A tool to assist in planning a minimally invasive procedure

Abstract

The invention relates to a data processing device implementing a method to assist in the selection of at least one optimal trajectory for a minimally invasive medical intervention.The device is specifically configured to identify (102) in a three-dimensional anatomical model a target point to be reached in a treatment region within a patient's anatomy of interest, and to perform at least one iteration comprising the steps of: determining (103) a region and a sampling resolution of candidate entry points, eliminating (104) candidate trajectories based on at least one predetermined validity criterion, for each remaining candidate trajectory, calculating (105) at least one cost function from a plurality of quantified features, displaying (106) the candidate trajectories on the anatomical model with a means of rapid visualization of the cost function, and selecting (107) an optimal trajectory from among the displayed candidate trajectories. Figure for the abstract: Fig. 2.

Description

Title of the invention: Device for assisting in the planning of a minimally invasive intervention Scope of the invention

[0001] This application falls within the field of planning a minimally invasive medical intervention, possibly assisted by a medical robot. In particular, a data processing device is proposed to implement a method for assisting in the selection of an optimal trajectory for a minimally invasive medical intervention within a patient's anatomy of interest. State of the art

[0002] To prepare for a minimally invasive medical procedure aimed at reaching a target anatomical area of ​​interest in a patient's anatomy with a medical instrument, a practitioner generally plans the procedure based on a preoperative or intraoperative medical image. The minimally invasive medical procedure may, in particular, aim to remove or biopsy a tumor in an organ or bone, perform vertebroplasty or vertebroplasty, or stimulate a specific anatomical area. The anatomy of interest may correspond, for example, to a lung, a kidney, the liver, the brain, a vertebra, the tibia, the knee, etc. The medical instrument may be a needle, an electrode, a probe, etc.

[0003] The preoperative or intraoperative medical image is obtained for example by computed tomography, by magnetic resonance imaging, by ultrasound, or by positron emission tomography.

[0004] During the planning phase, the practitioner defines a target point in a region of the anatomy of interest to be treated. The practitioner also defines an entry point for the medical instrument on the patient's skin. These two points then define a trajectory that the medical instrument must follow to perform the medical procedure.

[0005] Depending on the type of intervention, certain constraints must be respected. For example, it may be important that the medical instrument does not pass through a high-risk anatomical structure (organ, bone or blood vessel, for example).

[0006] The trajectory is generally defined empirically based on the practitioner's knowledge and the information they visualize on the preoperative image. However, this trajectory is not necessarily optimal for ensuring effective treatment with minimal risk to the patient.

[0007] For a given intervention, there are theoretically an infinite number of possible trajectories. However, the practitioner must select a trajectory within a limited time, sometimes a very short time, particularly when the planning of the intervention is performed just before the procedure.

[0008] This task must also be performed using volumetric images of the patient, and therefore in three dimensions, whereas conventional methods of presenting these images are two-dimensional slices of this volume, making the search for the optimal trajectory even more difficult for the practitioner. Furthermore, when several trajectories must be considered for the procedure (for example, to insert several needles), the planning is even more complex for the practitioner to perform mentally.

[0009] Ultimately, the quality of trajectory optimization is highly dependent on the practitioner and the time he can allocate to this task.

[0010] Methods exist for evaluating a trajectory proposed by a practitioner, for example, based on the risk of traversing a hazardous area with the medical instrument. Such a solution does not allow the practitioner to quickly find an optimal or near-optimal trajectory for the medical intervention in question.

[0011] There are also planning methods in which the entry point, target point, and / or trajectory are automatically suggested to the practitioner. However, these solutions generally do not give the user the flexibility to visualize the optimality of a large number of trajectories, and to choose a different trajectory that may be suboptimal but preferable for reasons that can vary from one patient to another and that are not necessarily taken into account by the optimality criteria considered. Description of the invention

[0012] The solutions proposed in this application are intended to remedy all or part of the disadvantages of the prior art, in particular those set out above.

[0013] To this end, and according to a first aspect, a data processing device is proposed comprising a processor, computer memory, and a display screen. The computer memory includes program code instructions which, when executed by the processor, configure the processor to implement a method for assisting in the selection of at least one optimal trajectory for a minimally invasive medical intervention within a patient's anatomy of interest. The processor is configured to: - to obtain a three-dimensional anatomical model of the patient from previously acquired medical images of the patient, the anatomical model including a representation of the body envelope and the anatomy of interest of the patient, - identify in the anatomical model at least one target point to be reached in a region to be treated within the patient's anatomy of interest, - perform at least one iteration for which the processor is configured to: • determine a sampling region and a sampling resolution to sample candidate entry points in the anatomical model at the level of the patient's body envelope, each candidate entry point belonging to the sampling region, the sampling resolution being representative of a minimum distance separating two candidate entry points, each pair formed by the target point and a candidate entry point forming a candidate trajectory, • eliminate candidate trajectories based on at least one predetermined validity criterion, • For each remaining candidate trajectory, quantify each of a plurality of predetermined characteristics, and calculate at least one cost function from the quantified characteristics, • display on the display screen, superimposed on the anatomical model, at least some of the remaining candidate trajectories, with, for each displayed candidate trajectory, a means of quickly visualizing a value of the cost function, • to obtain the selection, by a user, for said at least one target point, of a trajectory considered optimal among the candidate trajectories displayed.

[0014] Such arrangements allow a practitioner to be offered a set of optimal or near-optimal trajectories for a minimally invasive procedure based on a three-dimensional anatomical model of the patient. The proposed trajectories take into account various types of constraints (for example, practical constraints, performance constraints, and / or safety constraints). The practitioner has the option of choosing a trajectory that may be suboptimal according to a predefined criterion, but preferable for constraints not captured by that criterion. The invention also allows for a rapid and optimized exploration of all solutions to determine candidate trajectories. It enables the user to quickly visualize different candidate trajectories and their optimality criteria. The proposed solution is flexible, as it can be adapted to the specific needs of the procedure.The practitioner also has the option to update optimality criteria (cost function) based on user feedback (evaluation of a selected intervention trajectory).

[0015] In particular embodiments, the device may further comprise one or more of the following features, taken individually or in all their variations:

[0016]

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[0024] technically possible combinations. In certain embodiments, the processor is configured to perform at least two iterations. For an iteration of rank n, where n is an integer strictly greater than one: - the sampling region for iteration n is determined to be smaller than the sampling region determined for iteration (n-1), and to include the candidate entry point corresponding to the trajectory selected at iteration (n-1), and - the sampling resolution of the iteration of rank n is determined to be finer than the sampling resolution determined for the iteration of rank (n-1). In particular embodiments, the sampling resolution of iteration n is determined as a function of a variability of the cost function calculated for iteration (n-1) in the neighborhood of the trajectory selected at iteration (n-1). In particular embodiments, the sampling region of iteration n is determined as a function of a variability of the cost function calculated for iteration (n-1) in the neighborhood of the trajectory selected at iteration (n-1). In particular embodiments, to sample candidate entry points, the processor is configured to determine, within the anatomical model at the level of the patient's body envelope, a set of points defined by a spherical coordinate system centered on the target point. Each point is defined by a distance r from the target point and by two angles α and θ, the angles α and θ being defined such that, for indices i and j corresponding to strictly positive integers: [Math.1] And [Math.2] where R represents a minimum distance separating two candidate entry points at the level of the patient's body envelope. In specific embodiments, a validity criterion allows verification, for a given candidate trajectory, of at least one of the following elements: - the validity of the length of the candidate trajectory in relation to a medical instrument envisaged for the intervention, - the possibility of configuring a robotic arm equipped with a medical instrument so that said medical instrument can follow the candidate trajectory, - the presence of objects placed on the patient and obstructing the candidate trajectory, - an intersection of the candidate trajectory with at least one critical anatomical structure internal to the patient's body.

[0025] In particular embodiments, several validity criteria are considered to eliminate candidate trajectories, and the different validity criteria are evaluated according to an order defined as a function, for each validity criterion, of an estimated computation time required to evaluate said validity criterion.

[0026] In particular embodiments, the plurality of characteristics of a candidate trajectory includes at least one of the following characteristics: - a minimum distance between the candidate trajectory and a critical anatomical structure internal to the patient's body, - a length of the intersection between the candidate trajectory and the anatomy of interest, - a minimum angle of incidence between the candidate trajectory and an anatomical interface crossed by the candidate trajectory, - an estimated value representative of the stability, under the effect of its own weight, of a medical instrument positioned up to the target point along the candidate trajectory, - a minimum distance between an envelope of the region to be treated and an envelope of an estimated ablation region for the candidate trajectory, - an angular deviation between the candidate trajectory and a major axis of the region to be treated.

[0027] In particular embodiments, several cost functions are calculated from the quantified characteristics, each cost function being calculated according to a different set of weights assigned respectively to the different quantified characteristics, and the processor is configured to obtain an indication, from a user, of the cost function to be considered for the display of candidate trajectories.

[0028] In particular embodiments, the means for quickly visualizing the value of the cost function for each displayed candidate trajectory includes a color code associating different colors respectively with different values ​​of the cost function.

[0029] In particular embodiments, the processor is configured to display the quantifications determined for the characteristics of the trajectory selected by the user.

[0030] In particular embodiments, the processor is configured to obtain, for at least one displayed candidate trajectory, a user evaluation of said candidate trajectory, and to update the cost function based on said evaluation.

[0031] In particular embodiments, the processor is configured to: determine in the anatomical model, post-intervention, a real trajectory followed by a medical instrument during the intervention, from medical images acquired on the patient at a time when the medical instrument is in place, determine a value of the cost function for the real trajectory, calculate a difference between the value of the cost function of the candidate trajectory selected for the intervention and the value of the cost function of the real trajectory.

[0032] In particular embodiments, the processor is configured to compare the calculated deviation with a predetermined threshold and display an indication relating to the value of the calculated deviation.

[0033] In particular embodiments, the processor is configured to: obtain an evaluation of the actual trajectory by the user, determine, based on the calculated gap and the evaluation obtained, a statistical value representative of the variability of the cost function, eliminate candidate trajectories considered equivalent according to the statistical value thus obtained. Presentation of the figures

[0034] The invention will be better understood upon reading the following description, given by way of non-limiting example, and made with reference to Figures 1 to 7, which represent:

[0035] [Fig-1] a schematic representation of a data processing device according to the invention,

[0036] [Fig.2] a schematic representation of the main steps of a process for assisting the selection of an optimal trajectory for a medical intervention,

[0037] [Fig.3] a schematic representation of the definition of an orbital angle and of a craniocaudal angle to define a sampling region,

[0038] [Fig.4] a schematic representation of a set of candidate entry points in the anatomical model at the level of the patient's body envelope,

[0039] [Fig.5] a schematic representation of the display of a set of trajectories candidates for medical intervention, with their optimality criteria visualized via a scale, represented here in grey levels,

[0040] [Fig.6] a schematic representation of additional steps to take in includes a user evaluation of a candidate trajectory,

[0041] [Fig.7] a schematic representation of additional steps to take into account a discrepancy between the selected trajectory and the trajectory actually followed by the medical instrument,

[0042] [Fig.8] a schematic representation of a bone to be treated and two candidate trajectories to reach a target point in the bone.

[0043] In these figures, identical reference numerals from one figure to another designate identical or analogous elements. For clarity, the elements shown are not necessarily to the same scale, unless otherwise stated.

[0044] Detailed description of at least one embodiment of the invention

[0045] Figure 1 represents a data processing device 10 according to the invention. data processing device 10 includes at least one processor 11, at least one computer memory 12 and at least one display screen 13.

[0046] The computer memory 12 includes program code instructions which, when executed by the processor 11, configures the processor 11 to implement a method for assisting in the selection of at least one optimal trajectory that a medical instrument must follow to perform a minimally invasive medical intervention within an anatomy of interest of a patient.

[0047] Minimally invasive medical procedures can be used, in particular, to remove or biopsy a tumor in an organ or bone, to treat a bone condition, to perform vertebroplasty or cementoplasty, or to stimulate a specific anatomical area. The anatomy of interest may correspond to an organ or bone, for example, a lung, a kidney, the liver, the brain, a vertebra, the tibia, the femur, the hip, the knee, the pelvic bones, the pelvis, etc. The medical instrument may be a needle, an electrode, a probe, a drill, a trocar, a screw, etc.

[0048] Fig. 2 represents the main steps of a process 100 implemented by the treatment device 10 to assist in the selection of an optimal trajectory for a minimally invasive medical intervention.

[0049] As illustrated in [Fig. 2], the process 100 includes a step 101 of obtaining a three-dimensional anatomical model of the patient. The anatomical model includes, in particular, a representation of the body envelope (or a part of the body envelope) and the anatomy of interest of the patient.

[0050] The anatomical model is obtained from medical images previously acquired on the patient. The medical images used to generate the anatomical model are obtained, for example, by computed tomography, magnetic resonance imaging, ultrasound, or positron emission tomography (any medical imaging modality that allows the reconstruction of a three-dimensional volume can be used). Several images are generally required to generate a three-dimensional anatomical model. Alternatively, it is possible to generate a three-dimensional anatomical model from a single image and a statistical model of the patient.

[0051] The anatomical model can be generated directly by the processing device 10, but it can also optionally be generated by another separate device and transmitted to the processing device 10 via communication means.

[0052] Conventionally, three-dimensional modeling is performed, for example, by using segmentation methods on medical images to identify anatomical structures considered relevant to clinical practice. The results of the segmentations can be represented as binary images or surfaces. For example, in the context of minimally invasive ablation of a liver tumor, it may be relevant to segment the liver, the tumor, the lungs, the gallbladder, the bile ducts, the organs of the digestive system, the bones, certain blood vessels (those with a significant diameter), and the patient's body envelope.According to another example, in the context of a minimally invasive intervention on a bone, it may also be relevant to segment the outer covering of the bone to be treated, its cortical part (cortical bone corresponds to the particularly rigid peripheral part of the bone; this corresponds to the "outer shell" of the bone, i.e., a more or less thick wall of the bone), and / or its spongy part (spongy bone corresponds to the bone tissue that forms the porous part of the bone, located below the cortical bone). This three-dimensional modeling of the anatomy can be represented mathematically as follows:

[0053] [Math.3] «=

[0054] where M is the three-dimensional anatomical model and Si are the representations of the segmentations considered to generate this model, the different segmentations all being represented in the same anatomical frame.

[0055] Segmentation methods are generally automatic artificial intelligence methods (no user input is required), semi-automatic methods (a single user input such as a point or segment is required), or interactive methods (segmentation results are iteratively corrected by a user). Manual segmentation could also be considered, but the time required to perform such segmentation makes this alternative impractical in a clinical setting.

[0056] Among artificial intelligence methods, convolutional neural networks are currently the most efficient. However, other methods may offer interesting alternatives, such as tree-based methods. decision-making (“random forest”), on k-means partitioning (“k-means”), on watershed segmentation (“watershed”), on an active contour model (“active contour model”), on level-set methods (“level-set method”), etc.

[0057] As illustrated in [Fig. 2], the method 100 includes an identification step 102, in the anatomical model, of a target point to be reached in the region to be treated (e.g., a tumor) within the patient's anatomy of interest (e.g., the liver). It should be noted that one can also consider the case where several target points must be taken into account (e.g., when several trajectories must be selected for inserting several needles). In this case, this set of target points can be determined from a "virtual" target point and a geometric arrangement that the target points must respect with respect to each other and with respect to the virtual target point (e.g., the target points must correspond to the vertices of a particular geometric shape centered on the virtual target point, or follow a particular distribution in space around the virtual target point).The virtual target point and the geometric arrangement that the target points must respect are input data provided by the user.

[0058] The area to be ablated can be segmented automatically or semi-automatically. The use of correction tools or manual segmentation is possible depending on the desired quality. Once satisfactory segmentation of the area to be treated has been obtained, the position of a target point can be estimated. It should be noted that the area to be treated can be segmented by the treatment device 10, or it can have been previously segmented on the anatomical model by another separate device and then transmitted to the treatment device 10 via communication means.

[0059] Segmentation of the area to be treated can, for example, be used to automatically determine a target point (for example, by calculating the centroid or center of a sphere or encompassing ellipsoid determined according to parameters known a priori). Alternatively, when the procedure involves the insertion of several medical instruments, it is possible to use segmentation to determine a set of several target points, for example, based on a virtual target point (corresponding, for example, to the center of the area to be treated), a geometric arrangement that the target points must respect, and / or a desired ablation coverage (it is indeed possible, for a given medical instrument, to predict an ablation zone that can be obtained around the target point in order to ensure that it covers a desired ablation zone).

[0060] Depending on the type of treatment envisaged (radiofrequency ablation, microwave ablation, cryotherapy or electroporation, biopsy, cementoplasty, etc.), the target point may be find within or around the area to be treated. This estimation of the target point's position can optionally be made based on a model of the ablation zone likely to be obtained when the medical instrument is positioned at this target point.

[0061] The candidate trajectories for the intervention will be determined from the target point(s) thus defined and a set of candidate entry points on the patient's skin. The set of entry points on the skin therefore constitutes an initial domain from which an optimal trajectory must be identified. Indeed, each pair formed by a target point and a candidate entry point constitutes a candidate trajectory. It is therefore necessary to identify candidate entry points on the patient's skin. This can be achieved using methods such as Otsu's, which is well-suited for voxel classification in the case of bimodal intensity distributions. However, in continuous space, there is an infinite number of candidate entry points. Even in the discrete domain, for the image resolutions used in clinical practice, there is a very large number of candidate entry points.It is therefore preferable to carry out sampling in order to reduce the number of candidate points.

[0062] To this end, and as illustrated in [Fig.2], the method 100 includes a step of determining 103 a sampling region and a sampling resolution for sampling candidate entry points in the anatomical model at the level of the patient's body envelope.

[0063] The sampling resolution represents a minimum distance separating two candidate entry points. A compromise must be found between the sampling precision and the computation time required to evaluate the candidate trajectories associated with each candidate entry point. A brute-force approach with a high sampling precision for the candidate entry points would impose a very fast computation time for trajectory evaluation, which would lead to significant constraints (limitations in optimization criteria and / or the use of expensive computing resources). Furthermore, the user would have to visualize a large amount of information.

[0064] It may therefore be advantageous to consider a sampling resolution such that two contiguous trajectories are sufficiently different from each other. The sampling resolution can be defined by default (for example, a minimum distance of at least five millimeters between two entry points on the patient's body surface can be imposed), but it can also be adjusted by the user according to their needs (a compromise between computation time and the desired accuracy).

[0065] To sample candidate entry points, it is particularly possible to determine, in the anatomical model, at the level of the patient's body envelope, A set of points defined by a spherical coordinate system centered on the target point (when several target points are considered, it is possible to process each target point successively in order to determine an optimal trajectory individually for each target point; alternatively, it is possible to process a set of target points in a grouped manner, and in this case, it is possible to sample the candidate entry points with respect to a "virtual" target point corresponding, for example, to a point equidistant from each of the target points). Each point can then be defined by a distance r from the target point and by two angles α and θ. The sampling region can then correspond to the intersection of the body envelope with a cone of angular opening (with angles αmax and θmax) centered on the target point.The calculation is initialized, for example, with a first point at the skin level corresponding to a trajectory with a0 = (30 = 0); the distance between this point and the origin of the coordinate system (the target point) is denoted rOjO. Sampling can be obtained by calculating angles a; and [3; (in radians) such that an increment between each angle corresponds to an arc length on the patient's body envelope corresponding to a predefined resolution parameter. The angles a; and [3j can notably be defined such that, for indices i and j corresponding to strictly positive integers: .

[0066] [Math.l] "zj - "MJ + 7~

[0067] and

[0068] [Math.2]

[0069] where R is a predefined resolution parameter representing a minimum distance separating two candidate entry points at the level of the patient's body envelope. The angles a and [3] are incremented until they cover the desired aperture cone.

[0070] As illustrated in [Fig. 3], angle α can correspond to an orbital angle measured with respect to an anteroposterior axis 53 in a transverse plane 51 of the patient 50. The transverse plane 51 is defined by the target point, the anteroposterior axis 53, and a transverse axis 54 of the patient. Angle α can correspond to a craniocaudal angle formed by the transverse plane 51 and an oblique plane 52. The oblique plane 52 is defined by the target point, the transverse axis 54, and the candidate trajectory 55 formed by the target point and the considered entry point.

[0071] Fig. 4 schematically represents a set of candidate entry points 22 in the anatomical model at the level of the patient's body envelope 21 (the candidate entry points 22 form a "mesh" at the level of the patient's body envelope 21).

[0072] As previously mentioned, it is advantageous to reduce the number of candidate trajectories to limit the overall computation time of the optimal trajectory selection process. To this end, and as illustrated in [Fig. 2], the process 100 includes an elimination step 104 of certain candidate trajectories based on one or more predetermined validity criteria.

[0073] It is advantageous to consider several different validity criteria and evaluate them in a predefined order. This order is defined according to the estimated computation time required to evaluate each validity criterion.

[0074] This involves applying successive simple filters to reduce the set of candidate trajectories. These filters are simple to calculate and allow for the rapid rejection of certain trajectories that are impossible to achieve. These filters are applied from the simplest to the most complex, in terms of computation time, so that the final filters are applied to the smallest possible number of candidate trajectories.

[0075] By way of non-limiting example, the following validity criteria may be taken into account successively.

[0076] Initially, if the medical procedure is assisted by a robot with a robotic arm equipped with a medical instrument, it is possible to eliminate candidate trajectories for which the robotic arm cannot be configured to allow the medical instrument to follow the trajectory in question. Once sampling is complete, the candidate trajectories are filtered based on prior knowledge of the robot's accessibility for the orbital and cranio-caudal angles and for the target point in question. This step depends on the robot model used, the geometry of the tools used to guide the medical instrument, and the positioning of the target point. These parameters are known when the candidate trajectories are determined, and it is therefore possible to significantly reduce the number of candidate trajectories.

[0077] In a second step, it is possible to eliminate candidate trajectories for which the length of the trajectory is greater than the length of the medical instrument envisaged for the intervention.

[0078] In a third step, it is possible to eliminate candidate trajectories for which an object placed on the patient acts as an obstacle. As illustrated in [Fig. 4], this could be, for example, a patient reference 23 used by an optical navigation system to guide the robotic arm. According to another example, it could also be a catheter. Using the segmentation of the patient and the objects placed on them for the procedure, and knowing a priori the three-dimensional geometry of the robotic arm, it is possible to determine trajectories leading to collisions between the robotic arm and objects placed on the patient for the procedure. Such trajectories can be eliminated.

[0079] In a fourth step, it is possible to eliminate candidate trajectories for which there is an intersection of the candidate trajectory with at least one critical anatomical structure internal to the patient's body. By critical anatomical structure, we mean, for example, an organ different from the anatomy of interest (e.g., the lungs, spleen, gallbladder, or kidneys if the anatomy of interest is the liver) or vascular structures at risk (arteries, veins, bile ducts, digestive tract). In general, the segmentations are not represented parametrically; therefore, to detect a collision between an anatomical structure at risk and the trajectory, it is possible to finely sample the latter and retrieve the segmentation value for each point of the sample.A distance between points on the trajectory equal to half the image resolution in the acquisition direction, which is typically on the order of a millimeter, is sufficient for this purpose.

[0080] The validity criteria listed above by way of example can apply equally to the case of a minimally invasive intervention on a soft organ and to the case of a minimally invasive intervention on a bone.

[0081] As illustrated in [Fig. 2], the process 100 includes a calculation step 105 of at least one cost function for each remaining candidate trajectory (i.e., for each candidate trajectory that was not eliminated in the elimination step 104) based on several characteristics relating, for example, to the safety or performance of the intervention. These characteristics are quantified; this means that a measured and normalized value is assigned to each characteristic considered (in other words, each quantified characteristic corresponds to a metric representative of a safety or performance criterion).

[0082] By way of non-limiting example, all or part of the following characteristics may be taken into account for the calculation of a cost function. Unless otherwise specified, each of these characteristics may be taken into account for a minimally invasive procedure on a soft organ or for a minimally invasive procedure on a bone.

[0083] According to a first example, it is possible to take into account a minimum distance between the candidate trajectory and a critical anatomical structure internal to the patient's body. To this end, the anatomical model can include a segmentation of the internal critical anatomical structures. A distance between each point of the trajectory and a given anatomical structure can then be calculated using a distance transform (also called a distance map) of the associated segmentation. When this transformation is applied to a binary image, the result is a new image where the value of each voxel corresponds to the shortest distance between that voxel and the image contour. The minimum distance between the trajectory candidate and a critical internal anatomical structure corresponds to a safety margin which must take into account the factors of imprecision of the medical robot and / or biomechanical hazards during the insertion of the medical instrument.

[0084] According to another example, it is possible to consider the length of the intersection between the candidate trajectory and the anatomy of interest. This characteristic is linked to a safety criterion for the medical procedure. Indeed, a minimum distance traversed by the medical instrument within the anatomy of interest may be required to prevent the spread of tumor cells outside the anatomy of interest during its withdrawal. This characteristic is also linked to a performance criterion because the greater the length of the intersection between the candidate trajectory and the anatomy of interest, the better the stability of the medical instrument within the anatomy of interest during the procedure. Therefore, a compromise must be found depending on the clinical objectives; this compromise may be specific to each procedure.In cases where the minimally invasive procedure aims to insert a screw into a bone, the minimum distance the screw travels within the bone is also relevant to the stability of the screw in the bone.

[0085] According to another example, it is possible to consider a minimum angle of incidence between the candidate trajectory and an anatomical interface traversed by the candidate trajectory. When inserting a medical instrument, for example a needle, for a minimally invasive procedure, the instrument may pass through a number of interfaces, such as the skin or the walls of certain organs (for example, the liver capsule). The angle of incidence of the instrument relative to these interfaces can impact the performance of the instrument placement (potential instrument deflection or organ displacement). This parameter also has implications for safety, since an angle of incidence that is too tangential to an anatomical interface can promote the formation of a hematoma. Calculating the angle of incidence requires the normal to the surface at the point of entry of the trajectory.There are approaches based on surface meshing, but they can be costly, which limits their use in a clinical setting. To reduce computation time, the normal can be calculated as the gradient of the distance transform of the segmentation evaluated at the entry point. It is advisable to calculate the gradient by convolution with the derivative of a Gaussian to obtain normal estimates that are more robust to noise (noise sensitivity is a drawback of finite difference methods). In the case of an intervention on bone, an angle of incidence that is too small can increase the risk of the medical instrument slipping on the bone.

[0086] According to another example, it is possible to take into account an estimated value representative of the stability, under the effect of its own weight, of a medical instrument positioned up to the target point along the candidate trajectory. This value may Specifically, this value can be calculated based on the insertion length of the trajectory: the shorter the insertion length, the more easily the medical instrument can move under its own weight or external forces. This value can also be calculated using a biomechanical model that provides information on, for example, the viscoelasticity of the organs traversed. Trajectories with the lowest risk of causing the medical instrument to move under its own weight will be preferred. This characteristic is more relevant for a soft organ than for a bone.

[0087] According to another example, when the minimally invasive procedure involves ablation, it is possible to consider a minimum distance between the envelope of the area to be treated and the envelope of an estimated ablation zone for the candidate trajectory. This minimum distance corresponds to an estimated ablation margin. For a given medical instrument, ablation zone models are available. These models allow estimation of the theoretical coverage area obtained for each trajectory. For example, it is advantageous to optimize the coverage of a tumor area by ablation (maximizing the margin between the ablated area and the tumor area) while minimizing damage to healthy parenchyma and nearby organs. Generally, ablation zone models are ellipsoidal and eccentric with respect to the distal end of the instrument.The shape and orientation of the tumor are important aspects to consider (the tumor is not necessarily spherical). Therefore, in order to obtain better tumor coverage, certain trajectories may be preferred depending on their orientation and the position of the distal end of the instrument at the end of the trajectory.

[0088] According to another example, it is also possible to take into account an angular deviation between the candidate trajectory and a major axis of the region to be treated. Indeed, in practice, for the ablation of a tumor, it is advisable to attack the tumor along its major axis because the effect of the treatment is more effective along the axis of the medical instrument.

[0089] According to another example, when the minimally invasive procedure involves an ablation, it is also possible to consider a coverage value for the area to be treated. This coverage value can be estimated based on a segmentation of the area to be treated and an estimate of an ablation zone likely to be obtained with the candidate trajectory under consideration. Minimum ablation margins may indeed be required to minimize the recurrence rate, for example, in the case of tumor ablation. In the case of a bone tumor, another important factor is that the ablation zone remains within the bone (the coverage constraints are then dictated by the anatomy and position of the lesion).

[0090] According to another example, when the anatomy of interest is a bone, it is also It is possible to consider a minimum distance between a portion of the candidate trajectory and the cortical bone wall for the portion of the trajectory that is inside the bone. Maximizing this minimum distance between the portion of the candidate trajectory inside the bone and the cortical bone wall is advantageous in order to minimize the risk of the medical instrument, after insertion into the bone, contacting the cortical bone wall again. The actual trajectory followed by the medical instrument may differ from a theoretical trajectory selected due to inaccuracies in instrument placement or biomechanical effects (deflection of the medical instrument, displacement of the anatomy of interest due to patient respiration or effects related to instrument insertion, etc.).Maximizing the minimum distance between the portion of the candidate trajectory that is inside the bone and the cortical bone wall is equivalent to maximizing a safety margin for the candidate trajectory.

[0091] Figure 8 schematically represents a bone 80 to be treated, showing the cortical wall 81 of the bone and the spongy portion 82 of the bone. Reference 83 represents a target point that a medical instrument must reach to treat the bone 80. Figure 8 also shows two candidate trajectories 31-1 and 31-2 for reaching the target point 83 from two candidate entry points 22-1 and 22-2 respectively on the body surface 21 of the patient. In the example illustrated in [Fig.8], candidate trajectory 31-1 could be preferred to candidate trajectory 31-2 because the minimum distance di separating the part of candidate trajectory 31-1 that is inside bone 80 and the cortical wall 81 of the bone is greater than the minimum distance d2 separating the part of candidate trajectory 31-2 that is inside bone 80 and the cortical wall 81 of the bone.

[0092] According to another example, when the anatomy of interest is a bone, it is also possible to consider an angle of incidence between the candidate trajectory and the bone wall. It is indeed advantageous for the candidate trajectory to have an angle of incidence as close as possible to a right angle (90°) to prevent the medical instrument from slipping on the bone wall during insertion (this could damage the bone during insertion of the medical instrument and / or cause the medical instrument to deviate from the selected trajectory). The "angle of incidence" is understood to be the angle formed by the candidate trajectory and the bone at the interface where the trajectory enters the bone.

[0093] In the example illustrated in [Fig.8], candidate trajectory 31-1 could be preferred to candidate trajectory 31-2 because the angle of incidence h formed by candidate trajectory 31-1 and the wall of bone 80 is closer to a right angle than the angle of incidence i2 formed by candidate trajectory 31-1 and the wall of bone 80.

[0094] According to another example, when the anatomy of interest is a bone, it is also It is possible to consider an estimate of the risk of bone fracture following an intervention based on the candidate trajectory under consideration. For example, when the intervention aims to place a screw in a bone to consolidate it, a biomechanical model capable of modeling the bone structure (with strong and weaker areas) as well as the mechanical stresses experienced by the bone (for example, based on the patient's measurements) can be used to estimate the risk of bone fracture following the addition of the screw along the candidate trajectory. This risk can then be compared to the risk of bone fracture without the intervention (i.e., without the consolidation screw) to estimate the reduction in fracture risk provided by the intervention.The risk of fracture can also be determined from a database of past interventions including trajectories that prevented a bone fracture and trajectories that did not prevent a bone fracture.

[0095] The characteristics of a trajectory described above can be quantified and used to construct a cost function that allows trajectories to be ranked according to an objective criterion. Several mathematical functions can be considered to combine the characteristics in order to accurately represent the clinical objectives. The simplest function is a linear combination, for which the cost function C(T) of a trajectory T takes the form of a weighted sum of the characteristics described above:

[0096] [Math.4] EK

[0097] where the weights w; allow control of the "balance of power" of the characteristics y;.

[0098] Different sets of weights w; with predefined values ​​can form different cost functions that can be used as is if they prove suitable for the practitioner's clinical practice. The practitioner also has the option of creating their own cost function by modifying the values ​​of the weights Wi in order to give more or less importance to the characteristics y;, depending on the objectives of the intervention in question. This can be done manually, for example through a user interface, or automatically based on previous interventions. Indeed, for each of the previous interventions, an evaluation (a score) could be assigned to the trajectory used and the characteristics calculated.From this information, a system of equations (with a number N of equations significantly greater than the number K of features considered) can be created and solved using regression methods. Neural networks could also be used to generate a cost function.

[0099] It may be advantageous to use different cost functions representative of different Different optimization categories (e.g., "performance" or "safety") allow for the consideration of predefined or user-defined criteria. A user can then provide an indication, via a user interface, of the cost function to be considered for displaying candidate trajectories on screen 13.

[0100] As illustrated in [Fig.2], the method 100 includes a display step 106, on the display screen 13, superimposed with the anatomical model, of at least some of the remaining candidate trajectories with, for each candidate trajectory displayed, a means of quickly visualizing a value of the cost function.

[0101] It should be noted that the display screen 13 can take different forms: it can be, for example, a flat liquid crystal display of the type of computer screen (LCD panel), but it can also be the screen of an augmented reality headset, in which case the information displayed can be projected directly onto the patient.

[0102] It is possible not to display candidate trajectories for which the cost function is less than a threshold value.

[0103] In specific implementations, the means of quickly visualizing the cost function value of a displayed candidate trajectory corresponds to a color code: different colors are respectively associated with different cost function values. Such a display allows for the rapid identification of the best trajectory according to established clinical criteria and its selection using the user interface. When a trajectory is selected by the user, it is possible to display the quantifications determined for the characteristics of that trajectory in order to provide the user with a detailed view of each characteristic used to calculate the trajectory's cost function. Of course, the system offers the possibility of choosing any candidate trajectory, which makes it possible to manage optimality criteria not captured by the cost function.

[0104] Figure 5 schematically illustrates the display of a set of candidate trajectories 31 on the display screen 13. Each candidate trajectory 31 is represented with a color associated with the value of the cost function calculated for the candidate trajectory 31 in question. The color code 30 used is also displayed. In the example illustrated in Figure 5, the candidate trajectories 31 are represented at the level of the patient's body surface 21. When the user selects one of these candidate trajectories 31, it is possible to display different views of the selected trajectory in the anatomical model (for example, according to different section planes including the selected trajectory).

[0105] As illustrated in [Fig.2], the method 100 includes a selection step 107, by a user, and for the target point considered, of a trajectory considered optimal among the displayed trajectories.

[0106] The selected optimal trajectory can, for example, be used to configure the movement of a robotic arm adapted to hold or guide the medical instrument intended to be used to perform the medical intervention.

[0107] The set of steps for determining 103 a region and a sampling resolution, eliminating 104 certain candidate trajectories, calculating 105 at least one cost function, displaying 106 the candidate trajectories, and selecting 107 a trajectory can be iterated several times to refine the selection of the candidate trajectory that will be used to perform the medical intervention. For this purpose, and as illustrated in [Fig. 2] at step 108, the user can, for example, be prompted to indicate, via a user interface, whether they wish to refine the selected candidate trajectory.

[0108] Thus, in some embodiments, at least two iterations are performed. Advantageously, for a given iteration (different from the first iteration), the new sampling region includes the entry point corresponding to the trajectory selected in the previous iteration, the new sampling region is determined to be smaller than the sampling region determined for the previous iteration, and the sampling resolution is determined to be finer than the sampling resolution determined for the iteration (this means that the minimum distance separating two candidate entry points is smaller).

[0109] In certain implementations, for a given iteration (different from the first iteration), the new sampling resolution and / or the new sampling region is determined based on the variability of the cost function calculated for the previous iteration in the neighborhood of the trajectory selected in the previous iteration. The variability of the cost function can, for example, be measured as a function of the angles α and θ. The greater the variability in the previous iteration, the finer the resolution should be chosen for the new iteration. The lower the variability, the wider the sampling region should be chosen for the new iteration.

[0110] As illustrated in [Fig.6], the process 100 may also include a user evaluation step 110 of at least one displayed candidate trajectory, and an update step 111 of the cost function from the evaluation obtained.

[0111] For each displayed trajectory, the user can evaluate the trajectory by assigning it a score. This score can be used to trigger a possible update of the cost function parameters (for example, if the cost function is a linear combination of several quantified characteristics, the weights of the linear combination can be modified to adapt to the score provided by the user, via, for example, regression algorithms or neural networks).

[0112] The trajectory that was actually followed by the medical instrument during The medical intervention may deviate from the selected candidate trajectory, particularly due to inaccuracies in needle placement or biomechanical effects (needle deflection, displacement of the anatomy of interest due to respiration or insertion-related effects). It is therefore relevant to determine the a posteriori cost function for the actual trajectory followed by the medical instrument, as well as the difference AC(T) between the value C(T) of the cost function calculated for the candidate trajectory selected for the intervention and the value C'(T) of the cost function for the actual trajectory.

[0113] Thus, and as illustrated in [Fig.7], the process 100 may also include the following steps, subsequent to the medical intervention: - a determination 120 in the anatomical model of a real trajectory that was actually followed by the medical instrument during the intervention (this real trajectory can in particular be determined from medical images acquired on the patient at a time when the medical instrument is in place, i.e. when it has reached the target point and is in position to perform the treatment); - a determination 121 of a value of the cost function for the actual trajectory; - a calculation 122 of a difference between the value of the cost function of the candidate trajectory selected for the intervention and the value of the cost function of the actual trajectory.

[0114] Calculating the value C'(T) of the actual trajectory cost function requires estimating the position of the medical instrument after insertion from a control image. This can be done manually (by clicking on the entry point and the end of the instrument) or automatically using image processing methods to segment the instrument and then extract the required information.

[0115] The a posteriori determination of the cost function of the actual trajectory can, in particular, be used as a means of alerting the user to the poor execution of the action (the instrument placement is of lower quality than planned). This alert can be triggered when the AC(T) variation of the cost function is significant. A significance threshold can be determined from a retrospective analysis of trajectories executed in the past for which an evaluation criterion (a score) has been assigned.

[0116] Thus, and as illustrated in [Fig.7], the method 100 may include a display step 123, on the display screen 13, of an indication relating to the value of the deviation calculated in step 122.

[0117] A retrospective analysis of the statistics of the variation AC(T) of the function of The cost of trajectories whose execution conforms to the plan can be calculated to determine typical AC(T) values ​​in order to define equivalence classes for trajectories. Indeed, for a set of trajectories whose execution has been judged to conform to the plan, an AC(T) statistic can be derived to determine the typical variation of the cost function. This value can then be used in the optimal trajectory selection process to avoid presenting a user with trajectories that are too similar (i.e., trajectories whose cost functions do not differ sufficiently).

[0118] Thus, and as illustrated in [Fig.7], process 100 may comprise the following steps: - obtaining 124 of an evaluation, by the user, of the actual trajectory; - a determination 125, based on the calculated gap and the evaluation obtained, with a statistical value representative of the variability of the cost function; - an elimination of 126 candidate trajectories considered equivalent according to the statistical value thus obtained.

[0119] In the method 100 for assisting in the selection of an optimal trajectory, steps 101 to 108, 110, and 111 described with reference to Figures 2 and 6 are all performed before the medical intervention; steps 120 to 126 described with reference to [Fig. 7] are performed after the medical intervention. These steps therefore do not involve the execution of a surgical procedure. Steps 108, 110, 111, and 120 to 126 are optional.

[0120] The data processing device 10 described above allows a practitioner to be offered a set of optimal or near-optimal trajectories for a minimally invasive procedure based on a three-dimensional anatomical model of the patient. The proposed trajectories take into account constraints of different types (for example, practical constraints, performance constraints, and / or safety constraints). The practitioner has the option of choosing a trajectory that may be suboptimal according to a predefined criterion, but preferable for constraints not captured by that criterion.

[0121] The proposed solution allows for a rapid and optimized exploration of all solutions to determine candidate trajectories (a "multi-resolution" approach for sampling candidate entry points). It allows the user to quickly visualize different candidate trajectories and their optimality criteria. The proposed solution is flexible, as it can be adapted to specific intervention needs (increased need for placement accuracy, increased need for safety margins due to potential weakness in the patient's condition, etc). The practitioner also has the possibility of updating optimality criteria (cost function) based on user feedback (evaluation of a trajectory selected for the intervention).

[0122] The above description was primarily based on a case with a single target point (e.g., for a single-needle procedure), or on a case where several target points are considered successively to determine an optimal trajectory individually for each target point (e.g., for a multi-needle procedure). However, in the case of a procedure where several medical instruments must be inserted (e.g., for a multi-needle procedure), it is also possible to consider all the target points as a group (and thus to consider groups of candidate trajectories associated respectively with the different target points). This can, in particular, allow the calculation of the cost function to take into account characteristics that depend on the candidate trajectories with respect to each other.For example, this can allow the calculation of the cost function to take into account a maximum relative angle between the different trajectories (minimizing this relative angle may be advantageous). As another example, it can allow the calculation to account for a deviation between the distance separating the different trajectories and a target value (the target value may, in particular, be based on recommendations from the manufacturer of the medical device used).

Claims

1. Demands A data processing device (10) comprising a processor (11), a computer memory (12) and a display screen (13), the computer memory (12) comprising program code instructions which, when executed by the processor (11), configures the processor (11) to implement a method (100) to assist in the selection of at least one optimal trajectory for a minimally invasive medical intervention within a patient's anatomy of interest, the processor (11) is configured to: - to obtain (101) a three-dimensional anatomical model of the patient from previously acquired medical images of the patient, the anatomical model including a representation of the body envelope (21) and the anatomy of interest of the patient, - identify (102) in the anatomical model at least one target point to be reached in a region to be treated within the patient's anatomy of interest, - perform at least one iteration for which the processor is configured to: • determine (103) a sampling region and a sampling resolution for sampling candidate entry points (22) in the anatomical model at the level of the patient's body envelope (21), each candidate entry point (22) belonging to the sampling region, the sampling resolution being representative of a minimum distance separating two candidate entry points (22), each pair formed by the target point and a candidate entry point (22) forming a candidate trajectory, • eliminate (104) candidate trajectories based on at least one predetermined validity criterion, • for each remaining candidate trajectory, quantify each of a plurality of predetermined characteristics, and calculate (105) at least one cost function from the quantified characteristics, display (106) on the display screen (13), superimposed on the anatomical model, at least some of the remaining candidate trajectories with, for each candidate trajectory (31) displayed, a means of quickly visualizing a value of the cost function, obtain (107) the selection, by a user, for said at least one target point, of a trajectory considered optimal among the candidate trajectories (31) displayed.

2. Device (10) according to claim 1 wherein the processor (11) is configured to perform at least two iterations, and wherein, for an iteration of rank n, n being an integer strictly greater than one: - the sampling region of the iteration of rank n is determined to be smaller than the sampling region determined for the iteration of rank (n-1), and to include the candidate entry point (22) corresponding to the trajectory selected at the iteration of rank (n-1), and - the sampling resolution of the iteration of rank n is determined to be finer than the sampling resolution determined for the iteration of rank (n-1).

3. Device (10) according to claim 2 wherein the sampling resolution of the rank n iteration is determined as a function of a variability of the cost function calculated for the rank (n-1) iteration in the neighborhood of the trajectory selected at the rank (n-1) iteration.

4. Device (10) according to any one of claims 2 to 3 wherein the sampling region of iteration n is determined as a function of a variability of the cost function calculated for iteration (n-1) in the neighborhood of the trajectory selected at iteration (n-1).

5. Device (10) according to any one of claims 1 to 4 wherein, for sampling candidate entry points (22), the processor (11) is configured to determine in the anatomical model, at the level of the body envelope (21) of the patient, a set of points defined by a spherical coordinate system centered on the target point, each point being defined by a distance r^ from the target point and by two angles a; and [3j, the angles a; and [3j being defined such that, for indices i and j corresponding to strictly positive integers: n ■ ■ — n - < • + — and where R is representative of a minimum distance separating two candidate entry points (22) at the level of the body envelope (21) of the patient.

6. Device (10) according to any one of claims 1 to 5 wherein a validity criterion makes it possible to verify, for a given candidate trajectory, at least one of the following: - a validity of the length of the candidate trajectory with respect to a medical instrument envisaged for the intervention, - a possibility of configuring a robotic arm equipped with a medical instrument so that said medical instrument can follow the candidate trajectory, - a presence of objects placed on the patient and obstructing the candidate trajectory, - an intersection of the candidate trajectory with at least one critical anatomical structure internal to the patient's body.

7. Device (10) according to any one of claims 1 to 6 wherein several validity criteria are considered to eliminate candidate trajectories, and the different validity criteria are evaluated in an order defined as a function, for each validity criterion, of an estimated computation time required to evaluate said validity criterion.

8. Device (10) according to any one of claims 1 to 7 wherein the plurality of features of a candidate trajectory comprises at least one of the following features: - a minimum distance between the candidate trajectory and a critical anatomical structure internal to the patient's body, - a length of the intersection between the candidate trajectory and the anatomy of interest, - a minimum angle of incidence between the candidate trajectory and an anatomical interface crossed by the candidate trajectory, - an estimated value representative of the stability, under the effect of its own weight, of a medical instrument positioned up to the target point along the candidate trajectory, - a minimum distance between an envelope of the region to be treated and an envelope of an estimated ablation region for the candidate trajectory, - an angular deviation between the candidate trajectory and a major axis of the region to be treated.

9. Device (10) according to any one of claims 1 to 8 wherein several cost functions are calculated from the quantified features, each cost function being calculated according to a different set of weights assigned respectively to the different quantified features, and the processor (11) is configured to obtain an indication, from a user, of the cost function to be considered for the display of candidate trajectories.

10. Device (10) according to any one of claims 1 to 9 in which the means for quickly visualizing the value of the cost function for each candidate trajectory (31) displayed includes a color code associating different colors respectively with different values ​​of the cost function.

11. Device (10) according to any one of claims 1 to 10 wherein the processor (11) is configured to display the quantifications determined for the user-selected trajectory features.

12. Device (10) according to any one of claims 1 to 11 wherein the processor (11) is configured to obtain (110), for at least one candidate trajectory (31) displayed, a user evaluation of said candidate trajectory, and to update (111) the cost function based on said evaluation.

13. Device (10) according to any one of claims 1 to 12 wherein the processor (11) is configured to: - determine (120) in the anatomical model, post-intervention, an actual trajectory followed by an instrument medical during the intervention, from medical images acquired on the patient at a time when the medical instrument is in place, - determine (121) a value of the cost function for the actual trajectory, - calculate (122) a difference between the value of the cost function of the candidate trajectory selected for the intervention and the value of the cost function of the actual trajectory.

14. Device (10) according to claim 13 wherein the processor (11) is configured to compare the calculated deviation with a predetermined threshold and display (123) an indication relating to the value of the calculated deviation.

15. Device (10) according to claim 13 in which the processor is configured to: - obtain (124) an evaluation of the actual trajectory by the user, - determine (125), based on the calculated deviation and the evaluation obtained, a statistical value representative of the variability of the cost function, - eliminate (126) candidate trajectories considered equivalent according to the statistical value thus obtained.