprises N elementary combination steps 113;, with i= 1 to N.

[0161] Each elementary combination step 113; consists of combining the J basic images IM;, jwith j= 1 to J representative of the elementary images IE; jwith j= 1 to J of block B; so as to generate a single combined image IC;

[0162] We thus obtain N combined images IC; with i= 1 to N.

[0163] These combined IC images; correspond to a sequence of N IC images; in which the index i corresponds to the order number of the image in the sequence.

[0164] The elementary combination 113; is implemented so that the combined image ICire represents the trajectories of the microbubbles during the acquisition time of block B; in the region of interest R.

[0165] Thus, the combined image IC; represents the vasculature within the region of interest R.

[0166] In other words, this step makes the blood vessels more distinct and visible by combining all the positions of the microbubbles that have explored space.

[0167] This allows for the reliable and precise detection of a target vascular area based on morphological criteria on at least one combined IC image; which is not permitted on an elementary image, or on an enhanced image or on a refined image representing, not the trajectories of the microbubbles during the acquisition time of the B block; but the "fixed" positions of the different microbubbles during an elementary acquisition.

[0168] Furthermore, the generation of a combined image makes it possible to generate spatially resolved and contrasted TICs, i.e. having a low noise level, which allows for a precise determination of the AIF and therefore to generate precise and reliable perfusion indicators.

[0169] Detection of the main artery, i.e. the largest artery within a region of interest, makes it possible to reconstruct the arterial entry function.

[0170] The combined image IC; is generated by combining 113i of the basic images IM^.

[0171] In one embodiment, the combined image IC; is obtained by summing in a consistent manner the basic images IM^ for j= 1 to J.

[0172] For example, the consistent sum is a quadratic sum.

[0173] Thus the intensity JC^x, y) of each pixel of the combined image IC; is given by the following formula: [01741

[0175] Where IM^x, y, j ) is the intensity of the pixel with spatial coordinates (x,y) of the basis image IMij.

[0176] More generally, the combined image IC can be generated by performing a temporal and / or spatial cross combination of the basic images IMij with j= 1 to J.

[0177] This combination is, for example, a summation.

[0178] Temporal cross-combination consists of combining or merging basic IMijai having different j.

[0179] An example is the cross summation of basic images having the same order number i, but different order numbers j such that the difference between these order numbers is Ôj.

[0180] The intensity IC^x, y) of each pixel with coordinates (x,y) of the combined image IC; is given by: 101811 ic^x, J) = J' 7) P y

[0182] where lM^xyj is the intensity of the pixel with spatial coordinates (x,y) of the base image IMij Ôj.

[0183] Spatial cross-combination consists of combining or merging different spatial areas of basic IM^ images.

[0184] An example is the cross summation of a pixel with coordinates (x,y) with areas spaced two by two by a number 5X pixels, called time offset, of a spatial window centered on the pixel (x,y) and defined along a direction, for example the direction of the abscissas (x being the coordinate of the pixel along the x-axis), and having a length equal to a number equal to A x pixels.

[0185] The intensity IC^x, y) of each pixel with coordinates (x,y) of the combined image IQ is then given by: 101861 IC^x, y) = y- j) I • v, 2)1

[0187] Where y, j) is the intensity of the pixel with spatial coordinates IM^x-ôx, y, j) of the base image IM^.

[0188] It is also possible to perform a cross summation with a shift along the ordinate axis (y being the coordinate along the ordinate axis) and / or with a shift along a third axis perpendicular to the x-axis and the ordinate axis in the three-dimensional case.

[0189] In general, this step can be generalized to any spatial or temporal cross combination on the prepared images.

[0190] An example of this type of combination is a cross cumulant of order 1 or higher with a zero or non-zero spatial offset in a predetermined size spatial window in each of the directions and with a zero or non-zero temporal offset.

[0191] When all the offsets are null, the cross cumulative is a quadratic sum

[0192] At the end of the combination step, N combined IC images; with i= 1 to N were generated.

[0193] The sequence number of each combined image IC; is the sequence number i of the block B; of images from which the combined image IC; was generated in step 110.

[0194] Generation of intensity-time curves from combined images

[0195] According to one embodiment, the Z intensity-time curves TIC (x,y,t) are then generated in a step 114 from the N combined images IC; with i= 1 to N or from a plurality of these combined images.

[0196] More precisely, each TIC (x,y,t) is generated from the intensities of the pixel with coordinates (x,y) of each of the N combined images IC; with i=l to N or a plurality of these combined images.

[0197] Thus, the TIC (x,y,t) generated for the pixel with coordinates (x,y) represents the evolution of the intensity of this pixel on the different combined IC images; with i=l to N as a function of time.

[0198] Alternatively, the method includes a step of determining the Z intensity-time curves TIC(x,y,t) from the N*J elementary images IE;j with i=1 to N and j=1 to J or from a plurality of these elementary images. Thus, the TIC(x,y,t) generated for each pixel with coordinates (x,y) represents the evolution of the intensity of that pixel on the different elementary images IE;,j with i=1 to N as a function of time and j=1 to J. This is, for example, the case when the method lacks steps 111, 112, 113.

[0199] Alternatively, the method includes a step of determining the intensity-time curves Z TIC (x,y,t) from the N*J enhanced images IA; j with i= 1 to N and j= 1 to J or from the refined enhanced images IAF; j with i= 1 to N and j= 1 to J or from a plurality of the enhanced images or a plurality of the refined images. Acquisition

[0200] The method includes a sequential acquisition step 10 of raw ultrasonic data.

[0201] More specifically, we acquire blocks of BD; of raw ultrasonic data.

[0202] This step is implemented by an acquisition system SA comprising a probe S including an array RE of TR transducers referenced in [Fig. 6]. It is possible to define sub-apertures of the RE array, each sub-aperture being composed of one or more TR transducers of the RE array. This step is thus implemented by physical interaction between the probe and the region of interest R.

[0203] Each sub-opening is, for example, composed of all the TR transducers of the RE network.

[0204] As shown in [Fig. 6], a transmitting sub-aperture SOo and a receiving sub-aperture SOo' are defined, which define a transmitting / receiving configuration Ch characterized by a pair (SOo, SOo'). The receiving sub-aperture SOo' may be different from or the same as the transmitting sub-aperture SOo. The pairs of sub-apertures for different configurations Ch are different.

[0205] In the non-limiting example of [Fig.6], the emission sub-aperture SOo is the reception sub-aperture SOo'.

[0206] The acquisition of a block of raw ultrasonic data BD; includes, for example, the repetition, for example a large number of times, of an individual acquisition sequence comprising the following steps shown in [Fig. 6]: - Emission of an ultrasonic beam Wk by the emission sub-aperture SOo of the RE array of TR transducers, - Reception Rhk of echoes emitted by the surrounding medium under the effect of the ultrasonic beam Wk by the receiving sub-aperture SOo' of the RE array of TR transducers so as to generate electrical signals, - PTkh preprocessing, implemented by computer, by the SA acquisition system, including the digitization of electrical signals in order to generate an elementary RFijhk data set, and possibly (but not necessarily) one or more other operations performed on the received echoes, such as a filtering step and / or a demodulation step (carrier suppression) and / or a time gain compensation step better known as TGC in reference to the Anglo-Saxon expression "Time Gain Compensation" and / or resampling of the digitized signals.

[0207] Among the TR transducers of the RE network, only each of the transducer(s) of the emission sub-aperture emits a signal to generate the ultrasonic beam Wkémis during the emission step E^.

[0208] The electrical signals processed, in particular digitized, during preprocessing include only electrical signals generated by each of the transducer(s) of the receiving sub-aperture, during the receiving step R^, among the electrical signals generated by the transducers TR of the RE network during this receiving step Rhk.

[0209] The received echoes are represented, on [Fig.6], as a plurality of time signals corresponding to the evolution, over time, of the signal received by each of the transducers of the receiving sub-aperture SOo'.

[0210] The RFijhk element dataset is represented in [Fig. 6] as a table. It comprises a time sampling of the signal received by each of the transducers of the receiving sub-aperture. Each cell corresponds to a time sample from one of the transducers of the receiving sub-aperture. The cell containing the black circle corresponds to the time sample marked by a black circle.

[0211] According to one embodiment, each individual sihk sequence is carried out so that the ultrasound beam emitted during the reception stage insonifies the region of interest R of the individual containing the contrast agents, for example, in the form of microbubbles and so as to receive, during reception, echoes from the region of interest R. Thus, the raw ultrasound data come from a region of interest R of an individual comprising contrast agents during acquisition.

[0212] According to one embodiment, the region of interest R is a region of the individual comprising blood vessels in which endogenous or exogenous contrast agents are present at the time of acquisition.

[0213] According to one embodiment, the contrast agents R move in the region of interest.

[0214] In the case where the region of interest includes blood vessels, the contrast agents move into the blood vessels under the effect of the circulation of blood in the vessels.

[0215] In the following text, we consider a non-limiting example in which the acquisition concerns a region containing contrast agents in the form of microbubbles. As before, the steps described below relating to these microbubbles are valid for other types of contrast agents.

[0216] As shown in [Fig.7], the acquisition step 10 comprises an elementary acquisition sequence seh, during which K individual acquisition sequences sihk are implemented, where K is an integer greater than or equal to 1, using the same transmit / receive configuration Ch so as to acquire K elementary data sets RFÿhk.

[0217] According to one embodiment, K is between 1 and 100.

[0218] The individual acquisition sequences sihk differ from one another in that the ultrasonic beams Wk exhibit distinct spatial and / or temporal (frequency) characteristics. For example, in one embodiment, the K ultrasonic beams may be a family of plane waves emitted at different angles to the RE grating. Another embodiment involves sending K beams corresponding to waves having respective amplitudes that differ from one another by predetermined scalar factors.

[0219] The method includes an acquisition step sbj of a data subblock SBÿ which includes the implementation of H elementary acquisition step(s) seh with h= 1 to H, where H is an integer greater than or equal to 1, carried out with distinct respective transmit / receive configurations Ch.

[0220] For example, H equals 1, which corresponds to a single transmit / receive configuration Ch = b

[0221] According to one embodiment, H is between 1 and 100.

[0222] h is an integer.

[0223] The elementary acquisition 10; of a data block BD; comprises a sequence of J acquisitions sbj of sub-data blocks so as to acquire J sub-data blocks SBÿ. J is an integer greater than or equal to 1.

[0224] In other words, the raw data subblocks SBÿ are acquired sequentially such that all the raw data of subblock SBÿ are acquired before any raw data of subblock SB; j +b

[0225] Typically, J is between 1 and 2000. According to one embodiment, J can be the same for each data block B; but data blocks B; can, alternatively, have distinct J values.

[0226] According to one embodiment, J is greater than or equal to 2.

[0227] According to one embodiment J is greater than or equal to 10, for example greater than or equal to 15 or 20.

[0228] According to a particular embodiment, J is less than or equal to 1000 or 2000.

[0229] According to one embodiment, the product of J* N is greater than or equal to 4.

[0230] According to a particular embodiment, the product of J*N is between 50000 and 150000.

[0231] The data subblocks SBÿ are acquired successively in the temporal order defined by the index j representing the jth acquisition of a subblock denoted sbj.

[0232] In other words, the index j is an integer.

[0233] When the acquisition sbj of a subblock of data SBÿ has been implemented, i.e. when the elementary acquisition sequences seh have been implemented for the different transmit / receive configurations Ch (with h = 1 to H), an elementary data subblock SBy is obtained consisting of H*K elementary data sets RFijhk, as shown in [Fig.8].

[0234] The sequential acquisition 10 of the N raw data blocks BD; comprises a sequence of N elementary acquisitions 10; to acquire N ultrasonic data blocks BD; of order i. The data blocks BD; are acquired successively in the temporal order defined by the index i representing the ith implementation of the elementary acquisition step 10;.

[0235] N is an integer greater than 1. According to one embodiment, N is determined such that N*J*H*K is between 1000 and 10,000,000. In a typical embodiment, N = 300, J = 1000, H = 4 and K = 5, i.e. N*J*H*K = 6,000,000.

[0236] According to one embodiment, N is greater than or equal to 2, 10, 15 or 20.

[0237] According to a particular embodiment N is greater than or equal to 100.

[0238] According to one embodiment, N is less than or equal to 1000 or 10,000.

[0239] According to one embodiment, N is greater than J.

[0240] Alternatively, N is less than or equal to J.

[0241] In order to generate a number equal to N blocks of raw data BD;, the acquisition 10; of a block of raw data BD; is repeated Nl times. This yields a set denoted BD, such that BD = {BD;, ..., BD;, ..., BDN] of N blocks of raw data BD;. The step 10; index i corresponds to the implementation, for the ith time, of the acquisition step 10; of a raw data block and allows the acquisition of the BD data block; of order i.

[0242] Typically, the acquisition frequency of the raw data blocks BD; is between 5 Hz and 500 Hz.

[0243] The acquisition frequency, denoted FB, of the elementary image blocks B is considered to be the acquisition frequency of the raw data blocks BD.

[0244] Typically, the acquisition frequency of the SBÿ subblocks of raw data is between 200 and 20,000 Hz.

[0245] According to one embodiment, this frequency is between 500 and 4000 Hz.

[0246] The acquisition frequency of elementary images fE^ is considered to be the acquisition frequency of raw data subblocks SB;j.

[0247] The FB frequency is, for example, equal to the quotient of the subblock acquisition frequency by the number of subblocks per block. Generation of elementary images Ei, j

[0248] The generation 20 of the elementary images Eij Comprises, for each subblock SBÿ with j= 1 to J, the primary combination 20ÿ of the raw data of the data subblock SBÿ, to generate an elementary image IE;, j.

[0249] The elementary image IE;, j is generated solely from the raw data of the data subblock SBÿ among all the data subblocks with i= 1 to N and j= 1 to J.

[0250] Implementing the J primary combinations 20ÿ for i given with j= 1 to J allows generating the block B; of images IEi r

[0251] The implementation of the N sets, for i= 1 to i, of J primary combinations 20ÿ for i given with j= 1 to J allows the generation of the N blocks B; of images lEÿj with i= 1 to N and j= 1 to J.

[0252] The primary combination 20ÿ allows passage from time space to distance space. The elementary image is, for example, two-dimensional or three-dimensional.

[0253] According to one embodiment, the primary combination 20ÿ is carried out so as to generate elementary images IE;, j (with i = 1 to i and j = 1 to J) in B-Mode.

[0254] Non-limiting examples of this process are listed below.

[0255] According to one embodiment, the primary combination 20ÿ of the data from the data subblock SBÿ to generate an elementary image IE;, j, comprises the combination of data from at least one elementary dataset RFijhk acquired for index i and index j of the data subblock SBÿ.

[0256] According to one embodiment, the primary combination 20ÿ of the data from the data subblock SBÿ to generate an elementary image IE;, j comprises the combination data from several elementary datasets RFijhk acquired for index i and index j of the sub-block of data SBÿ.

[0257] According to one embodiment, the primary combination 20ÿ of the data from the data subblock SBÿ to generate an elementary image IE;, j comprises the combination of data from each elementary dataset RFijhkacquis for index i and index j of the data subblock SBÿ.

[0258] According to one embodiment, the primary combination 20ÿ includes a beamforming step, called “beamforming” in Anglo-Saxon terminology.

[0259] In one embodiment, the channel formation step is carried out by the method called Delay and Sum, better known by its Anglo-Saxon name, "Delay & Sum".

[0260] For example, in the case of 2D plane wave imaging where there is only one sub-aperture defined by transducers aligned along a probe axis. The different beams Wk are plane waves emitted at respective angles ak with respect to a normal direction. The elementary images IE;, j are generated by combining signals acquired by the transducers delayed by delays r(yy\ak):

[0261] lE^x, j) = Y^RF^^ J - «J)

[0262] Where ).^008(^.40^11¾ =-----c-----+---ê---

[0264] IEu(z, x) = La^Fijak(t(x, x,

[0265] Where y is a coordinate of a point along the axis of the probe in a frame attached to the probe; x is a coordinate of a point along the normal to the axis of the probe in the frame attached to the probe and y' is a coordinate of the transducer along the axis of the probe in the frame attached to the probe.

[0266] The sum is performed on the coordinates y' of the different transducers and on the respective angles ak with k = 1 to K.

[0267] The sum is performed only on the coordinates y' in the case of a single angle a.

[0268] In the case of several transmit / receive configurations, the elementary images IEi, j are, for example, generated by combining signals acquired by the transducers delayed by the delays t ( y, y', ) in the following way:

[0269] iEi .^ y) = ^y. hRFijhak(T(y, y ak))

[0270] The sum is performed on the coordinates y' of the different transducers, on the respective angles ak and on the different transmission / reception configurations with h = 1 to H.

[0271] Alternatively, combining the data from the subblock SBÿj to generate the elementary image IE;, j can be achieved by other methods based on mathematical models, for example, by path formation in Fourier space, using adaptive or multivariate methods. Alternatively, it can be achieved by a learning method using, for example, a neural network trained to reconstruct elementary images from the subblock data.

[0272] In a particular embodiment, the elementary acquisition step seh comprises the implementation of several individual acquisition sequences sihk with k = 1 to K and K is an integer greater than 1. These individual acquisition sequences si^ differ in that spatial and / or temporal characteristics of the beams emitted during their respective emission steps E^ differ as explained previously.

[0273] The primary combination 20ÿ of the data from the subblock SB.ylc so as to generate the elementary image IE;, j comprises, for example, several channel-forming steps, the respective channel-forming steps using data acquired under the effect of the emission of respective beams, during the acquisition of the data subblock SBÿ, so as to form several intermediate images and a combination step, for example averaging, of these intermediate images so as to form the elementary image. This process is known by the English name "compounding" or "coherent compounding". It makes it possible to increase the contrast and resolution of the images. Combining a plurality of images makes it possible to limit noise and thus increase contrast. Combining images acquired by means of distinct beams makes it possible to increase resolution.

[0274] Alternatively, K is equal to 1.

[0275] Alternatively and / or in addition, the acquisition step sbj of a subblock SBÿ comprises the implementation of several elementary acquisition sequences seh with h = 1 to H and H is an integer greater than 1. These elementary acquisition sequences seh differ in the transmit / receive configurations Ch used. This type of method makes it possible to address probes with a greater number of transducers than the number of available electronic processing channels.

[0276] In this case, the primary combination 20ÿ of the data from the subblock SBÿjde so as to generate the elementary image IE; j may include a known step of concatenating the data acquired with the different transmit / receive configurations so as to reconstruct the entire field of view. This step is implemented prior to the channel formation step(s).

[0277] The primary combination 20ÿ of the data from the subblock SB^dc sort to generate the elementary image IE;, j includes, for example, a step of concatenating the data acquired by means of the different elementary sequences.

[0278] Alternatively, H= 1. System

[0279] In [Fig.9], a block diagram of the SYS system is shown according to an example embodiment of the invention configured to implement the method according to the invention.

[0280] The SYS system includes a processing system which is a DT processing device in the non-limiting example of [Fig.9].

[0281] Alternatively, the processing system includes various elements capable of communicating by wired or wireless means.

[0282] According to one embodiment, the SYS system includes an SA acquisition system.

[0283] The SA acquisition system comprises a probe S and an acquisition device DA.

[0284] The probe S includes an RE array of TR transducers.

[0285] The TR transducer array can be one-dimensional. The TR transducers are then arranged in a line. The line is, for example, a straight line, in which case the array is linear, or curved. Alternatively, as in the example in [Fig. 9], the TR transducers are arranged in rows and columns on a flat or curved surface.

[0286] According to one embodiment, the TR transducers are regularly distributed in space.

[0287] One variant consists of distributing the TR transducers randomly in space to form a sparse probe. Another variant consists of using probes called RCAs for Raw-Column Arrays, where the elements of the same row and column are connected together.

[0288] The DA acquisition device comprises an EM transmitter, a CTR controller, an optional MUX multiplexer, an MPR preprocessing module including an analog-to-digital converter (ADC), a first MT memory, and a COL probe communication system

[0289] The DA acquisition system is configured to implement acquisition step 10 of the process according to the invention when the latter includes acquisition step A.

[0290] The MUX multiplexer allows for selective addressing of the transmit and receive sub-openings.

[0291] When excited by the EM transmitter, via the MUX multiplexer, each TR transducer emits an ultrasonic pulse.

[0292] The CTR controller is capable of controlling the other elements of the DA acquisition device.

[0293] The CTR controller is configured to control, at each emission step Ehk: - the EM transmitter so that it generates, for the TR transducers, a defined excitation so that the emission sub-aperture SOo, receiving the excitation, emits an ultrasonic beam Wk defined by its temporal and spatial properties, - the MUX multiplexer so that the excitation is transmitted to the transducers of the emission sub-aperture SOo.

[0294] For example, the EM emitter generates an excitation signal defining a waveform and a predetermined wave frequency and applies different delays to this signal so as to generate respective elementary excitation signals intended to excite the respective TR transducers of the emitting sub-aperture SOo so as to define a direction of the beam Wk intended to be emitted by the sub-aperture SOo.

[0295] The CTR controller controls the switch configuration of the MUX multiplexer so that the multiplexer transmits the respective elementary excitation signals to the respective TR transducers of the emission sub-aperture SOo so that the TR transducer array emits the emission beam Wk.

[0296] The CTR controller is configured to control, at each reception stage R^, the multiplexer MUX so that only the TR transducers of the receiving sub-aperture SOo' transmit the electrical signals generated by the emission of the ultrasonic beam Wk to the pre-processing device MPR.

[0297] The MPR preprocessing device may further include, by way of example, at least one filter and / or a demodulator and / or a time gain compensator and / or a sampler.

[0298] The CTR controller is programmed to control elements of the DA acquisition device so that the latter implements acquisition step 10.

[0299] In one embodiment, the CTR controller includes, for example, a set of at least one processor operationally coupled to a memory in which is stored a program executed by the CTR controller so that the DA acquisition system implements the acquisition step 10.

[0300] The multiplexer MUX transmits the signals received by the TR transducers of the receiving sub-aperture SOo' to the pre-processing device MPR, which includes the analog-to-digital converter (ADC), to generate the raw digital RFijhk data sets from the echo signals. These data sets are recorded in the MT memory of the ACQ acquisition device during a storage step.

[0301] The DA acquisition device includes a set of at least one COI communication system enabling the DA acquisition device to communicate with the probe S and with a set of at least one CO2 communication system of the device or DT processing system so as to enable the transmission of data from the MT memory of the ACQ acquisition device to the DT processing device, for example to a second MDT memory of the DT processing device.

[0302] The CTR controller is capable of controlling the COI communication system of the DA acquisition device so that it transmits the data to the CO2 communication system of the device or DT processing system.

[0303] The processing system, for example the processing device DT, includes the second memory MDT, a processing unit UT and a user interface INT comprising an input interface INTE and an output interface INTS.

[0304] The processing unit UT is configured to implement the processing step T or at least the generation step of at least one perfusion indicator of the process.

[0305] In one embodiment, the processing unit UT comprises, for example, a set of at least one processor operationally coupled to a memory in which is stored a program executed by the processing unit UT so that the processing unit UT implements the processing step T or at least step of the processing step, for example, the step of generating at least one perfusion indicator of the process.

[0306] According to one embodiment, the processing unit UT and / or the controller CTR are configured to synchronize the operations performed by the acquisition system SA and the processing system so as to implement the process according to the invention.

[0307] The data generated during implementation during processing is advantageously stored in the second MDT memory or in one or more other memories of the processing system which may be a DT processing device.

[0308] This data can then be transferred into an internal memory of the DT processing system, for example the DT processing device.

[0309] According to one embodiment, the DT processing system is configured to implement the display step of at least one generator and the UT processing unit is configured to generate the perfusion indicators and to display information representative of at least one indicator on at least one screen of the INTI output interface.

[0310] The same applies to the generation of the data to be displayed during the possible step of displaying an image on a screen of the output interface INTS to allow manual selection of the reference area via the input interface INTE.

[0311] According to one embodiment, the DT processing system is configured to implement the display step of at least one generator and the UT processing unit is configured to generate the data to be displayed during this step and display this data on the INTI output interface.

[0312] The system, in particular the memories and the processing unit(s) and the communication systems, are configured, in particular sized, to allow the implementation of the process according to the invention. This configuration is, for example, carried out experimentally. Material

[0313] From a hardware point of view, the processing system and the CTR controller can be seen as computers interacting with computer programs.

[0314] The DT processing system and the DA acquisition device include at least one computer, for example, a microcomputer, a computer network, an electronic component, a tablet, a smartphone or a personal digital assistant (PDA).

[0315] The data processing unit UT and the controller CTR each include, for example, a computer, comprising a set of at least one processor, and optionally a memory operationally coupled to the computer.

[0316] The memory includes, for example, a computer-readable medium. The computer-readable medium is a tangible device readable by a reader of the processing unit, capable of storing electronic instructions and of being coupled to the COI, CO2 communication system.

[0317] In other words, the computer-readable medium is a tangible medium. That is to say, it is not a transient signal in itself, such as radio waves or other freely propagating electromagnetic waves, such as light pulses or electronic signals. Such a computer-readable storage medium is, for example, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof.

[0318] By way of example, the readable medium is an optical disc, a magneto-optical disc, a read-only memory (ROM), an erasable and programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a random access memory (RAM), a magnetic card or an optical card.

[0319] Each of the first MT memory and second MDT memory can be of one of the aforementioned types.

[0320] The readable medium may include an operating system and load the programs according to the invention. It includes registers adapted to record Parameter variables are created and modified during the execution of the aforementioned programs. A computer program containing software instructions is then stored on the readable medium.

[0321] Alternatively, the program instructions are taken from an external source and downloaded via a network. This is particularly the case for applications.

[0322] The data processing unit UT and the controller CTR each comprise a computer, that is to say at least an electronic data processing circuit designed to manipulate and / or transform data represented by electronic or physical quantities in registers of the evaluation system and / or memories into other similar data corresponding to physical data in register memories or other types of display devices, transmission devices or storage devices.

[0323] The data processing unit UT and / or the controller CTR include, for example, memories for storing data, operationally coupled to the data processing circuit and a reader adapted to read computer-readable media.

[0324] The steps of the method according to the invention are, for example, carried out by bringing the processing circuits of the data processing unit UT and the controller CTR to read predetermined programs stored on materials such as memories in such a way that their data processing circuits perform calculations, control communications and read and / or write data into memories.

[0325] The processing step is, for example, carried out on a processing device, for example a single computer, or on a system distributed between several computers (in particular via the use of cloud computing).

[0326] The data processing unit UT and the controller CTR each comprise at least one computer comprising at least the following elements: a set of one or more processors (for example at least one central processing unit (CPU) and / or at least one graphics processing unit (GPU) and / or a microcontroller and / or a digital signal processor (DSP)) capable of interpreting instructions in the form of a computer program and / or a hardware element, such as an electronic board, in which steps of the process according to the invention are implemented in hardware elements.

[0327] In a particular embodiment of the invention, the processing unit UT comprises a graphics processing unit (GPU). Alternatively, the processing unit comprises a central processing unit (CPU).

[0328] The invention relates to a computer program product comprising the computer-readable medium containing instructions which, when executed by the processing circuit, cause the SYS system or the DT processing system to implement the steps of the process according to the invention, that is to say to execute the functional building blocks of the system according to the invention.

[0329] The product-program may include the computer-readable recording medium.

[0330] Alternatively, the program instructions are obtained from an external source and downloaded via a network. This is particularly the case for applications. In this case, the computer program product includes a computer-readable data carrier on which the program instructions are stored or a data carrier signal on which the program instructions are encoded.

[0331] The form of program instructions is, for example, a form of source code, a computer-executable form, or any intermediate form between source code and a computer-executable form, such as the form resulting from the conversion of the source code via an interpreter, assembler, compiler, linker, or locator. Alternatively, program instructions are microcode, firmware instructions, state definition data, integrated circuit configuration data (e.g., VHDL), or object code. Program instructions are written in any combination of one or more programming languages, for example, an object-oriented programming language (C++, Java, Python), a procedural programming language (e.g., C).

[0332] COI, CO2 communication systems enable communication between system elements and optionally between at least one system element and a device external to the system. The communication systems can establish a physical link between system elements and / or between a system element and a device external to the system and / or a remote (wireless) communication link between system elements and / or between a system element and a device external to the system.

[0333] Communication systems may include any hardware, firmware, and / or software suitable for communicating information between elements of the device to which the communication system belongs, for example, via a data bus, or to an element external to the device. To enable data communication between different devices to which the communication systems belong, these systems include hardware, firmware, and / or software for establishing a wired or wireless communication link between them, for example, Wi-Fi, Bluetooth, cellular, or Ethernet.

[0334] The INT user interface allows a user to enter data or commands so as to be able to interact with the programs according to the invention.

[0335] The INT user interface includes, for example, an INTS output interface and an INTE input interface.

[0336] The input interface INTE includes, for example, a keyboard or a pointing interface, such as a mouse, a light pen, a touchpad, a remote control, a speech recognition device, a haptic device.

[0337] The INTE input interface allows, for example, a user to enter data or commands in order to be able to interact with the programs according to the invention.

[0338] For example, the input interface INTE allows a user to select a reference area ZR on an image displayed on a screen of the output interface INTS.

[0339] The INTS output interface is designed to provide information to a user, either sensorially or electrically, such as, for example, visually or audibly. The output interface includes, for example, a display or screen. The display step 200 can then be a step for providing information from data generated during the processing of order i blocks by the INTS output interface, by means other than a display.

[0340] The output interface INTS can be the input device INTE, for example, in the case of a touch tablet.

Claims

Demands

1. Perfusion imaging method in a region of interest (R) of an individual, the method comprising a computer-implemented processing method, the processing method comprising: • Generating intensity-time curves from pixel / voxel intensities of N blocks (B;) of J elementary (1¾) ultrasound images of the region of interest (R) generated from raw ultrasound data, • Determining, from the intensity-time curves, a reference intensity-time curve of a predetermined reference area of ​​the region of interest (R), • Generating at least one perfusion indicator from the reference intensity-time curve.

2. Method according to the preceding claim, wherein the processing method comprises: - displaying information representative of a perfusion indicator on a screen.

3. A method according to any one of the preceding claims, wherein the J elementary images of each block (Bi) are generated from J subblocks of ultrasonic raw data acquired sequentially, each elementary image (11¾) being generated from a single subblock taken from the subblocks of ultrasonic raw data and the N blocks being generated from N blocks of J subblocks of raw data, the N blocks of J subblocks of raw data being acquired sequentially and each block of J elementary images being generated from a single block of subblocks of raw data.

4. Method according to claim 3, wherein, generating the intensity-time curves comprises, for each of a plurality of N blocks of J elementary images, generating a combined image (ICi) of the region of interest (R) by combining J images representative of the J elementary images of said block.

5. Method according to the preceding claim, wherein the combination comprises the calculation of a temporal and / or spatial cross combination of the J representative images of the J elementary images of the block.

6. A method according to any one of claims 4 to 5, wherein generating at least one perfusion indicator comprises deconvolving at least one of the intensity-time curves from the reference intensity-time curve.

7. A method according to any one of claims 4 to 6, wherein generating intensity-time curves comprises: • enhancing the signals from a contrast agent relative to the signals from surrounding tissues so as to generate N blocks of J enhanced images from the N blocks of J elementary images such that the images representing the J elementary images are representative of the J enhanced images.

8. The method of the preceding claim, wherein, generating the intensity-time curves comprises: • generating N blocks of J refined images from the N blocks of J enhanced images such that the refined images have a narrower point spread function than the enhanced images so that the representative images of the J elementary images are representative of the J refined images.

9. A method according to any one of the preceding claims, comprising: • Acquiring, by means of an acquisition system comprising a transducer array, the raw ultrasonic data, • Generating the N blocks of J elementary images from the raw ultrasonic data.

10. A method according to the preceding claim, wherein the J blocks of raw data subblocks are acquired at a frequency between 5 Hz and 500 Hz and wherein the raw data subblocks are acquired at a frequency between 200 and 20,000 Hz.

11. Perfusion imaging system configured to implement the process according to any one of the preceding claims, the system comprising a processing system (DT) comprising a processing unit comprising an assembly of at least one processor, the processing unit being configured to implement the processing process (T).

12. System according to the preceding claim, comprising an acquisition system (AS) comprising an array (AR) of transducers (TR), the acquisition system being configured to acquire the raw ultrasonic data, the processing system being configured to generate the N elementary image blocks from the raw ultrasonic data.

13. Product computer program comprising instructions which lead the system according to any one of claims 11 to 12, to perform the steps of the process according to claim 1.

14. Computer-readable medium on which the computer program according to claim 13 is recorded.