Method of deblurring an image resulting from an acquisition sequence by a medical imaging device

Abstract

The invention relates to a method (100) for deblurring an experimental medical image (Mn) resulting from an acquisition sequence by a medical imaging device (1) by: calculating the respective Fourier transforms (TFMn, TFM0) of said experimental image (Mn) and of a reference image (M0) of the same resolution; determining the respective moduli (YMn, YM0) of the Fourier transforms of the experimental image and of the reference image; obtaining a deblurring mask (D) by calculating the ratio of the moduli (YMn, YM0); applying said ratio (D) to the Fourier transform of the experimental image (TFMn) by multiplication, resulting in a corrected Fourier transform (TFMnc); obtaining the deblurred experimental image (Mnc) by applying the inverse Fourier transform to said corrected Fourier transform (TFMnc). Figure for the abstract: Fig 4

Description

Title of the invention: Method for deblurring an image resulting from an acquisition sequence by a medical imaging device

[0001] The present invention relates, in general, to image processing in the field of medical imaging. The invention describes more particularly a method for correcting blur or deblurring, otherwise known as "deblurring" in English terminology, of a medical image resulting from an acquisition sequence via a medical imaging device.

[0002] In medical imaging, an acquisition sequence makes it possible to collect and store raw data on the anatomy and / or on the functioning of an organ making it possible to reconstruct a three-dimensional visual representation of said organ, in other words an image of said organ. Such acquisitions can induce, in the frequency domain, a loss or a filtering of certain frequencies, high and / or low, inducing a blur on said image.

[0003] Although the invention can be applied to all techniques or modalities based on imaging and in which a resulting image would have undergone the virtual application of a filter strongly attenuating or eliminating certain frequencies in the frequency domain, we will mainly distinguish in the remainder of the present application, by way of illustration, magnetic resonance imaging, better known by the acronym MRI or the English acronym MRI for "Magnetic Resonance Imaging", and in particular perfusion MRI and more precisely perfusion by arterial spin labeling, known by the acronym ASL for perfusion by "arterial spin labeling", according to English terminology.

[0004] Magnetic resonance imaging is based on an analysis of the response of the proton of a water molecule when it is excited in a magnetic field. This response depends on the environment of such a proton and thus makes it possible to differentiate different types of tissue. A Nuclear Magnetic Resonance imaging device, such as the device 1 of a SAIM medical imaging analysis system illustrated by way of non-limiting example by Figures 1 and 2, is generally used. This delivers a plurality of digital image sequences 12 of one or more parts of a patient's body, by way of non-limiting examples, the brain, the heart, the lungs. The MRI acquisition is done by sampling a parallelepiped volume in a given section plane. [Fig.3] illustrates the section planes most commonly used in medical imaging: View A: frontal projection or coronal (or frontal) section, the patient's right is presented on the left in the image: as if the observer were looking simply the patient from the front; - View B: on an axial (or transverse) view, the patient's right is always located on the left in the image: as if the observer were looking at the patient - lying on his back - from his feet; - View C: In a sagittal or profile view, the image is presented with the anterior part of the patient on the left, as if the observer were looking at the patient from the left profile.

[0005] Said apparatus 1 applies for this purpose a combination of high-frequency electromagnetic waves to the part of the body considered and measures the signal re-emitted by certain atoms, such as, by way of non-limiting example, hydrogen for Nuclear Magnetic Resonance imaging. The apparatus 1 thus makes it possible to determine the magnetic properties and, consequently, the chemical composition of the biological tissues and therefore their nature, in each elementary volume of interest, which is commonly called a voxel, of the imaged volume. As indicated in [Fig.l], a Nuclear Magnetic Resonance imaging apparatus 1 is controlled using a console 2. A user 6, for example an operator, practitioner or researcher, can thus choose commands 11 to control the apparatus 1, from parameters or instructions 16 entered via a human-machine input interface 8 of the analysis system.Such a human-machine interface 8 may consist, for example, of a computer keyboard, a pointing device, a touch screen, a microphone or, more generally, any interface arranged to translate a gesture or an instruction issued by a human 6 into control or configuration data. From information 10 produced by said device 1, a plurality of sequences of digital images 12 of a part of a human or animal body are obtained. We will also call such information 10 or images 12 “experimental data”.

[0006] The image sequences 12 may optionally be stored within a server 3, i.e. a computer equipped with its own storage means, and constitute a medical file 13 of a patient. Such a file 13 may comprise images of different types, such as structural images highlighting the activity of the tissues or anatomical images reflecting the properties of the tissues. The image sequences 12 or, more generally, the experimental data, are analyzed by a processing unit 4 arranged for this purpose. Such a processing unit 4 may, for example, consist of one or more microprocessors or microcontrollers implementing suitable application program instructions loaded into storage means of said imaging analysis system. The term “storage means” means any volatile or, advantageously, non-volatile computer memory. A non-volatile memory is a computer memory whose technology allows its data to be retained in the absence of an electrical power supply. It can contain data resulting from inputs, calculations, measurements and / or program instructions. The main non-volatile memories currently available are electrically writable, such as EPROM (Erasable Programmable Read-Only Memory) or electrically writable and erasable, such as EEPROM (Electrically-Erasable Programmable Read-Only Memory)\ flash, SSD (Solid-State Drive), etc. Non-volatile memories are distinguished from so-called "volatile" memories, the data of which is lost in the absence of an electrical power supply.The main volatile memories currently available are of the RAM type ("Random Access Memory" in English terminology or also called "live memories"), DRAM (dynamic random access memory, requiring regular updating), SRAM (static random access memory requiring such updating during a power shortage), DPRAM or VRAM (particularly suitable for video), etc. A "data memory", in the rest of the document, can be volatile or non-volatile depending on the intended application.

[0007] Said processing unit 4 comprises means for communicating with the outside world to collect the images. Said means for communicating further allow the processing unit 4 to deliver or output, ultimately, a rendering, for example graphic and / or sound, of an estimation or quantification of a biomarker or a pharmacokinetic parameter QI developed by said processing unit 4 from the experimental data 10 and / or 12 obtained by Magnetic Resonance Imaging, to a user 6 of the imaging analysis system via an output human-machine interface 5.Throughout the document, the term "output human-machine interface" means any device, used alone or in combination, making it possible to output or deliver a graphic, haptic, audio or, more generally, human-perceptible representation of a reconstructed physiological signal, in this case a biomarker, to a user 6 of a Magnetic Resonance Imaging analysis system. Such an output human-machine interface 5 may consist, in a non-exhaustive manner, of one or more screens, speakers or other suitable alternative means. Said user 6 of the analysis system can thus confirm or deny a diagnosis, decide on a therapeutic action that he deems appropriate, deepen research work, refine adjustment parameters of measuring equipment, etc.Optionally, this user 6 can also configure the operation of the processing unit 4 or the output human-machine interface 5, by means of operating and / or acquisition parameters 16. For example, he can thus define display thresholds or choose the biomarkers, indicators or estimated or quantified parameters for which he wishes to have a . representation. The user uses for this the input human-machine interface 8 previously mentioned or a second input interface provided for this. Advantageously, the input 8 and output 5 human-machine interfaces may constitute a single physical entity. Said input 8 and output 5 human-machine interfaces of the imaging analysis system may also be integrated into the acquisition console 2. There is a variant, described in connection with [Fig. 2], for which an imaging system, as described previously, further comprises a preprocessing unit 7 for analyzing the image sequences 12, deducing experimental signals 15 therefrom and delivering the latter to the processing unit 4 which is thus relieved of this task.

[0008] More specifically, perfusion MRI provides access to information on the capillary microcirculation of tissues by analyzing their microcirculation. The essential quantitative parameters evaluated are blood volumes and temporal data (such as transit time, time to peak contrast, etc.). The final objective of perfusion MRI is to measure, or estimate, the blood flow irrigating the organ being explored, expressed in milliliters / minute / 100 grams of tissue, corresponding to tissue perfusion. The differentiation of perfused and non-perfused tissues is based on the use of an intravascular marker: - exogenous in a first application case: injected contrast product; - endogenous in a second application case: by marking the hydrogen nuclei of the water in the bloodstream.

[0009] In this respect, ASL perfusion imaging is placed in this second application case to measure cerebral perfusion, that is to say the blood supply to the cerebral tissues, without the use of contrast agents or possible pharmacological side effects. This technique is thus non-invasive, which makes its acquisition faster and allows it to be repeated. ASL makes it possible to highlight perfusion abnormalities in numerous pathologies, in particular encephalic pathologies, in a completely non-invasive manner, which makes it a particularly interesting technique from a clinical point of view. It is a differential technique during which two acquisitions are carried out: an acquisition with labeling of arterial protons and a control acquisition. ASL perfusion imaging therefore quantifies perfusion in the brain by labeling the hydrogen nuclei of the arterial intravascular water with one or more radiofrequency pulses.These pulses are used to reverse the longitudinal magnetization of arterial blood, as the blood flows through a single plane (or tissue plate) just upstream of the region of interest, whose measurable magnetization and proton relaxation time Tl (time for protons to return to equilibrium) will thus be modified. This process is called "tagging" and involves converting the water present in the blood flowing through the arteries and through the neck into a . endogenous tracer. Thus, after a delay allowing the blood to circulate from the labeling site to its final exchange point with the brain, called arterial transit time, otherwise known by the acronym ATT for "Arterial Transit Time", images are acquired and contain signals coming from both the "labeled" water and the water from the static tissues. Also associated with this process is a so-called post-labeling delay, otherwise known by the acronym PLD for "Post Labeling Delay", defining the time between labeling and the acquisition of an image. The second control acquisition, called the "control" image, is carried out without prior arterial labeling: the arterial protons in the region of interest are then at equilibrium, completely relaxed. The subtraction of the labeling acquisition and the control acquisition cancels the signal from the static tissues and allows to obtain an image weighted according to the perfusion.It should be noted that the signal difference is of the order of a few percent, hence a low signal-to-noise ratio. It is therefore necessary to acquire several dozen pairs of marked images and "control" images, then to average them to obtain a satisfactory signal-to-noise ratio.

[0010] Thus, data acquisition using ASL technique requires two main steps: the labeling of protons in circulating blood and the acquisition of images. Regarding proton labeling, there are three main types: - continuous marking (known by the English acronym CASL), marking is carried out continuously at the level of the vessels of the neck, this technique allows obtaining a good signal / noise ratio but generates a high energy deposition in the tissues; - pulsed marking (known by the English acronym PASL) uses brief radiofrequency pulses limiting the deposition of energy in the tissues; - and pseudo-continuous marking (known by the English acronym pCASL) uses trains of very short duration radiofrequency pulses.

[0011] Concerning the acquisition of images, as previously specified, it is necessary to acquire numerous pairs of labeled images and “control” images. Due to a satisfactory signal / noise ratio and an acquisition speed limiting movement artifacts, the “echo-planar imaging” technique, known by the acronym EPI, is mainly used. However, in order to improve the quality of the images, 3D sequences have been developed. It is possible to use ultra-fast “single-shot” 3D sequences, combining gradient echo and spin-echo acquisition (known by the acronym GRASE). The use of such sequences facilitates the suppression of the signal from static tissues.

[0012] Nevertheless, due to an echo time, representing the time interval between the excitation of the protons and the occurrence of the signal, which is too long in relation to the reduction of the T2 relaxation time, i.e. the phase shift time of the protons, the ASL images can be affected by a significant blur, visible both in the plane and through the plane, a phenomenon all the more marked with the GRASE sequence, a phenomenon mentioned, for example, in the scientific article “Reducing blurring artifacts in 3D-GRASE ASL by integrating new acquisition and analysis strategies” published in Proceedings of the International Society for Magnetic Resonance in Medicine volume 22 (2014), page 2704. Such blur acts as a low-pass filter in the frequency domain, letting through low-frequency values ​​and cutting off all high-frequency values.Thus, the distinction between different brain tissues, such as white matter, gray matter, tumor, and cerebrospinal fluid (CSF), becomes difficult. Blurring is particularly problematic at the boundary between gray and white matter, which have different perfusion levels (long transit time in white matter significantly affects labeling) and for which white matter signals are affected by gray matter signals.

[0013] In order to address this blurring issue in an experimental medical image, it is known to use blur removal methods. Such methods generally perform a deconvolution in the image domain after estimating the deconvolution kernel in the frequency domain, we can notably cite the work of Chappell et. Al with reference to the scientific article “Reducing blurring artifacts in 3D-GRASE ASL by integrating new acquisition and analysis strategies” published in Proceedings of the International Society for Magnetic Resonance in Medicine volume 22 (2014), page 2704. In addition, any two-dimensional image blur removal method could potentially be applied to said experimental image, by applying said method to each slice of the image independently.In this regard, the work of Wang and Tao presents a series of deblurring techniques in the context of natural image deblurring in the scientific article “Recent progress in image deblurring” published in arXiv: 1409.6838.

[0014] However, such blur removal methods focus on correcting blur crossing only the plane. Thus, the development of a method for removing and / or correcting three-dimensional blur (simultaneously correcting blur in the plane and across the plane) of an image resulting from an acquisition sequence via a medical imaging device is of great interest. As such, such a method makes it possible to remove blurry artifacts from images in order to improve their readability and facilitate their interpretation with a view to exploiting such images in the field clinical to ultimately characterize different lesions with altered metabolic properties such as tumors, ischemic tissues, or multiple sclerosis.

[0015] To this end, the invention provides a method for deblurring an experimental medical image resulting from an acquisition sequence by an imaging device describing a plurality of elementary volumes of interest of an organ of a patient, called "voxels", said method being implemented by a processing unit of a medical imaging analysis system. Such a method comprises: - a step of calculating the Fourier transform of said experimental image; - a step of calculating the Fourier transform of a reference image of the same resolution as the experimental image; - a step of determining a first module of the Fourier transform of the experimental image and a second module of the Fourier transform of the reference image; - a step of obtaining a deblurring mask consisting of calculating the ratio between the first and second modules; - a step of applying said ratio to the Fourier transform of the experimental image by multiplication to obtain a corrected Fourier transform; - a step of obtaining a deblurred experimental image consisting of applying the inverse Fourier transform to said corrected Fourier transform.

[0016] In a particular embodiment of the invention, said reference image may come from an atlas, a control patient or an artificial anatomical reference.

[0017] In a preferred embodiment of the invention, said method may comprise a step prior to the Fourier transform calculation steps consisting of a step of acquiring a structural image of said patient of the same resolution as said experimental image. Said structural image then corresponds to said reference image.

[0018] Preferably, in order to ensure voxel-by-voxel correspondence of the reference image with respect to the experimental image, the method according to the invention may comprise a step prior to the Fourier transform calculation steps consisting of a step of resetting the experimental image with respect to the reference image.

[0019] According to an advantageous embodiment, said method may comprise a subsidiary step prior to the step of obtaining the deblurred experimental image and subsequent to the step of obtaining a deblurring mask consisting of applying a smoothing filter to said deblurring mask.

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[0027] According to the previous embodiment, the smoothing filter may be a Gaussian filter. Preferably, the Fourier transforms of the experimental image and the reference image are respectively calculated with the fast Fourier transform algorithm. According to a preferred embodiment, the experimental image acquisition sequence is a perfusion acquisition sequence by a magnetic resonance medical imaging device during which an endogenous tracer circulates within said plurality of elementary volumes of interest. According to a second object, the invention relates to a computer program product comprising one or more program instructions executable by the processing unit of a computer, said program instructions being loadable into a non-volatile memory of said computer and the execution of which by said processing unit causes the implementation of a post-processing method according to the invention. According to a third object, the invention relates to a computer-readable storage medium comprising the instructions of such a computer program product. Finally, the invention further relates to a medical imaging analysis system comprising a processing unit arranged to deblur an experimental medical image resulting from an acquisition sequence by a medical imaging device in connection with a plurality of elementary volumes of interest, called voxels, said processing unit being configured to: - calculate the Fourier transform of said experimental image; - calculate the Fourier transform of a reference image of the same re solution than the experimental image; - determine a first modulus of the Fourier transform of the experimental image and a second modulus of the Fourier transform of the reference image; - obtain a deblurring mask by calculating the ratio between the first and second modules; - apply said ratio to the Fourier transform of the experimental image by multiplication to obtain a corrected Fourier transform; - obtain the deblurred experimental image by applying the inverse Fourier transform on said corrected Fourier transform. The invention will be better understood and other characteristics and advantages thereof will appear on reading the following description of particular embodiments of the invention, given as illustrative and non-limiting examples, and referring to the appended drawings, among which: [Fig.l], already described, illustrates a simplified description of a system analysis of images obtained by magnetic resonance,

[0028] [Fig.2], already described, illustrates a simplified description of a variant of a system for analyzing images obtained by magnetic resonance,

[0029] [Fig.3] already described, illustrates the cutting planes most commonly used in medical imaging,

[0030] [Fig.4] illustrates an example of a functional algorithm of a deblurring method according to the invention,

[0031] [Fig.5] illustrates variant embodiments of a functional algorithm of a deblurring method according to the invention,

[0032] [Fig.6] illustrates a gain resulting from the implementation of a deblurring method 100 in accordance with the invention,

[0033] [Fig.7] illustrates a gain resulting from the implementation of a deblurring method 100 in accordance with the invention.

[0034] In order to simplify the description, the same reference is used in different figures to designate the same object, element or step. Thus, when the description cites a referenced object, element or step, this object, element or step may be identified in several figures. In addition, the figures as well as the description are given as non-limiting examples of embodiment.

[0035] As a preamble, within the meaning of the invention, an image is a BGR digital matrix representation of elements called “voxels”, of thickness k (along a transverse axis), BGR(i,j), i and j being indices of integer values ​​to identify the voxel located at row i and column j of the BGR matrix, i.e. a graphical representation in the form of a table or matrix, of BGR(i,j) elements each encoding a triplet of integer values ​​between zero and two hundred and fifty-five, according to the RGB color coding, acronym for “Red Green Blue”, also known by the acronym RGB, the Anglo-Saxon acronym for “Red Green Blue”. Such computer color coding is the closest to the hardware currently available to constitute human-machine output interfaces such as computer screens.In general, these reconstruct a color by additive synthesis from three primary colors, red, green and blue, forming on the screen a mosaic generally too small to be discriminated by a human eye. The RGB coding indicates a luminous intensity value for each of these primary colors. Such a value is generally coded on a byte and therefore belongs to a range of integer values ​​between zero and two hundred and fifty-five. Other color codings could alternatively be used. In this case, each voxel or element of the BGR table would describe a set of numerical values ​​adapted to said coding instead of the triplet mentioned above for RGB coding.

[0036] A method 100 for deblurring a medical image, in accordance with the invention and illustrated in [Fig.4], advantageously translates into the form of a computer program product whose program instructions are intended to be implanted in the program memory of an element of a medical imaging system, such as the system S according to figures 1 and 2, for example a computer or a computer server or, more generally, any electronic object having sufficient computing power.

[0037] [Fig. 4] thus illustrates a method 100, in accordance with the invention, for deblurring an experimental image Mn resulting from an acquisition sequence by an imaging device, such as the device 1 of the medical imaging analysis system S illustrated by FIGS. 1 and 2, in connection with a plurality of n voxels. Such an experimental image Mn can be considered as all or part of the “total” image resulting from said acquisition sequence. The image Mn can therefore be the total image or a portion of the total image representing an area of ​​interest. Such a method 100 can be implemented by the processing unit 4 of such a medical image analysis system S.

[0038] For illustrative but non-limiting purposes, such a method 100 according to the invention is advantageously described in the remainder of the present document and implemented for the processing of a medical image, representing the brain of a patient, resulting from a perfusion acquisition sequence by a medical magnetic resonance imaging device 1 during which an endogenous tracer passes within said plurality of n voxels.

[0039] Such a deblurring method 100 according to the invention consists of analyzing an experimental medical image Mn in the frequency domain and comparing said image Mn to a reference image MO of the same resolution, namely the same number of voxels per unit of length, as said image Mn in order to recover the lost or truncated frequencies of the experimental image Mn. Thus, said reference image MO represents an area of ​​interest similar to said image Mn, for example a brain as illustrated in [Fig.4], according to the same section plane (the section planes have been explained previously and illustrated in [Fig.3]) for example in coronal section as illustrated in [Fig.4].

[0040] In a preferred embodiment so that the deblurring method 100 is the most efficient, it is possible that said reference image MO can be a structural image of said patient. Such a structural image MO represents the same area of ​​interest and is of the same resolution as said experimental image Mn. In this case illustrated in [Fig.5], said method 100 may first comprise a step 101. Such a step 101 consists of a step of acquiring a structural image of said patient resulting from an acquisition sequence by a medical imaging device. The acquisition of the reference structural image MO can be carried out at the same time as the acquisition of the experimental image Mn by the same imaging device. medical, such as for example the magnetic resonance medical imaging device 1. By way of illustration but not limitation, the structural MO image of the patient can be: - a so-called "Tl-weighted" image, often used for anatomy, since such an image results from an acquisition sequence, called "anatomical", favoring the detection of low-mobile water, i.e. intracellular. For such an image, fat appears hyper-intense, light in color, and water hypo-intense, dark in color; - a so-called "T2-weighted" image, often used as a functional image, since such an image results from an acquisition sequence favoring the detection of mobile water, i.e. extracellular or intravascular. For such an image, the water appears hyper-intense, light in color and the fat appears a little darker than the water; - a so-called "FLAIR" image for "Fluid Attenuated Inversion Recovery" according to Anglo-Saxon terminology, very suitable for brain imaging, insofar as such an image results from an acquisition sequence suppressing the signal coming from the cerebrospinal fluid. For such an image, white matter lesions, softening (Cerebrovascular Accident), inflammatory demyelination (multiple sclerosis) appear hyper-intense and are particularly well highlighted. The invention cannot be limited to the use of this type of structural images. Any other structural image may be used to implement the method 100 according to the invention.

[0041] Alternatively or in addition, such a reference image MO may come from: - an atlas, namely a typical image resulting from an average of images acquired from various patient populations or from the same patient; - of a healthy subject otherwise called a “control” patient; - or even an artificial anatomical reference. The use of such images can be of interest, for example, when the patient's structural image is lost or accidentally erased, or when problems are encountered in acquiring the patient's structural image, due for example to movements of the patient during acquisition, these movements being able to be voluntary (for example in the case of claustrophobia) or involuntary (for example in the case of certain diseases such as Parkinson's disease).

[0042] In addition, in order to guarantee perfect alignment between the reference image MO and the experimental image Mn, optionally, as illustrated in [Fig.5], said method 100 may comprise a prior step 102 of resetting the experimental image Mn with respect to the reference image MO or vice versa of the experimental image MO reference image with respect to the experimental image Mn in order to ensure the correspondence of the two images MO and Mn voxel by voxel. Indeed, the closer the MO reference image is to the experimental image Mn, the better the deblurring result will be. Following such optional registration, the two images being of substantially the same dimensions are superimposable because they have common orientations.

[0043] As mentioned previously, the invention consists of analyzing an experimental medical image Mn in the frequency domain. Thus, as illustrated in Figures 4 and 5, said method 100 comprises first 110 and second 120 steps of calculating the Fourier transforms TFMn and TFM0 respectively of the experimental image Mn and the reference image MO. In this respect, the Fourier transform is a mathematical technique making it possible to determine the frequency spectrum of a signal resulting from an acquisition sequence. Said signal resulting from an acquisition sequence being a discrete signal, the discrete Fourier transform may be favored. Indeed, the analysis of discrete signals by discrete Fourier transform makes it possible to represent a discrete signal in the frequency domain: it is then possible to directly process the frequencies of the image.In this respect, to obtain the frequencies contained in said signal, an operation called transformation is applied and the result of this transformation is called transform.

[0044] In practice, to calculate a discrete Fourier transform, an algorithm called "fast Fourier transform" is advantageously used, known by the English acronym FFT for "Fast Fourier Transform", allowing a faster calculation. However, the invention cannot be limited to the use of this algorithm. Any other algorithm allowing the calculation of a Fourier transform may be used for the implementation of the method 100 according to the invention.

[0045] The images obtained by applying a Fourier transform give a complex image. Thus, in general, the modulus of the Fourier transform is calculated and represented. Such a modulus is calculated as being the square root of the sum of the square of the imaginary part and the square of the real part. In this respect, as illustrated in [Fig.4], said method 100 according to the invention comprises a step 130 of determining the modulus YMn of the Fourier transform TFMn of the experimental image Mn and the modulus YM0 of the Fourier transform TFM0 of the experimental image MO. Advantageously, such a step 130 may comprise a normalization processing of said moduli YMn and YM0 so that said moduli share a common scale of values. Such normalization is carried out for each modulus YMn and YM0 and consists for example in dividing the values ​​of each modulus YMn and YM0 respectively by their central frequency value, otherwise known as “zero frequency”.Alternatively, said normalization may consist of using any normalization system allowing the two modules YMn and YM0 to share the same scale of values. In . complement, each pixel of said module acting as a divider is preferentially non-zero to prevent any division by zero. For this, it may be considered to add a constant, such as, for example, a tenth or a hundredth, so as not to unbalance the scale.

[0046] Once said moduli YMn and YMO are determined, said method 100 comprises a step 140 of obtaining a deblurring mask D, as illustrated in [Fig.4]. Such a step 140 consists of calculating the ratio D between the moduli YMn and YMO. Preferably but not limitingly, the deblurring mask D or ratio D is equal to the division of the modulus YMO of the Fourier transform TFM0 of the reference image MO by the modulus YMn of the Fourier transform TFMn of the experimental image Mn. Advantageously, said step 140 may comprise a subsidiary processing of limiting the values ​​of said ratio or of said deblurring mask D consisting of clipping the values ​​of D to a maximum amplitude. Preferably, such a maximum amplitude is between 2 and 100, ideally 5 to 20 to avoid any risk of noise amplification. It should be noted that an amplitude of value 10 seems to be the ideal value.

[0047] Then, said method 100 comprises a step 150 of applying said deblurring mask or said ratio D to the Fourier transform TFMn of the experimental image Mn by multiplication. At the end of such a step 150, a corrected Fourier transform TFMnc is obtained. Thus, said deblurring mask D is applied to the entirety of the Fourier transform TFMn, i.e. to all the frequencies and therefore to the entirety of the experimental image Mn. Said step 150 could also only concern certain frequencies.

[0048] Finally, said method 100 comprises a final step 160 of obtaining the deblurred experimental image Mnc. Such a step 160 consists of applying the inverse Fourier transform to said corrected Fourier transform TFMnc. Indeed, the inverse Fourier transform is a mathematical operation making it possible to reconstruct the original image (the original signal) from its frequency representation. In the case of the invention, a deblurred experimental image Mnc is therefore obtained.

[0049] In addition, as illustrated in [Fig.5], said method 100 may comprise an optional step 142, prior to step 150 of applying said ratio D and subsequent to step 140 of obtaining a deblurring mask. Such a step 142 consists of applying a smoothing filter to said deblurring mask D (otherwise called said ratio D). At the end of such a step 142, a smoothed deblurring mask D1 is therefore obtained, making it possible to reduce artifacts. Such a smoothing operation 142 may advantageously consist of applying a Gaussian filter. Such a Gaussian filter preferably has a standard deviation of between 1 and 50, beyond a deviation- type of 50 the smoothing would lose all its interest, and ideally a standard deviation of value 10. The invention cannot be limited to the use of this type of smoothing filter nor even to the values ​​stated as ideal. Any other smoothing filter could be used for the implementation of the method 100 according to the invention. In addition, such a step 142 can advantageously consist of normalizing said ratio D. In this case, such normalization can consist for example of dividing the values ​​of said ratio D by the value of its central frequency, called zero frequency or consist of any other normalization technique.

[0050] Alternatively, if several experimental images Mnl, Mn2, ..., Mnx have been acquired and therefore available for processing, steps 110, 130 and 140 (as well as optional steps 101, 102 and / or 142 if necessary) are carried out independently for each experimental image in order to calculate for each an individual deblurring mask Dl, D2, ..., Dx. The final deblurring mask Df is then equal to the average, advantageously arithmetic, or even the median, of all the calculated individual deblurring masks Dl, D2, ..., Dx.

[0051] Figures 6 and 7 illustrate the benefit resulting from the implementation of a deblurring method 100 according to the invention. Indeed, to measure and illustrate the impact of such a deblurring method 100, [Fig.6] shows the difference in contrast between an experimental image Mn and the deblurred experimental image Mnc. It is also possible, as illustrated in [Fig.7], to calculate the respective moduli YMn and YMnc of the Fourier transforms of the experimental image Mn and the deblurred experimental image Mnc. Such modules YMn and YMnc are shown in [Fig.7] and the white dotted circles clearly highlight that frequencies lost in the case of the experimental image Mn have been restored in the case of the deblurred experimental image Mnc, the image representing the YMnc module appearing more contrasted than that representing the YMn module.

[0052] It will be appreciated by those skilled in the art that the present disclosure is not limited to what is particularly shown and described above. Other modifications may be envisaged without departing from the scope of the present invention defined by the appended claims. In particular, in the preferred example described above, the method 100 is implemented to deblur an image, representing the brain of a patient, resulting from an ASL perfusion acquisition sequence by a magnetic resonance medical imaging device.However, such a method 100 in accordance with the invention could be used in the context of all medical imaging techniques, as soon as an image presents a blur, for example for the PET technique (an English acronym for positron emission tomography), for which the quality of the acquired image can be limited for example by the non-collinearity of the pairs of emitted photons, intercrystalline diffusion, or even the penetration of crystals.

Claims

Claims

1. Method (100) for deblurring an experimental medical image (Mn) resulting from an acquisition sequence by a medical imaging device (1) describing a plurality of elementary volumes of interest of an organ of a patient, called "voxels", said method (100) being implemented by a processing unit (4) of a medical imaging analysis system (S), said method (100) comprising: - a step (110) of calculating the Fourier transform (TFMn) of said experimental image (Mn); - a step (120) of calculating the Fourier transform (TFM0) of a reference image (MO) of the same resolution as the experimental image (Mn); - a step of determining (130) a first module (YMn) of the Fourier transform (TFMn) of the experimental image (Mn) and a second module (YM0) of the Fourier transform (TFM0) of the reference image (MO);- a step of obtaining (140) a deblurring mask (D) consisting of calculating the ratio (D) between the first and second modules (YMn, YM0); - a step of applying (150) said ratio (D) to the Fourier transform (TFMn) of the experimental image (Mn) by multiplication to obtain a corrected Fourier transform (TFMnc); - a step of obtaining (160) a deblurred experimental image (Mnc) consisting of applying the inverse Fourier transform to said corrected Fourier transform (TFMnc).;

2. Method (100) according to the preceding claim, for which the reference image (MO) comes from an atlas, a control patient or an artificial anatomical reference.

3. Method (100) according to claim 1 comprising a step (101) prior to the steps (110, 120) of calculating the Fourier transform consisting of a step (101) of acquiring a structural image of said patient of the same resolution as the experimental image (Mn), said structural image corresponding to said reference image (MO).

4. Method (100) according to one of the preceding claims comprising a step (102) prior to the steps (110, 120) of calculating the Fourier transform consisting of a step (102) of resetting the experimental image (Mn) with respect to the reference image (MO) or of the reference image (MO) with respect to the experimental image (Mn) so as to ensure their voxel-by-voxel correspondence.

5. Method (100) according to one of the preceding claims comprising a step (142) prior to the step (150) of applying said ratio (D) and subsequent to the step (140) of obtaining a deblurring mask (D) consisting of applying a smoothing filter to said deblurring mask (D) to obtain a smoothed deblurring mask (DL).

6. Method (100) according to the preceding claim, for which the smoothing filter is a Gaussian filter.

7. Method (100) according to one of the preceding claims, for which the Fourier transforms (TFMn, TFM0) of the experimental image (Mn) and of the reference image (MO) are respectively calculated with the fast Fourier transform algorithm.

8. Method (100) according to one of the preceding claims for which the sequence of acquisition of the experimental image (Mn) is a sequence of acquisition of perfusion by a medical imaging device by magnetic resonance (1) during which an endogenous tracer passes within said plurality of elementary volumes of interest.

9. Computer program product comprising one or more program instructions executable by the processing unit (4) of a computer, said program instructions being loadable into a non-volatile memory of said computer and the execution of which by said processing unit (4) causes the implementation of a method (100) according to any one of the preceding claims.

10. A computer-readable storage medium comprising the instructions of a computer program product according to the preceding claim.

11. Medical imaging analysis system (S) comprising a processing unit (4) arranged to deblur an experimental medical image (Mn) resulting from an acquisition sequence by a medical imaging device (1) in connection with a plurality of elementary volumes of interest, called voxels, said processing unit (4) being configured to: - calculate the Fourier transform (TFMn) of said experimental image (Mn); - calculate the Fourier transform (TFMO) of a reference image (MO) of the same resolution as the experimental image (Mn); - determine a first module (YMn) of the Fourier transform (TFMn) of the experimental image (Mn) and a second module (YMO) of the Fourier transform (TFMO) of the reference image (MO); - obtain a deblurring mask (D) by calculating the ratio (D) between the first and second modules (YMn, YMO); - applying said ratio (D) to the Fourier transform (TFMn) of the experimental image (Mn) by multiplication to obtain a corrected Fourier transform (TFMnc); - obtain a deblurred experimental image (Mnc) by applying the inverse Fourier transform on said corrected Fourier transform (TFMnc).

12. Medical imaging analysis system (S) according to the preceding claim comprising a program memory comprising the program instructions of a computer program product according to claim 9.

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