Device and method for assisting a percutaneous tumor ablation procedure

Abstract

The present invention relates to a medical assistance device (1) comprising: - a memory (3) arranged to receive pre-procedural image data from one or more areas of interest, - a tool (5) arranged to receive current image data from one or more areas of interest including at least one electromagnetic stimulation needle obtained by cone-beam computed tomography during a percutaneous ablation procedure based on electromagnetic fields, to perform registration of current image data and pre-procedural image data, and to return augmented cone-beam computed tomography image data, and - a simulator (7) arranged to receive augmented cone-beam computed tomography image data and electromagnetic stimulation parameters and to return electromagnetic field simulation data. [Fig. 1]

Description

Title of the invention: Device and method for assisting a percutaneous tumor ablation procedure

[0001] The invention relates to the field of interventional oncology, in particular assistance in a percutaneous tumor ablation procedure based on electromagnetic fields.

[0002] When a tumor is detected in a patient, it can generally be removed by surgical ablation, called resection. However, some deep tumors are not resectable, so percutaneous tumor ablation - particularly that based on electromagnetic fields - then constitutes an alternative of choice from a curative perspective.

[0003] Some of the known techniques involve inserting one or more needles into the tumor percutaneously. In the case of radiofrequency ablation, for example, the needle is connected to a generator to conduct the electric current to the tumor and destroy it by thermal energy. It is also possible to use microwaves. Apart from thermal treatments, irreversible electroporation consists of using the needles to subject the tumor to pulsed electromagnetic fields so as to damage the membrane of the tumor cells and destroy them by coagulation necrosis or apoptosis. The increase in the permeability of the tumor cells can also be exploited to deliver therapeutic products to them in the context of electrochemotherapy, which consists of injecting cytotoxic molecules after the application of electrical pulses.

[0004] According to current clinical recommendations, images of the target region and surrounding vital organs are collected several weeks before percutaneous tumor ablation using medical imaging techniques such as magnetic resonance imaging (MRI) or computed tomography (CT scan). On the day of ablation, the interventional radiologist inserts the needles percutaneously into the tumor and verifies their positioning using images obtained by one of these techniques or by cone beam computed tomography (CBCT). Electromagnetic energy is then delivered to the tumor via the needles.

[0005] However, the interventional radiologist does not have visual feedback during the percutaneous ablation procedure. The effectiveness of the treatment is only evaluated by follow-up image acquisition (CT-Scan and / or MRI) a few days after the intervention for electroporation or a few weeks for radiofrequency ablation.

[0006] Each medical imaging technique (MRI, CT-scan or CBCT) has its advantages and disadvantages. Magnetic resonance imaging (MRI) and computed tomography (CT-scan) provide high quality images. However, an MRI device is expensive, has low availability and, above all, is incompatible with metal needles, while CT scans present high risks of irradiation since it involves measuring the absorption of X-rays by tissues. Conversely, cone beam computed tomography is a simple, less irradiating, inexpensive medical imaging technique that can be used during surgery. This technique, although increasingly used in the operating room, only provides low quality images and therefore does not allow the tumor to be properly visualized.

[0007] The present invention improves the situation.

[0008] In this respect, the invention relates to a medical assistance device comprising: - a memory arranged to receive pre-procedural image data of one or more areas of interest, - a tool arranged to receive current image data of one or more areas of interest including at least one electromagnetic stimulation needle obtained by cone beam computed tomography during a percutaneous ablation procedure based on electromagnetic fields, to implement a registration of current image data and pre-procedural image data, and to return augmented cone beam computed tomography image data, and - a simulator arranged to receive cone beam computed tomography augmented image data and electromagnetic stimulation parameters, and to return electromagnetic field simulation data.

[0009] In one or more embodiments, the device further comprises a display arranged to receive electromagnetic field simulation data, the simulation data comprising electromagnetic field distribution data, and to display a distribution of the electromagnetic field in one or more areas of interest.

[0010] The invention also relates to a medical assistance method implemented by the device described above and comprising: - receive pre-procedural image data of one or more areas of interest including all or part of a tumor, - receive current image data of one or more areas of interest including at least one electromagnetic stimulation needle obtained by cone beam computed tomography during a percutaneous ablation procedure based on electromagnetic fields, - implement a registration of current image data and pre-procedural image data, - return augmented image data from cone beam computed tomography, - receive augmented image data from cone beam computed tomography and electromagnetic stimulation parameters, and - return electromagnetic field simulation data.

[0011] For example, preprocedural image data is obtained by magnetic resonance imaging or computed tomography.

[0012] In one or more embodiments, the pre-procedural image data and the current image data relate to three-dimensional images of one or more areas of interest.

[0013] In one or more embodiments, the registration comprises an initial operation of implementing a nearest neighbor algorithm applied to at least one pair formed of a preprocedural image and a current image.

[0014] Advantageously, the preprocedural image and the current image are respectively decomposed into a set of local elements - or patches -, and the nearest neighbor algorithm comprises a propagation during which a distance between a patch of the preprocedural image and a patch of the current image is calculated as follows: Dtvp- vc) =

[0015] where: -1 is a preprocedural image and J is a current image, - VP and VC are the respective centers of the patches of the preprocedural image I and the current image J, - r is a local variable corresponding to a displacement vector whose variation allows VP+r and VC+r to cover the entire patch r considered, and - vz and vj are the respective gradients of the preprocedural image and the current image.

[0016] In one or more embodiments, the registration comprises determining a geometric transformation making it possible to pass from a preprocedural image to a current image, or vice versa, by minimizing an energy function depending on a criterion estimated on the basis of respective gradients of the preprocedural image and the current image.

[0017] The energy function can be defined as follows: E(T) =V(r) +<( Il Vu II A II Vv II î+ Il Vw II JW

[0018]

[0019]

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] where T is a geometric transformation, - E is the energy function defined on an image domain Q, - D is the estimated criterion, - a is a weighting coefficient, - u, v and w are the components of the geometric transformation T. In one or more embodiments, the energy function is minimized by solving a system of Euler-Lagrange equations obtained by differentiating the energy function with respect to each component of the geometric transformation, the value of each component being calculated by iteration. The criterion is for example estimated as follows: D(T) = ^'(T) where: - T is a geometric transformation, - D is the criterion calculated on a patch T, -1 is a preprocedural image and J is a current image, - V is a sub-element of patch T, and - vz and V j are the respective gradients of the preprocedural image and the current image. In one or more embodiments, a Sobel filter is applied to the pre-procedural image and the current image to determine the magnitude and orientation of their respective gradients. In one or more embodiments, the electromagnetic stimulation parameters include the intensity of the electrical current flowing through at least one electromagnetic stimulation needle, and the simulation data includes electromagnetic field distribution data. The electromagnetic field distribution is determined from an effective electrical conductivity of tissues in one or more areas of interest calculated as a function of the intensity of the electrical current flowing through the at least one electromagnetic stimulation needle. In one or more embodiments, one or more pairs of electromagnetic stimulation needles are used to implement the percutaneous ablation procedure based on electromagnetic fields, such that two electromagnetic stimulation needles of a same pair act as active electrodes, and the effective electrical conductivity of the tissues is calculated as follows for such a pair of active electrodes: s,'+J

[0026] where: - 0eff is the effective electrical conductivity of the tissues, - Imes is the measured electric current intensity, - EL+ and EL are the active electrodes, - g+ and g are the respective electrical voltages of the active electrodes EL+ and EL, - x is a local variable flowing through each of the active electrodes EL+ and EL, and - dnu+, respectively d„u, is the normal component of the potential gradient along the electrode EL+, respectively EL.

[0027] The invention also relates to a computer program comprising instructions for implementing the method described above, when the instructions are executed by at least one processor.

[0028] Other characteristics, details and advantages will appear on reading the detailed description below, and on analyzing the attached drawings, in which:

[0029] [Fig.l] schematically illustrates a device for assisting a percutaneous tumor ablation procedure according to the invention;

[0030] [Fig.2] illustrates a method of assisting a tumor ablation procedure percutaneous according to the invention; and

[0031] [Fig.3] illustrates a current image data and data registration operation pre-procedural image of one or more areas of interest.

[0032] [Fig.l] schematically illustrates a device 1 for assisting a percutaneous tumor ablation procedure based on electromagnetic fields, hereinafter referred to as device 1.

[0033] A percutaneous tumor ablation procedure based on electromagnetic fields is particularly suitable when a patient's tumor is unresectable, i.e. when surgery is not possible to remove the tumor completely. Such a case may arise when the tumor is too deep or the patient has contraindications due to his or her health condition.

[0034] Known techniques of percutaneous tumor ablation - for example radiofrequency or microwave ablation, or irreversible electroporation - consist of introducing one or more electromagnetic stimulation needles into the tumor.

[0035] The device 1 comprises a memory 3, a tool 5 and a simulator 7. In the example illustrated in [Fig.l], the device 1 also comprises a screen 9.

[0036] The memory 3 is arranged to receive pre-procedural image data of one or more areas of interest.

[0037] By "pre-procedural image" is meant here an image obtained before the percutaneous tumor ablation procedure. Pre-procedural images are therefore obtained before the interventional radiologist performs the percutaneous ablation of the patient's tumor and, more specifically, before the introduction of any needle into the tumor of the patient.

[0038] Generally speaking, an area of ​​interest designates an anatomical region of the patient whose visualization is necessary or useful for the preparation of the operation or for the implementation thereof. An area of ​​interest includes, for example, all or part of the detected tumor to enable morphological characteristics to be determined, for example its size or position, in particular in relation to the surrounding vital organs. However, an area of ​​interest may also include only parts of the body affected or likely to be affected by the tumor or organs to which the interventional radiologist must pay particular attention during the percutaneous tumor ablation procedure.

[0039] Pre-procedural images of the area(s) of interest are collected up to several weeks before the operation using medical imaging techniques. The techniques used in this instance are magnetic resonance imaging (MRI) or computed tomography (CT-scan), which have the advantage of providing high-quality images and therefore precise visualization of the tumor and surrounding vital organs.

[0040] A preprocedural image may be two-dimensional or three-dimensional.

[0041] In the example illustrated in [Fig.l], the pre-procedural IM_CT image represented is acquired by computed tomography.

[0042] The tool 5 is arranged to receive current image data of one or more areas of interest including at least one electromagnetic stimulation needle. It is understood here that an area of ​​interest may comprise only a portion of an electromagnetic stimulation needle.

[0043] By "current image" is meant here an image obtained during the percutaneous tumor ablation procedure, i.e. while the interventional radiologist is ablating the patient's tumor in the operating room. More specifically, a current image is an image obtained after at least one electromagnetic stimulation needle has been introduced.

[0044] Indeed, as explained previously, the percutaneous tumor ablation procedure involves introducing one or more needles into the tumor. A current image therefore has the advantage, compared to a pre-procedural image, of visualizing the position of an electromagnetic stimulation needle.

[0045] Furthermore, in the context of the invention, the current images are obtained by cone beam computed tomography (CBCT). Cone beam computed tomography can indeed be used during the intervention, unlike magnetic resonance imaging, and is less irradiating than computed tomography. As explained previously, medical imaging techniques such as magnetic resonance imaging and computed tomography tometry are more suited to the acquisition of preprocedural image data.

[0046] Cone beam digital volume tomography consists of an X-ray emitter whose conical irradiation beam allows, in a single rotation sequence - complete (360°) or not (180°) - around the patient, to scan the entire volume and to reconstruct a three-dimensional representation. Conversely, computed tomography is based on a flattened X-ray which only allows a section of the analyzed volume to be explored.

[0047] The conical beam passes through the patient and is analyzed, after attenuation, by a detector. The detector is integral with the emitter so that, during rotation, the emitter releases a pulse of X-rays which pass through the patient's body to be received by the detector which rotates simultaneously. The sensor is generally a flat sensor which makes it possible to obtain, at each angular displacement, a two-dimensional image of the volume crossed by the conical beam. The digitized flat images - of the order of several hundred - are then processed by a volume reconstruction algorithm for a virtual visualization of the analyzed anatomical structures. A brightness amplifier can be used as an alternative to the flat sensor and has the advantage of having very high light sensitivity.

[0048] Like the pre-procedural image, the current image obtained by cone beam computed tomography can be either a two-dimensional or a three-dimensional image.

[0049] A current IM_CBCT image, obtained by cone beam computed tomography, is illustrated in [Fig.l].

[0050] Furthermore, the tool 5 is arranged to implement a resetting of current image data and pre-procedural image data. It is therefore understood that the tool 5 is arranged to communicate with the memory 3 and receive, from the latter, pre-procedural image data.

[0051] Image registration consists of comparing images to match them, in particular to combine or superimpose them. Registration generally involves estimating a geometric transformation allowing an optimal and coherent superposition of the common or similar characteristics present in the processed images.

[0052] The tool 5 is here arranged to receive, on the one hand, a pre-procedural image and, on the other hand, a current image to carry out a registration of these images. This registration is complex due to the fact that the pre-procedural images are obtained by magnetic resonance imaging or computed tomography, whereas the current images are obtained by cone beam digitized volume tomography.

[0053] The pre-procedural images and the current images are preferably of the same dimension to facilitate registration.

[0054] The recalibration implemented by the tool 5 will be detailed in the remainder of the description, with reference to [Fig.2] and especially to [Fig.3].

[0055] Finally, the tool 5 is arranged to return augmented image data from cone beam computed volume tomography.

[0056] The tool 5 makes it possible to generate images whose quality is close to that of the pre-procedural image while including the electromagnetic stimulation needles visible on the current images. Such images constitute augmented images whose visualization, during the percutaneous tumor ablation procedure, makes it possible to verify and precisely adjust the position of the electromagnetic stimulation needles. The augmented image data of cone beam computed tomography can be stored in the memory 3.

[0057] The tool 5 can be implemented in the form of an appropriate computer program executed on one or more processors. Such a processor can be implemented in any known manner, for example in the form of a microprocessor for a personal computer, a programmable logic circuit (better known by the English acronym PLD for “Programmable Logical Device”) or a dedicated chip of the FPGA type (English acronym for “Field Programmable Gate Array”) or SoC (English acronym for “System on Chip”), a grid of computing resources, a microcontroller, or any other specific form having the computing power necessary for implementing the medical assistance method described below. One or more of these elements can also be implemented in the form of specialized electronic circuits such as an ASIC (English acronym for “Application-Specific Integrated Circuit”).A combination of processors and electronic circuits may also be envisaged. The tool 5 may also comprise a memory or any known type of storage medium for storing instructions of a computer program whose implementation by the processor(s) results in the operation of the tool 5.

[0058] The simulator 7 is arranged to receive augmented image data from cone beam digital volume tomography.

[0059] The simulator 7 is for example arranged to communicate with the tool 5 to receive the augmented image data of cone beam digitized volume tomography, or with the memory 3 when the augmented image data of cone beam digitized volume tomography are stored therein.

[0060] Furthermore, the simulator 7 is arranged to receive electromagnetic stimulation parameters. These parameters include, for example, the intensity of the electric current flowing in at least one electromagnetic stimulation needle.

[0061] From the augmented image data of cone beam computed tomography and the electromagnetic stimulation parameters, the simulator 7 is arranged to return electromagnetic field simulation data.

[0062] The simulator 7 makes it possible to model and translate into data the electromagnetic field to which the tumor area is exposed, whether in the context of radiofrequency or microwave ablation, irreversible electroporation or any other percutaneous tumor ablation technique based on electromagnetic fields. Such data can be used during the operation, for example by the interventional radiologist, to know the distribution of the electromagnetic field generated by the electromagnetic stimulation needle(s).

[0063] Again, the electromagnetic field simulation data may be stored in memory 3.

[0064] Like the tool 5, the simulator 7 may be implemented in the form of a suitable computer program executed on one or more processors. Regarding the implementation of such a processor, the preceding considerations relating to the tool 5 also apply to the simulator 7. Furthermore, here again the simulator 7 may also comprise a memory or any known type of storage medium for storing instructions of a computer program whose implementation by the processor(s) results in the operation of the simulator 7.

[0065] In [Fig.l], the memory 3, the tool 5 and the simulator 7 are represented as distinct elements. However, it must be understood here that this distinction is functional and that, consequently, all or part of these three elements can be constituted by a single element adapted to implement the associated functionalities.

[0066] Finally, the screen 9 is arranged to receive electromagnetic field simulation data. The screen 9 is for example arranged to communicate with the simulator 7 to receive the electromagnetic field simulation data, or with the memory 3 when the electromagnetic field simulation data is stored therein.

[0067] The screen 9 is arranged to display a distribution of the electromagnetic field in one or more areas of interest. The screen 9 allows the interventional radiologist to view live not only the position of the electromagnetic stimulation needles but also the distribution of the electromagnetic field to which the tumor is exposed.

[0068] The screen 9 is optional and can be directly integrated into the device 1 or be removed from it.

[0069] A method of assisting a percutaneous tumor ablation procedure will now be described with reference to [Fig.2] and [Fig.3].

[0070] In the context of the invention, a tumor is detected in a patient and a percutaneous tumor ablation procedure based on electromagnetic fields is programmed to remove it. In order to prepare for the operation, pre-procedural images of the tumor are collected by magnetic resonance imaging or computed tomography.

[0071] During an operation 200, pre-procedural image data of one or more areas of interest including all or part of a tumor are received by the memory 3.

[0072] The pre-procedural image data may be directly stored in the memory 3 or be retained on another storage medium pending the operation.

[0073] During the percutaneous tumor ablation procedure based on electromagnetic fields - for example, radiofrequency or microwave ablation, or irreversible electroporation - the interventional radiologist introduces one or more electromagnetic stimulation needles into the tumor.

[0074] Furthermore, in parallel with the percutaneous tumor ablation procedure, cone beam computed tomography is used to collect current images. As explained above, an X-ray transmitter rotates around the patient and emits a cone beam that scans the target region. The X-rays are received by a receiver and current image data of one or more areas of interest including at least one electromagnetic stimulation needle are then generated.

[0075] During an operation 210, the current image data obtained by cone beam digital volume tomography are received by the tool 5.

[0076] In addition to the current image data, the tool 5 also receives pre-procedural image data from the memory 3.

[0077] During an operation 220, the tool 5 implements a recalibration of current image data and preprocedural image data.

[0078] This recalibration is detailed below with reference to [Fig.3] and can be broken down into two sub-operations: a sub-operation 300 of nearest neighbor algorithm applied to at least one pair formed of a pre-procedural image and a current image, then a sub-operation 310 of determining a geometric transformation making it possible to move from a pre-procedural image to a current image.

[0079] In the following description, the registration of a pre-procedural image and a current image is considered. However, it should be understood here that, advantageously, the tool 5 performs a registration for several pairs formed respectively of a pre-procedural image and a current image.

[0080] The sub-operation 300 corresponds to a “PatchMatch” type algorithm for establishing a correspondence between the patches of the preprocedural image and their nearest neighbors in the current image. This algorithm makes it possible to obtain a correspondence table, often referred to by the English acronym NNF for “Nearest Neighbor Field”. This correspondence table is equivalent to a function which, each patch of the pre-procedural image, associates a displacement vector which, starting from the patch of the current image occupying the same position as the considered patch of the procedural image, makes it possible to reach the patch of the current image detected as being the nearest neighbor.

[0081] A “patch” here designates a local element of the domain of an image. In the case of a two-dimensional image, a patch is a local surface of square shape centered on a pixel. In the case of a three-dimensional image, a patch is a local volume of cubic shape centered on a voxel.

[0082] A patch can thus be broken down into sub-elements which are pixels for a two-dimensional image or voxels for a three-dimensional image.

[0083] The “PatchMatch” algorithm can be broken down into three steps: initialization, propagation, and random search. It is considered below that the preprocedural image and the current image of the pair on which the tool 5 applies a registration are three-dimensional.

[0084] Initialization consists of randomly associating each patch of the preprocedural image with a patch of the current image.

[0085] It is considered below that the preprocedural image and the current image have been decomposed into the same number of patches, so that a bijection between the preprocedural image and the current image can be randomly constructed.

[0086] As explained previously, the preprocedural image and the current image are three-dimensional. Each patch can therefore be located by three-dimensional coordinates of its central voxel. For example, we use any reference frame O with an abscissa axis X, an ordinate axis Y and a dimension Z. For the sake of simplification, we can provide these axes with an arbitrary graduation so that each patch has integer coordinates.

[0087] It is considered below that the preprocedural image and the current image are decomposed into MxNxP patches, where M, N and P are natural integers. Each patch has coordinates (x,y,z), where x is an integer between 1 and M, y is an integer between 1 and N, and z is an integer between 1 and P.

[0088] Propagation consists of examining each patch of the preprocedural image one by one to improve the initial random correspondence with the current image. Typically, the propagation order consists of examining the initial correspondence table in the increasing direction of the axes, from left to right, from top to bottom and from front to back. This is again a convention and it is possible to proceed differently.

[0089] For each patch examined, we consider the set of neighboring patches already examined. With the exception of patches on the periphery of the preprocedural image, each patch has exactly six neighbors, three of which have already been examined. Each of these three patches is associated with a displacement vector. We then consider, on the current image, the patch whose position corresponds to that of the patch being examined on the preprocedural image. We apply to this patch on the current image four displacement vectors: the displacement vector already associated, during initialization, with the patch examined and the three displacement vectors associated respectively with the neighboring patches of the patch examined on the preprocedural image. We thus arrive at four patches on the current image: the patch already associated, during initialization, with the patch examined and three patches reached by the application of each of the three remaining vectors. We then calculate a distance between the patch examined and each of these four patches and we choose, among these four patches, the one which allows this distance to be minimized. The patch examined is then associated with the corresponding displacement vector and therefore, in an equivalent manner, with the corresponding patch of the current image.

[0090] The propagation can be formalized as follows:

[0091] A patch examined on the preprocedural image is centered on a voxel VP; with coordinates (xi,y;,Zi). The patch examined here is not on the periphery of the preprocedural image, hence: Xi>l, yi> 1, Zi>l. The six neighboring patches are centered on voxels with respective coordinates: (x;-l,yi,Zi), (x;,yi-l,Zi), (x;,yi,Zi-l), (xr l,y;-l,Zi), (xr l,yi,z;-l) and (xi,y;-l,Zi-l). Among these six patches, three have already been examined and are centered on voxels whose respective coordinates are as follows: (x;-l,yi,Zi), (xi,y;-1,¾) and (x; ,yi,Zi-l).

[0092] The examined patch centered on VP; was associated, during initialization, with a displacement vector u;(0,0,0). The left patch, already examined during propagation, was associated with a displacement vector u;(-1,0,0). The top patch, already examined during propagation, was associated with a displacement vector u;(0,-1,0). The front patch, already examined during propagation, was associated with a displacement vector Ui(0,0,-1).

[0093] We now consider on the current image the patch centered on the voxel VCi, with the same coordinates (xi,yi,zi) as the voxel VP; on which the examined patch is centered. We apply to the patch centered on the voxel VC; each of the four displacement vectors to reach four voxels on the current image, therefore VC; + u;(0,0,0), VC; + u; (-1,0,0), VCi + Ui(0,-1,0) and VCi + U;(0,0,-1). We calculate the distance between the patch centered on the voxel VP; examined and each of the patches centered on the voxels thus reached. We then select, as the displacement vector corresponding to the patch centered on the voxel VPi, the one allowing this distance to be minimized. By noting u';(0,0,0) the new displacement vector associated, during propagation, with the patch centered on the voxel VP; , we therefore have:

[0094] "3°'°- °) VQ+mXOA 0) ). D( VP? VC^uS -1,0, 0) ), D( VP;, VCt+Uf(Q, -1.0) ), W;-1)))

[0095] It is possible to define the distance D in different ways. In the context of the invention, a distance is chosen to promote the alignments of contours between the preprocedural image and the current image. By setting and the respective gradients of the preprocedural image I and the current image J, the distance D(VP; ;VC; +Ui) between a patch centered on the voxel VP; on the preprocedural image I and the corresponding potential patch centered on the voxel VC.+u, on the current image J can be defined as follows: 100961 D (VP: VC, + u, ) = - .....

[0097] where the local variable r is a displacement vector whose variation allows VP;+r and VQ+r to cover the entire patch T considered. In other words, any voxel of the patch considered on the preprocedural image I, respectively on the current image J, can be reached from the central voxel VP;, respectively VCi+u;, with a displacement vector r. Thus, for any voxel V of the patch centered on the voxel VP; of the preprocedural image, respectively centered on the voxel VCi+u; of the current image, there exists a displacement vector r such that V=VP;+r, respectively V=VCi+u;+r.

[0098] Finally, the random search consists of improving each displacement vector associated with a patch of the preprocedural image by testing several candidates located at an exponentially decreasing distance from the nearest neighbor found during the propagation. Typically, for each patch of the current image, we first apply the propagation then the random search, and then move on to the next patch.

[0099] We again consider the patch of the preprocedural image centered on the voxel VP;. At the end of the propagation, this patch is now associated with the displacement vector u'; (0,0,0) and therefore, in an equivalent manner, with the patch centered on the voxel VCi+u'i(0,0,0) on the current image.

[0100] We then test a set of candidate displacement vectors uk(0, 0, 0) of the following form:

[0101] uk(0A 0) = 0) +

[0102] where: - k is a natural integer, - co is a maximum search radius, - [3 is a fixed ratio between the search window sizes, typically equal to 1 / 2, and - p is a random vector in [-1,1]3.

[0103] For each candidate uk(0,0,0), we measure the distance between the patch centered on the voxel VP; in the preprocedural image and the patch centered on the voxel VC; + uk(0,0,0) in the current image. We then select, as the displacement vector corresponding to the patch centered on the voxel VP;, the one allowing this distance to be minimized.

[0104] It should be noted that the propagation and random search can be repeated several times to improve the correspondence table between the preprocedural image and the current image.

[0105] Sub-operation 310 corresponds to the actual registration of the current image data and the pre-procedural image data. In practice, the registration consists of determining a geometric transformation making it possible to move from the pre-procedural image I to the current image J. Such a geometric transformation makes it possible to align the pre-procedural image I and the current image J. Equivalently, the current image J is registered on the pre-procedural image I. It should be noted that sub-operation 310 can be carried out alone, without sub-operation 300 which is therefore optional but makes it possible to facilitate and accelerate the implementation of the registration.

[0106] Of course, it is also possible to re-align the pre-procedural image I on the current image J, thus to determine a geometric transformation making it possible to pass from the current image I to the pre-procedural image J. It is understood that, in the remainder of the description, the re-alignment can be implemented by inverting I and J.

[0107] To do this, we first determine an estimated criterion based on the respective gradients V} and Vj of the preprocedural image I and the current image J for a given geometric transformation T. Such a criterion can be estimated as follows by integrating over all the voxels V of a patch T: [° 108 L

[0109] Optionally, the tool 5 can apply a Sobel filter to the preprocedural image I and to the current image J to determine the amplitudes Mb Mj and orientations 0b 0j of their respective gradients at the level of a voxel V. We then obtain the following expression:

[0110] \ 1 I

[0111] where coT(V) and A0t(V) are calculated as follows:

[0112] Mt(V) - M^TÇV) ) M / V)

[0113] aot(v)-of(T(v))-o / v)

[0114] Once the criterion D has been determined for any geometric transformation T, the tool 5 determines the geometric transformation making it possible to move from the preprocedural image I to the current image J by minimizing an energy function depending on this criterion.

[0115] The energy function E is for example defined as follows on the image domain Q which includes all the patches T:

[0116]

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] r / -s. ? 2\ £(T)=^D(T)+f( | V„ | ;+ || Vv || 2+ || V» || ,)dV where: - a is a weighting coefficient, and - u, v and w are the components of the geometric transformation T. As explained previously, here we consider that the preprocedural image and the current image are three-dimensional and, therefore, that the geometric transformation allowing to pass from one to the other has three components. However, the same procedure can be applied when the preprocedural image and the current image are two-dimensional, in which case it is sufficient to delete one of the components of the geometric transformation. Tool 5 can minimize the energy function by solving a system of Euler-Lagrange equations obtained by differentiating the energy function with respect to each component of the geometric transformation. Such a system of equations can then take the following form using a Laplacian operator A: ^(T) -aAu = 0 ' ^(T)-aAv = 0 ^(T)-aAw = 0 The value of each component of the geometric transformation can be calculated by iterations: Up+i = Up + A Up- a-ü^T ^) = Wp + A Wp - p) The previous system corresponds to the p-th iteration. The sequences up, vp and wp thus defined converge respectively towards the components of the desired geometric transformation. During an operation 230, the tool 5 returns augmented image data of cone beam computed tomography. The augmented image data characterizes an augmented image obtained by registration from a pair formed of a pre-procedural image and a current image. This augmented image of cone beam computed tomography has a quality close to the pre-procedural image and comprises a region of interest including at least one electromagnetic stimulation needle identified on the current image. Augmented image data of cone beam computed tomography can be stored in memory 3.

[0126] Augmented cone beam computed tomography image data allows the position of the electromagnetic stimulation needles to be adjusted, if necessary, to ensure that they are correctly introduced into the tumor without damaging a nearby body part or organ.

[0127] During an operation 240, the simulator 7 receives augmented image data from cone beam computed volume tomography and electromagnetic stimulation parameters.

[0128] The augmented image data of cone beam computed volume tomography may be received by the simulator 7 directly from the tool 5 or be retrieved from the memory 3.

[0129] As explained above, the electromagnetic stimulation parameters may include the intensity of the electrical current flowing in at least one electromagnetic stimulation needle. Preferably, these parameters include the intensity of the electrical current flowing in each electromagnetic stimulation needle.

[0130] During an operation 250, the simulator 7 returns electromagnetic field simulation data.

[0131] To do this, the simulator 7 calculates an effective electrical conductivity of the tissues as a function of the intensity of the electrical current flowing in each electromagnetic stimulation needle.

[0132] The case in which one or more pairs of electromagnetic stimulation needles are introduced into the patient's tumor is considered below. This case occurs in particular when the percutaneous tumor ablation technique used is irreversible electroporation. Indeed, irreversible electroporation operates in bipolar mode - we also speak of multi-bipolar mode when several pairs of needles are introduced - so that an electromagnetic field is created between each pair of electromagnetic stimulation needles. The electromagnetic stimulation needles forming a pair then act as active electrodes to deliver pulses of electromagnetic fields.

[0133] The effective electrical conductivity of tissues can be calculated as follows for a pair of active electrodes - or electromagnetic stimulation needles -: _ __ IlllCS CT£ ““ trrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr

[0134] where: - 0eff is the effective electrical conductivity of the tissues, - Imes is the measured electric current intensity, - EL+ and EL are the active electrodes, - g+ and g are the respective electrical voltages of the active electrodes EL+ and EL, - x is a local variable passing through each of the active electrodes EL+ and EL, and - dnu+, respectively d„u, is the normal component of the gradient of the potential le along the EL+ electrode, respectively EL.

[0135] The simulator 7 then determines, from the effective electrical conductivity of the tissues, electromagnetic field distribution data. This data makes it possible to know the distribution of the electromagnetic field in the tumor area and therefore, here too, to adjust the position of the electromagnetic stimulation needles or to vary the intensity of the electric current in each electromagnetic stimulation needle to carry out percutaneous tumor ablation in the most efficient way possible.

[0136] The electromagnetic field simulation data can be stored in the memory 3. The simulation data is for example stored in DICOM format (acronym for “Digital Imaging and COmunication in Medicine”) which is a classic standard for data from medical imaging.

[0137] Finally, during an optional operation 260, the screen 9 receives electromagnetic field simulation data.

[0138] The electromagnetic field simulation data can be received by the screen 9 directly from the simulator 7 or be retrieved from the memory 3.

[0139] Advantageously, the electromagnetic field simulation data comprises electromagnetic field distribution data, so that the screen 9 displays a distribution of the electromagnetic field in one or more areas of interest, and in particular in the area of ​​the tumor.

[0140] Displaying the distribution of the electromagnetic field on the screen 9 allows the interventional radiologist to have visual feedback during the percutaneous tumor ablation procedure and to adjust the position of the electromagnetic stimulation needles.

[0141] Of course, feedback other than visual feedback is possible so that the simulation data returned by the simulator 7 can be rendered in another form so as to inform the interventional radiologist of the position of the electromagnetic stimulation needles and the electromagnetic field to which the area of ​​the tumor is exposed.

Claims

Claims

1. A medical assistance device (1) comprising: - a memory (3) arranged to receive pre-procedural image data of one or more areas of interest, obtained by magnetic resonance imaging or computed tomography, - a tool (5) arranged to receive current image data of one or more areas of interest including at least one electromagnetic stimulation needle obtained by cone beam computed tomography during a percutaneous ablation procedure based on electromagnetic fields, to implement a registration of current image data and pre-procedural image data, and to return cone beam computed tomography augmented image data, and - a simulator (7) arranged to receive cone beam computed tomography augmented image data and electromagnetic stimulation parameters,and to return electromagnetic field simulation data.,

2. A device (1) according to claim 1, further comprising a screen (9) arranged to receive electromagnetic field simulation data, said simulation data comprising electromagnetic field distribution data, and to display a distribution of the electromagnetic field in one or more areas of interest.

3. The device of claim 1 or 2, wherein the pre-procedural image data and the current image data relate to three-dimensional images of one or more areas of interest.

4. Device according to one of claims 1 to 3, in which the registration (220) comprises an initial operation (300) of implementing a nearest neighbor algorithm applied to at least one pair formed of a preprocedural image and a current image.

5. Device according to claim 4, in which the preprocedural image and the current image are respectively decomposed into a set of local elements - or patches -, and in which the nearest neighbor algorithm comprises a propagation during which a distance between a patch of the preprocedural image and a patch of the current image is calculated as follows: „ z T / „ „ V7( VP+r^j( vc+r)\dr where: -1 is an image L)\ V r, VC ) - r _ _ Jr||Vf( V / '+r) || J|VZ( W7+r) ||2dr preprocedural and J is a current image, - VP and VC are the respective centers of the patches of the preprocedural image I and the current image J, - r is a local variable corresponding to a displacement vector whose variation allows VP+r and VC+r to cover the entire patch T considered, and - v and vj are the respective gradients of the preprocedural image and the current image.

6. Device according to one of claims 1 to 5, in which the resetting (220) comprises the determination (310) of a geometric transformation making it possible to pass from a pre-procedural image to a current image, or vice versa, by minimizing an energy function depending on a criterion estimated on the basis of respective gradients of the pre-procedural image and the current image.

7. Device according to claim 6, in which the energy function is defined as follows: £(7-)=1^(7)+^ || Vu || 2+ |Vv||2+ || Vw || ;prou:- T is a geometric transformation, - E is the energy function defined on an image domain Q, - D is the estimated criterion, - a is a weighting coefficient, - u, v and w are the components of the geometric transformation T.

8. A device according to claim 7, wherein the energy function is minimized by solving a system of Euler-Lagrange equations obtained by deriving the energy function with respect to each component of the geometric transformation, the value of each component being calculated by iteration.

9. Device according to one of claims 6 to 8, in which the criterion is estimated as follows: D(T) = C <T\^ ■UvAr(v))||^^ où : - T est une transformation géométrique, - D est le critère calculé sur un patch T, -1 est une image préprocédurale et J est une image courante, - V est un sous-élément du patch T, et - vz and vj are the respective gradients of the preprocedural image and the current image.

10. Device according to one of claims 6 to 9, in which a Sobel filter is applied to the pre-procedural image and to the current image to determine the amplitude and orientation of their respective gradients.

11. Device according to one of claims 1 to 10, wherein the electromagnetic stimulation parameters include the intensity of the electric current flowing in at least one electromagnetic stimulation needle, and wherein the simulation data comprises electromagnetic field distribution data, said electromagnetic field distribution being determined from an effective electrical conductivity of tissues in one or more areas of interest calculated as a function of the intensity of the electric current flowing in said at least one electromagnetic stimulation needle.

12. A device according to claim 11, wherein one or more pairs of electromagnetic stimulation needles are used to implement the percutaneous ablation procedure based on electromagnetic fields, such that two electromagnetic stimulation needles of a same pair act as active electrodes, and wherein the effective electrical conductivity of the tissues is calculated as follows for such a pair of active electrodes: mes_________________ ('tj ”” f ' f &+J ( X ) djC+SJ dx where: - oeff is the effective electrical conductivity of the tissues, - Imes is the measured electrical current intensity, - EL+ and EL are the active electrodes, - g+ and g are the respective electrical voltages of the active electrodes EL+ and EL, - x is a local variable flowing through each of the active electrodes EL+ and EL, and - dnu+, respectively d„u,is the normal component of the potential gradient along the electrode EL+, respectively EL.,

Images