Device to assist in planning minimally invasive bone procedures

Abstract

The present invention relates to a data processing device for performing a method for assisting in the selection of at least one optimal path for a minimally invasive medical procedure on a bone, the device being configured in particular to identify (102) in a three-dimensional anatomical model a target point to be reached in a patient's bone, and to perform at least one iterative procedure comprising the steps of: determining (103) a sampling area and a sampling resolution for candidate entry points, eliminating (104) candidate paths according to at least one predetermined validity criterion, calculating (105) for each remaining candidate path at least one cost function from a plurality of quantified characteristics, displaying (106) the candidate paths on the anatomical model using means for rapid visualization of the cost function, and selecting (107) an optimal path from the displayed candidate paths.

Description

[Technical Field]

[0001] FIELD OF THE INVENTION This patent application relates to the field of planning minimally invasive medical procedures, possibly assisted by medical robots. In particular, a data processing device is provided for implementing a method for assisting in the selection of an optimal path for a minimally invasive medical procedure within an anatomical structure of interest of a patient. [Background technology]

[0002] Conventional technology To prepare for a minimally invasive medical procedure aimed at reaching a target anatomical region within a patient's anatomical structure of interest using a medical instrument, a practitioner typically plans the procedure using preoperative or intraoperative medical images. The minimally invasive medical procedure may be aimed at biopsying or resecting an organ or bone tumor, performing vertebroplasty or cementoplasty, or even stimulating a specific anatomical region. The anatomical structure of interest may be, for example, the lung, kidney, liver, brain, spine, tibia, knee, etc. The medical instrument may be a needle, electrode, probe, etc.

[0003] Pre-operative or intra-operative medical images may be obtained, for example, by computed tomography, magnetic resonance imaging, ultrasound, or positron emission tomography.

[0004] When planning a procedure, the practitioner defines a target point in the area to be treated of the anatomical structure of interest. The practitioner also defines an entry point for the medical instrument on the patient's skin. These two points define the path the medical instrument must follow to perform the medical procedure.

[0005] Depending on the type of procedure, certain constraints must be taken into consideration: for example, it may be important that the medical device does not pass through high-risk anatomical structures (e.g., organs, bones, or blood vessels).

[0006] The route is usually determined empirically based on the practitioner's knowledge and information gathered from preoperative imaging, however, this route is not always optimal, i.e., it does not guarantee effective treatment of the patient with minimal risk.

[0007] For a given procedure, theoretically, an infinite number of paths are possible, however, the practitioner must choose a path within a limited, sometimes very short, time frame, especially when the procedure is planned shortly before it is performed.

[0008] Furthermore, this work must be performed based on volumetric, and therefore three-dimensional, images of the patient, whereas traditionally these images are presented as two-dimensional cross sections of this volume, making it more difficult for the practitioner to search for the optimal path.Furthermore, if the procedure requires defining multiple paths (e.g., inserting multiple needles), the practitioner's mental planning becomes even more complicated.

[0009] Ultimately, the quality of the route optimization depends heavily on the practitioner and the time they can allocate to this task.

[0010] Methods already exist that evaluate paths proposed by a practitioner, taking into account, for example, the risk of a medical device crossing a high-risk area. Such solutions do not allow a practitioner to quickly find an optimal or near-optimal path for the medical procedure in question.

[0011] Also, planning methods already exist in which entry points, target points, and / or paths are automatically suggested to the practitioner. However, these solutions generally consider the optimality of multiple paths and do not give the user the flexibility to select a different path that may not be optimal but is preferred for reasons that may vary from patient to patient and are not necessarily accounted for by the optimality criteria in question. Summary of the Invention [Problem to be solved by the invention]

[0012] Description of the invention The solution provided in this patent application aims to remedy all or some of the drawbacks of the prior art, in particular the drawbacks mentioned above. [Means for solving the problem]

[0013] To this end, according to a first aspect, there is provided a data processing apparatus comprising a processor, a computer memory, and a display screen, the computer memory including program code instructions that, when executed by the processor, configure the processor to perform a method for assisting in the selection of at least one optimal path for a minimally invasive medical procedure for treating a bone of a patient, the processor comprising: - obtaining a three-dimensional anatomical model of the patient from previously acquired medical images of the patient, the model including a representation of the body envelope and bones to be treated; - identifying in the anatomical model at least one target point to be reached in the bone to be treated; - performing the following repeated steps at least once: In the procedure configured and repeated as described above, the processor: determining a sampling area and a sampling resolution for sampling candidate entry points in an anatomical model on the outer skin of the patient's body, where each candidate entry point belongs to a sampling area, and the sampling resolution represents the minimum distance separating two candidate entry points, each pair formed by a target point and a candidate entry point forming one candidate path; o Eliminating candidate paths by considering at least one predetermined validity criterion; o for each remaining candidate pathway, quantifying each of a plurality of predetermined characteristics including a minimum distance between a portion of the candidate pathway located inside the bone to be treated and the cortical wall of the bone, and calculating at least one cost function from the quantified characteristics; o displaying at least some of the remaining candidate paths on a display screen overlaid on the anatomical model, wherein for each displayed candidate path, a means is provided for quickly visualizing the value of the cost function; obtaining a user selection of a route that is considered optimal from among the displayed candidate routes for said at least one destination point; It is configured as follows.

[0014] This preparation allows the practitioner to propose a set of optimal or near-optimal paths for the minimally invasive procedure based on a three-dimensional anatomical model of the patient. The proposed paths take into account various types of constraints (e.g., practical, performance, and / or safety constraints). The practitioner can select a path that may not be optimal in terms of a predetermined criterion, but is preferable in terms of constraints not addressed by the criterion. The present invention also enables a rapid and optimal search of all solutions to determine candidate paths. This allows the user to quickly visualize various candidate paths and their optimality criteria. The proposed solutions are flexible because they can be adapted to the specific needs of the procedure. The practitioner can also update the optimality criteria (cost function) taking into account user feedback (evaluation of the path selected for the procedure).

[0015] In certain embodiments, the device may further comprise one or more of the following features, taken alone or in any technically possible combination:

[0016] In certain embodiments, the processor is configured to perform at least two iterative procedures: for an iterative procedure of rank n, where n is an integer strictly greater than 1, - the sampling area of ​​the iterative procedure of rank n is determined to be smaller than the sampling area determined for the iterative procedure of rank (n-1) and to include the candidate entry point corresponding to the path selected in the iterative procedure of rank (n-1); The sampling resolution of the iterative procedure of rank n is defined to be a finer resolution than the sampling resolution defined for the iterative procedure of rank (n-1).

[0017] In certain embodiments, the sampling resolution of the rank n iterative procedure is determined by taking into account the variability of the cost function calculated for the rank (n-1) iterative procedure in the neighborhood of the path selected in the rank (n-1) iterative procedure.

[0018] In certain embodiments, the sampling region of the rank n iterative procedure is determined by taking into account the variability of the cost function calculated for the rank (n-1) iterative procedure in the vicinity of the path selected in the rank (n-1) iterative procedure.

[0019] In certain embodiments, to sample the candidate entry points, the processor is configured to determine a set of points on the skin of the patient's body defined by a spherical coordinate system centered on the target point in the anatomical model, each point being a distance r from the target point. i,j and the two angles α i and β j and the angle α i and β j is defined as follows, where the indices i and j correspond to strictly positive integers: [Number 1]

number

number

[0020] In a particular embodiment, one validity criterion allows for verifying, for a given candidate path, at least one of the following: - the adequacy of the length of the candidate pathway for the medical device envisaged for the procedure; - the ability to configure a robotic arm carrying a medical device so that said medical device can follow a candidate path; - the presence of an object attached to the patient that obstructs the candidate path; - Intersection of the candidate pathway with at least one significant anatomical structure within the patient's body.

[0021] In certain embodiments, multiple validity criteria are considered to eliminate candidate paths, and the various validity criteria are evaluated in a prescribed order taking into account, for each validity criterion, the estimated computation time required to assess said validity criterion.

[0022] In certain embodiments, the plurality of characteristics of the candidate pathway includes at least one of the following characteristics: - the minimum distance between the candidate pathway and critical anatomical structures within the patient's body; - the length of the intersection of the candidate path with the bone to be treated; - the minimum angle of incidence between the candidate path and the anatomical interface that the candidate path crosses; the minimum distance between the envelope of the area to be treated and the envelope of the ablation area estimated for the candidate path; - The angular difference between the candidate path and the main axis of the area to be treated.

[0023] In certain embodiments, multiple cost functions are calculated from the quantified characteristics, each cost function being calculated using a different set of respective weightings assigned to the various quantified characteristics, and the processor is configured to obtain a user indication of which cost functions to consider when displaying the candidate routes.

[0024] In certain embodiments, the means for quickly viewing the value of the cost function for each displayed candidate path includes a color code that associates different respective colors with different values ​​of the cost function.

[0025] In certain embodiments, the processor is configured to display the quantification determined for the characteristic of the pathway selected by the user.

[0026] In certain embodiments, the processor is configured to obtain, for at least one displayed candidate route, a user rating of said candidate route and update the cost function to take said rating into account.

[0027] In a particular embodiment, the processor: - determining, after the procedure, in the anatomical model the actual path taken by the medical instrument during the procedure from medical images of the patient taken at the time the medical instrument was in place; - determining the value of the cost function for the actual path; - calculating the difference between the value of the cost function of the candidate path selected for action and the value of the cost function of the actual path; It is configured as follows.

[0028] In certain embodiments, the processor is configured to compare the calculated difference to a predetermined threshold and display an indication related to the calculated difference value.

[0029] In a particular embodiment, the processor: - Obtain actual route ratings by users; determining a statistical value that represents the variability of the cost function, taking into account the calculated difference and the obtained evaluation; - eliminating candidate paths that are considered equivalent taking into account the statistics thus obtained; It is configured as follows.

[0030] Diagram Overview The invention can be better understood on reading the following description, given entirely by way of non-limiting example, and with reference to FIGS. [Brief explanation of the drawings]

[0031] [Figure 1] 1 is a schematic representation of a data processing device according to the invention; [Figure 2] Schematic representation of the main steps of a method to aid in the selection of an optimal route for a medical procedure. [Figure 3]Schematic representation of the definition of orbital angle and head-tail angle to define the sampling area. [Figure 4] A schematic representation of a set of candidate entry points in an anatomical model on the patient's body envelope. [Figure 5] 1 is a schematic representation of a display of a set of candidate pathways for a medical procedure, where the optimality criteria are displayed via a scale, here grayscale. [Figure 6] 10 is a schematic representation of an additional step to take into account user ratings of candidate routes. [Figure 7] 10 is a schematic representation of additional steps to account for differences between the selected path and the path actually followed by the medical device. [Figure 8] Schematic representation of the bone to be treated and two possible paths to reach the target point on that bone. DETAILED DESCRIPTION OF THE INVENTION

[0032] In these figures, the same reference numbers from one figure to another refer to the same or similar elements. For clarity, unless otherwise noted, elements shown are not necessarily to scale.

[0033] A detailed description of at least one embodiment of the present invention 1 shows a data processing apparatus 10 according to the present invention. The data processing apparatus 10 comprises at least one processor 11, at least one computer memory 12, and at least one display screen 13.

[0034] The computer memory 12 comprises program code instructions that, when executed by the processor 11, configure the processor 11 to perform a method for assisting in the selection of at least one optimal path that a medical instrument must follow to perform a minimally invasive medical procedure within an anatomical structure of interest of a patient.

[0035] The minimally invasive medical procedure may be aimed at biopsying or removing tumors, treating bone diseases, performing vertebroplasty or cementoplasty, or stimulating specific anatomical regions, among other things. The anatomical structures of interest may correspond to organs or bones, such as the lungs, kidneys, liver, brain, vertebrae, tibia, femur, hip, knee, pelvic bone, pelvis, etc. The medical instrument may be a needle, electrode, probe, drill, trocar, screw, etc.

[0036] FIG. 2 illustrates the main steps of a method 100 performed by the processing device 10 to assist in the selection of an optimal path for a minimally invasive medical procedure.

[0037] 2, method 100 includes step 101 of obtaining a three-dimensional anatomical model of a patient. The anatomical model includes, among other things, a representation of the patient's body envelope (or a portion of the body envelope) and anatomical structures of interest.

[0038] Anatomical models are derived from previously acquired medical images of a patient. The medical images used to generate the anatomical model may be obtained, for example, by computed tomography, magnetic resonance imaging, ultrasound, or positron emission tomography (any medical imaging technique that allows for three-dimensional volume reconstruction may be used). To generate a three-dimensional anatomical model, multiple images are generally required. Alternatively, a three-dimensional anatomical model can be generated from a single image and a statistical model of the patient.

[0039] The anatomical model may be generated directly by the processing device 10, but may optionally be generated by a separate, independent device and transmitted to the processing device 10 via a communication means.

[0040] Traditionally, three-dimensional modeling is performed using methods for segmenting anatomical structures on medical images that are deemed relevant by clinical practice, for example. The segmentation result may take the form of a binary image or a surface. For example, in the context of minimally invasive removal of a liver tumor, it may be relevant to segment the liver, tumor, lungs, gallbladder, bile ducts, digestive organs, bones, certain blood vessels (those with large diameters), and the outer skin of the patient's body. By another example, in the context of minimally invasive procedures on bone, it may be relevant to segment the outer layer of the bone to be treated, its cortical part (cortical bone corresponds to the particularly hard peripheral part of the bone and is the "outer shell" of the bone, i.e., the relatively 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 and is located below the cortical bone). This three-dimensional anatomical modeling can be mathematically expressed as follows: [Number 3]

number

[0041] Segmentation methods are generally automatic (no user input required), semi-automatic (single user input required such as a point or segment), or interactive (segmentation results are iteratively refined by the user). Manual segmentation is also possible, but the time required to perform such segmentation procedures makes this alternative of little relevance in clinical practice.

[0042] Among artificial intelligence methods, convolutional neural networks are currently the most efficient, however other methods, such as those based on random forests, k-means clustering, watershed segmentation, active contour models, level set methods, etc., may be advantageous alternatives.

[0043] As shown in FIG. 2 , method 100 includes step 102 of identifying target points to be reached within an anatomical model within a treatment target region (e.g., a tumor) within an anatomical structure of interest (e.g., a liver) of a patient. Note that it is also possible to envision cases where multiple target points must be considered (e.g., multiple paths must be selected for inserting multiple needles). In this case, this set of target points can be determined from “virtual” target points and the geometric configuration the target points must have relative to each other and the virtual target points (e.g., the target points must correspond to the vertices of a particular geometric shape centered on the virtual target points, or in fact must have a particular spatial distribution around the virtual target points). The virtual target points and the geometric configuration the target points must have are input data provided by a user.

[0044] The region to be ablated can in particular be segmented automatically or semi-automatically. Depending on the desired quality, manual segmentation or correction tools can be used. Once a satisfactory segmentation of the region to be treated is obtained, the position of the target point to be reached can be estimated. It should be noted that the region to be treated can be segmented by the processing device 10, or indeed could have been pre-segmented on an anatomical model by another independent device and transmitted to the processing device 10 by communication means.

[0045] Segmentation of the treatment area can be used, for example, to automatically determine target points (e.g., via calculation of the centroid or center of an enclosing sphere or ellipsoid determined using a priori known parameters). Alternatively, if the procedure involves the insertion of multiple medical instruments, segmentation can be used to determine a set of multiple target points, taking into account, for example, a virtual target point (e.g., corresponding to the center of the treatment area), the geometric arrangement the target points should have, and / or the desired ablation coverage (specifically, for a given medical instrument, it is possible to predict the ablation zone that is likely to be obtained around the target point to ensure coverage of the area where ablation is desired).

[0046] Depending on the type of treatment envisaged (radiofrequency ablation, microwave ablation, cryotherapy or electroporation, biopsy, cementoplasty, etc.), the target point may be located within or around the area to be treated, and the location of this target point may optionally be estimated in this way taking into account modelling of the ablation zone that is likely to be obtained when the medical device is positioned at the target point.

[0047] A candidate treatment path is determined from one or more target points thus determined and a set of candidate entry points on the patient's skin. The set of entry points on the skin thus forms an initial domain for identifying an optimal path. Specifically, each set formed by a target point and a candidate entry point forms one candidate path. Therefore, it is necessary to identify candidate entry points on the patient's skin. This can be achieved through methods such as Otsu's method, which is well suited to classifying voxels in the case of bimodal intensity distribution. However, in a continuous space, there are an infinite number of candidate entry points. Even in a discrete domain, the number of candidate entry points is very large at the image resolutions used in clinical practice. Therefore, sampling is desirable to reduce the number of candidate points.

[0048] To this end, as shown in FIG. 2, the method 100 includes a step 103 of determining a sampling area and a sampling resolution for sampling candidate entry points in an anatomical model at the outer skin of the patient's body.

[0049] The sampling resolution represents the minimum distance separating two candidate entry points. A compromise must be found between the fineness of the sampling and the computation time required to evaluate each candidate path associated with each candidate entry point. A "brute force" approach, with a high granularity of sampling of candidate entry points, requires a very fast computation for the path evaluation, which results in strong constraints (restricted optimization criteria and / or the use of expensive computational means). Furthermore, the user must take into account a large amount of information.

[0050] Therefore, it may be advantageous to consider the sampling resolution so that two successive paths are sufficiently different from each other. The sampling resolution may be determined by default (e.g., a minimum distance between two entry points of at least 5 millimeters on the patient's body surface may be required), but may also be adjusted by the user according to need (a compromise between calculation time and desired accuracy).

[0051] To sample candidate entry points, it is conceivable to determine a set of points on the anatomical model, on the skin of the patient's body, defined by a spherical coordinate system centered on the target point (if multiple target points are considered, it is conceivable to process each target point in turn in order to determine an optimal path for each of them individually; alternatively, it is possible to process the set of target points together, in which case it is possible to sample candidate entry points relative to "virtual" target points corresponding to points that are, for example, equidistant from each of the target points). Each point is at a distance r from the target point. i,j and the two angles α i and β j The sampling area is centered on the body skin and the target point (angle α max and β max The calculation starts, for example, at an initial point on the skin corresponding to the path where α = β = 0; the distance between this point and the origin of the coordinate system (the target point) is r 0,0 The sampling is performed by varying the angle α such that the increment between each angle corresponds to an arc length on the skin of the patient's body that corresponds to a predefined resolution parameter. i and β i (in radians) can be calculated by i and β j can be defined as follows, where the indices i and j correspond to strictly positive integers: [Number 1]

number

number

[0052] 3, angle α may correspond to a trajectory angle measured relative to an anterior-posterior axis 53 in a transverse plane 51 of a patient 50. The transverse plane 51 is defined by the target point, the anterior-posterior axis 53, and the patient's transverse axis 54. Angle β may 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 a candidate trajectory 55 formed by the target point and the entry point in question.

[0053] FIG. 4 shows a schematic set of candidate entry points 22 in an anatomical model on the outer skin 21 of a patient's body (the candidate entry points 22 form a "mesh" on the outer skin 21 of the patient's body).

[0054] As mentioned above, it is advantageous to reduce the number of candidate routes in order to limit the overall computation time of the method for selecting the optimal route. To this end, as shown in Figure 2, the method 100 includes a step 104 of eliminating certain candidate routes by taking into account one or more predetermined validity criteria.

[0055] It is advantageous to consider several different validity criteria and evaluate them in a predetermined order, which is determined by taking into account the estimated computational time required to assess each validity criterion.

[0056] The problem is to apply successive simple filters to reduce the number of candidate paths. These filters are easy to compute and allow certain impossible paths to be quickly rejected. The filters are applied from the simplest to the most complex in terms of computation time, so that the final filter is applied to the smallest number of possible candidate paths.

[0057] By way of non-limiting example, the following appropriateness criteria may be considered in sequence:

[0058] In a first step, if the medical procedure is assisted by a robot with a robotic arm equipped with a medical instrument, it is possible to eliminate candidate paths for which the robotic arm cannot be configured to make the medical instrument follow the path in question. Once sampling is complete, the candidate paths are filtered based on prior knowledge of the trajectory and longitudinal angles and the robot's ability to access the target point in question. This step depends on the robot model used, the shape of the tool guiding the medical instrument, and the location of the target point. Since these parameters are known at the time the candidate paths are determined, the number of candidate paths can be significantly reduced.

[0059] In a second step, candidate paths can be eliminated where the path length exceeds the length of the medical instrument envisioned for the procedure.

[0060] In a third step, it is possible to eliminate candidate paths where an object attached to the patient becomes an obstacle. As shown in FIG. 4, such an object can be, for example, a patient fiducial 23 used in an optical navigation system to guide the robotic arm. According to another example, it can be a catheter. By segmenting the patient and the object attached to the patient for the procedure and by prior knowledge of the three-dimensional geometry of the robotic arm, it is possible to determine paths that result in a collision between the robotic arm and the object attached to the patient for the procedure. Such paths can be eliminated.

[0061] In a fourth step, it is possible to eliminate candidate paths that intersect with at least one critical anatomical structure in the patient's body. Critical anatomical structures are, for example, organs other than the anatomical structure of interest (e.g., lungs, spleen, gallbladder, or kidneys if the anatomical structure of interest is the liver) or high-risk vascular structures (arteries, veins, bile ducts, digestive tract). Since segmentation is generally not parametric, to detect collisions between the high-risk anatomical structures and the path, it is possible to finely sample the latter and determine the segmentation value for each point in the sample. A distance between points on the path equal to half the image resolution in the acquisition direction, which is usually on the order of one millimeter, is sufficient for this purpose.

[0062] The appropriateness criteria listed above as examples are applicable to both minimally invasive procedures on soft tissue and minimally invasive procedures on bone.

[0063] 2, method 100 includes a step 105 of calculating at least one cost function for each remaining candidate pathway (i.e., each candidate pathway not eliminated in elimination step 104) based on a number of characteristics related to, for example, the safety or performance of the procedure. These characteristics are quantified, i.e., a measured and standardized value is assigned to each characteristic in question (in other words, each quantified characteristic corresponds to a reference value representing a safety or performance criterion).

[0064] By way of non-limiting example, all or some of the following characteristics may be considered when calculating the cost function: Unless otherwise specified, each of these characteristics may be considered for minimally invasive procedures on soft tissue or minimally invasive procedures on bone.

[0065] According to a first example, it is possible to take into account the minimum distance between a candidate path and important anatomical structures within the patient's body. To this end, the anatomical model may include a segmentation of important internal anatomical structures. The distance between each point of the path and a given anatomical structure can then be calculated using a distance transform (also called a distance map) of the associated segmentation. Applying this transform to the binary image generates a new image in which the value of each voxel corresponds to the minimum distance between that voxel and the edge of the image. The minimum distance between the candidate path and important internal anatomical structures corresponds to a safety margin that must take into account factors that affect the inaccuracy of the medical robot and / or uncontrollable biomechanical variables when inserting a medical instrument.

[0066] According to another example, it is possible to take into account the length of intersection between the candidate path and the anatomical structure of interest. This characteristic is related to safety criteria for medical procedures. Specifically, during tumor cell removal, it may be necessary for the medical instrument to pass a minimum distance through the anatomical structure of interest to prevent the spread of tumor cells outside the anatomical structure of interest. This characteristic is also related to performance criteria, since the longer the intersection length between the candidate path and the anatomical structure of interest, the greater the stability of the medical instrument within the anatomical structure of interest during the procedure. Therefore, there is a compromise to be found, taking into account the clinical objectives. This compromise may vary from procedure to procedure. If the objective of a minimally invasive procedure is to insert a screw into bone, the minimum distance the screw penetrates into the bone is also related to the stability of the screw within the bone.

[0067] Another example is to consider the minimum angle of incidence between a candidate path and the anatomical interfaces it crosses. When inserting a medical instrument, such as a needle, during a minimally invasive procedure, the instrument may pass through a number of specific interfaces, such as the skin or the walls of certain organs (e.g., the liver capsule). The angle of incidence of the instrument with respect to these interfaces can affect the effectiveness of the instrument's placement (potential deflection of the instrument or organ displacement). This parameter also impacts safety, since an angle of incidence that is too tangential to the anatomical interface can promote hematoma formation. The calculation of the angle of incidence requires the surface normal at the entry point of the proposed path. Surface mesh-based approaches exist, but their cost prohibits their use in clinical settings. To reduce computation time, the normal can be calculated as the gradient of the segmentation distance transform evaluated at the entry point. To make the normal estimate more robust to noise, it is preferable to calculate the gradient by convolution with a Gaussian derivative (the drawback of finite difference methods is their susceptibility to noise). In the case of procedures on bone, an angle of incidence that is too small may increase the risk of the medical instrument slipping on the bone.

[0068] According to another example, it is possible to take into account an estimated value that describes the stability under the influence of the weight of a medical device positioned along a candidate path to a target point. This value can be calculated taking into account the insertion length of the path in question: the shorter the insertion length, the easier the medical device can be displaced under the influence of its own weight or external forces. This value can also be calculated, for example, using a biomechanical model that provides information about the viscoelasticity of the organs that are traversed. Preference is given to paths that present the least risk of the medical device being displaced under its own weight. This property is more relevant for organs that are softer than for bones.

[0069] According to another example, if a minimally invasive procedure corresponds to ablation, it is possible to take into account the minimum distance between the envelope of the area to be treated and the envelope of the ablation area estimated for a candidate path. This minimum distance corresponds to the estimated ablation margin. For a given medical instrument, models of the ablation zone are available. These models allow for estimating the theoretical area of ​​coverage obtained for each path. For example, it is advantageous to optimize the coverage of the cancerous area through ablation (maximizing the margin between the ablation area and the cancerous area) while minimizing damage to healthy parenchyma and nearby organs. Typically, the ablation zone model is ellipsoidal and off-centered with respect to the distal end of the instrument. The shape and orientation of the tumor are important aspects to consider (tumors are not necessarily spherical). Therefore, to obtain better tumor coverage, certain paths may be preferred depending on their orientation and the location of the distal end of the instrument at the end of the path.

[0070] According to another example, it is also possible to take into account the angular difference between the candidate path and the major axis of the treatment target area. In particular, in practice, to ablate a tumor, it is desirable to attack the tumor along its major axis, since the treatment is more effective along the axis of the medical instrument.

[0071] According to another example, if the minimally invasive procedure corresponds to a resection, it is also possible to take into account the coverage value of the treatment target area. This coverage value can be estimated taking into account the segmentation of the treatment target area and an estimate of the likely resection area obtained for the candidate path in question. Specifically, for example, in the case of tumor resection, a minimum resection margin may be required to minimize the recurrence rate. In the case of bone tumors, another important factor is that the resection zone remains within the bone (coverage constraints are determined by the anatomical structure and location of the lesion).

[0072] According to another example, if the anatomical structure of interest is a bone, for the portion of the path that is inside the bone, it is also possible to take into account the minimum distance between the part of the candidate path that is inside the bone and the cortical layer. Specifically, in order to minimize the risk of the medical instrument contacting the cortical layer again after insertion into the bone, it is advantageous to maximize the minimum distance between the part of the candidate path that is inside the bone and the cortical layer of the bone. Specifically, the actual path followed by the medical instrument may differ from the selected theoretical path as a result of inaccuracies in the placement of the medical instrument or as a result of biomechanical effects (such as deflection of the medical instrument, movement of the anatomical structure of interest due to effects associated with the patient's breathing or the insertion of the instrument, etc.). Maximizing the minimum distance between the part of the candidate path that is inside the bone and the cortical layer of the bone means maximizing the safety margin of the candidate path.

[0073] 8 schematically illustrates a bone 80 to be treated, comprising a cortical layer 81 of the bone and a cancellous portion 82 of the bone. Reference numeral 83 indicates a target point to be reached by a medical instrument in order to treat the bone 80. FIG. 8 also illustrates two candidate paths 31-1 and 31-2 for reaching the target point 83 from two candidate entry points 22-1 and 22-2, respectively, on the patient's body surface 21. In the example illustrated in FIG. 8, candidate path 31-1 may be preferred over candidate path 31-2 because the minimum distance d1 between the portion of candidate path 31-1 that is within the bone 80 and the cortical layer 81 of the bone is greater than the minimum distance d2 between the portion of candidate path 31-2 that is within the bone 80 and the cortical layer 81 of the bone.

[0074] According to another example, when the anatomical structure of interest is a bone, it is also possible to take into account the angle of incidence between the candidate path and the wall of the bone. Specifically, to prevent the medical instrument from slipping on the wall of the bone during insertion (which could damage the bone during insertion of the medical instrument and / or cause the medical instrument to deviate from the selected path), it is advantageous for the candidate path to have an angle of incidence as close as possible to a right angle (a 90° angle). The "angle of incidence" is the angle formed by the candidate path and the bone at the interface where the path penetrates the inside of the bone.

[0075] In the example shown in FIG. 8, the angle of incidence i1 formed by candidate path 31-1 and the wall of bone 80 is closer to a right angle than the angle of incidence i2 formed by candidate path 31-1 and the wall of bone 80, so candidate path 31-1 may be preferable to candidate path 31-2.

[0076] According to another example, if the anatomical structure of interest is a bone, an estimate of the fracture risk as a result of a procedure using the candidate pathway in question can be taken into account. For example, in the case of a procedure aimed at inserting a screw into the bone to strengthen it, a biomechanical model that can model the bone's structure (including hard and weak areas of the bone) and the mechanical stresses to which the bone is subjected (e.g., dependent on patient measurements) can be used to estimate the fracture risk as a result of inserting the screw along the candidate pathway in question. This risk can optionally be estimated by the extent to which the procedure reduces the fracture risk compared to the fracture risk that would occur if the procedure were not performed (i.e., without the strengthening screw). The fracture risk can also be determined from a database of past procedures, including those that prevented and failed fractures.

[0077] The characteristics of the pathways mentioned above can be quantified and used to construct cost functions that allow to classify pathways according to objective criteria. A large number of mathematical functions can be considered to combine the characteristics in order to effectively represent the clinical goal. The simplest function is a linear combination, where the cost function C(T) of a pathway T takes the form of a weighted sum of the characteristics mentioned above: [Number 4]

number

[0078] weights w with predefined values iVarious sets of y can form different cost functions that can be used as is if they prove to be suitable for the practitioner's clinical practice. The practitioner can select the characteristic y, taking into account the goal of the treatment in question. i weight w to give importance to i It is also possible to create one's own cost function by modifying the values ​​of . This can be done manually, for example, via a user interface, or automatically based on previous actions. Indeed, for each previous action, a rating (score) can be assigned to the path used and the properties calculated. Based on this information, a system of equations (number of equations N much larger than the number K of properties in question) can be created and solved by regression methods. Neural networks can also be used to generate the cost function.

[0079] It may be advantageous to use different cost functions representing different optimization categories (e.g., "performance" or "safety"), allowing predefined or user-customized criteria to be taken into account. The user may provide an indication via the user interface of which cost functions should be considered when displaying candidate routes on the display screen 13.

[0080] As shown in FIG. 2, the method 100 includes a step 106 of displaying at least some of the remaining candidate paths on the display screen 13, overlaid on the anatomical model, with means for quickly displaying the value of the cost function for each displayed candidate path.

[0081] It should be noted that the display screen 13 can take various forms: it can for example be a flat LCD computer screen (LCD monitor), but it can also be the screen of an augmented reality headset, in which case the displayed information can be projected directly onto the patient.

[0082] It is also possible to not display candidate routes for which the cost function is below a threshold.

[0083] In a particular model of embodiment, the means for quickly displaying the value of the cost function for the displayed candidate pathway corresponds to a color code: different respective colors correspond to different values ​​of the cost function. Such a display makes it possible to quickly identify the best pathway according to established clinical criteria and select it using the user interface. Once a pathway is selected by the user, the quantifications determined for the pathway's characteristics can be displayed to provide the user with a detailed view of each characteristic used to calculate the pathway's cost function. Of course, the system provides the ability to select any candidate pathway, which makes it possible to manage optimality criteria not captured by the cost function.

[0084] Figure 5 shows schematically the display of a set of candidate paths 31 on the display screen 13. Each candidate path 31 is displayed in a color that is related to the value of the cost function calculated for the candidate path 31 in question. The color code 30 used is also displayed. In the example shown in Figure 5, the candidate paths 31 are displayed on the patient's body surface 21. When the user selects one of these candidate paths 31, various views of the selected path can be displayed on the anatomical model (e.g., at different cutting planes that include the selected path).

[0085] As shown in FIG. 2, the method 100 includes a step 107 in which the user selects the most optimal of the displayed routes with respect to the destination point in question.

[0086] The selected optimal path can be used, for example, to configure the movement of a robotic arm adapted to hold or guide a medical instrument intended to be used to perform a medical procedure.

[0087] All of the steps of determining region and sampling resolution 103, eliminating certain candidate paths 104, calculating at least one cost function 105, displaying candidate paths 106, and selecting a path 107 may be repeated multiple times to refine the selection of candidate paths to be used in performing the medical procedure. To this end, for example, the user may be asked via a user interface to indicate whether they wish to refine the selected candidate path, as shown in step 108 of Figure 2.

[0088] Thus, in a particular model of embodiment, at least two iterative procedures are performed. Advantageously, for a given iterative procedure (different from the first iterative procedure), a new sampling area is defined that includes an entry point corresponding to a path selected in the previous iterative procedure, the new sampling area is defined to be a smaller area than the sampling area defined for the previous iterative procedure, and the sampling resolution is defined to be a finer resolution than the sampling resolution defined for that iterative procedure (which means that the minimum distance separating two candidate entry points is smaller).

[0089] In a particular model of implementation, for a given iteration (different from the first iteration), a new sampling resolution and / or a new sampling area is determined taking into account the variability of the cost function calculated for the previous iteration in the vicinity of the path selected in the previous iteration. The variability of the cost function can be measured, for example, as a function of the angles α and β. The greater the variability in the previous iteration, the more it is recommended to select a finer resolution for the new iteration. The smaller the variability, the more it is recommended to select a wider sampling area for the new iteration.

[0090] As shown in FIG. 6, the method 100 may include a step 110 in which a user rates at least one displayed candidate route, and a step 111 in which a cost function is updated based on the obtained rating.

[0091] For each displayed path, the user may rate the path by assigning it a score, which can be used to trigger an optional update of the parameters of the cost function (e.g., 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, e.g., via a regression algorithm or neural network).

[0092] The path actually followed by the medical device during the medical procedure may deviate from the selected candidate path, especially as a result of inaccuracies in needle placement or biomechanical effects (needle deflection, movement of the anatomical structures of interest due to breathing or insertion-related effects). It is therefore appropriate to determine a cost function a posteriori for the path actually followed by the medical device, and also to determine the difference ΔC(T) between the cost function value C(T) calculated for the candidate path selected for the procedure and the cost function value C'(T) of the actual path.

[0093] Thus, as shown in FIG. 7, the method 100 may also include the following steps after the medical procedure: - a step 120 of determining in the anatomical model the actual path that the medical instrument actually followed during the procedure (this actual path can be determined from medical images of the patient taken at the time when the medical instrument was in position, i.e. when it had reached the target point and was in a position to perform the treatment); - a step 121 of determining the value of the cost function of the actual path; - A step 122 of calculating the difference between the value of the cost function of the candidate path selected for treatment and the value of the cost function of the actual path.

[0094] Calculating the cost function value C'(T) for the actual path requires estimating the position of the medical instrument after insertion from the control images, which can be done manually (by clicking on the entry point and the instrument's distal end) or automatically by using image processing methods to segment the instrument and extract the required information.

[0095] The post-hoc determination of the cost function of the actual path can be used, in particular, as a means to warn the user of incorrect execution of the procedure (lower quality of instrument placement than planned). This warning can be triggered if the variation of the cost function ΔC(T) is significant. The significance threshold can be determined by post-hoc analysis of past paths to which evaluation criteria (scores) have been assigned.

[0096] Thus, as shown in FIG. 7, the method 100 may include a step 123 of displaying on the display screen 13 a display associated with the difference value calculated in step 122 .

[0097] A post-hoc analysis of statistics of the cost function variation ΔC(T) for routes followed as planned can be performed to determine a typical value of ΔC(T) to determine equivalence classes of routes. Specifically, for a set of routes determined to have been followed as planned, a statistical value of ΔC(T) can be derived to determine a typical variation of the cost function. This value can be used by method 100 to select an optimal route so as not to present routes that are too similar (i.e., routes whose cost functions are not sufficiently different) to the user.

[0098] Thus, as shown in FIG. 7, the method 100 may include the following steps: - a step 124 of obtaining an actual route rating by the user; - a step 125 of determining a statistic representative of the variability of the cost function, taking into account the calculated difference and the obtained evaluation; - A step 126 of eliminating candidate paths that are considered equivalent taking into account the statistics thus obtained.

[0099] In the method 100 for assisting in optimal path selection, steps 101-108, 110, and 111 described with reference to Figures 2 and 6 are all performed before the medical procedure, and steps 120-126 described with reference to Figure 7 are performed after the medical procedure. Therefore, no surgical procedure is performed during these steps. Steps 108, 110, 111, and 120-126 are optional.

[0100] The above-described data processing device 10 enables the practitioner to suggest a set of optimal or near-optimal paths for a minimally invasive procedure based on a three-dimensional anatomical model of the patient. The suggested paths take into account various types of constraints (e.g., practical, performance-related, and / or safety constraints). The practitioner can select paths that may not be optimal in terms of a predetermined criterion, but are preferable in terms of constraints not addressed by the criterion.

[0101] The proposed solution allows for a fast and optimal exploration of a set of solutions with the aim of determining a candidate path (a "multi-resolution" approach for sampling candidate entry points). This allows the user to quickly visualize various candidate paths and their optimality criteria. The proposed solution is flexible, as it can be adapted to the specific needs of the procedure (e.g., the need for increased placement accuracy, the need for increased safety margins as a result of potential weaknesses in the patient's condition, etc.). The practitioner can further update the optimality criteria (cost function) taking into account feedback from the user (evaluation of the path selected for the procedure).

[0102] The above description has been given primarily for the case of a single target point (e.g., "single-needle" procedures) or indeed for the case of sequentially considering multiple target points and determining the optimal path for each target point individually (e.g., "multi-needle" procedures). However, in the case of procedures in which multiple medical instruments must be inserted (e.g., "multi-needle" procedures), it is also possible to consider a set of target points together (and thus a respective group of candidate paths associated with the various target points). This makes it possible, in particular, to take into account characteristics that depend on the candidate paths relative to one another in the calculation of the cost function. For example, this may make it possible to take into account the maximum relative angle between the various paths in the calculation of the cost function (it may be advantageous to minimize this relative angle). According to another example, this may make it possible to take into account the difference between the distance separating the various pairs of paths and a target value (the target value may be derived, in particular, from the recommendations of the manufacturer of the medical device used).

Claims

1. A data processing device (10) comprising a processor (11), computer memory (12), and a display screen (13), The computer memory (12), when executed by the processor (11), includes program code instructions that configure the processor (11) to perform a method (100) that assists in selecting at least one optimal route for a minimally invasive medical procedure to treat a patient's bone, The processor (11) is From previously acquired medical images of the patient, a three-dimensional anatomical model of the patient is obtained, including representations of the body's outer skin (21) and the bones to be treated (101). In the anatomical model, at least one target point to be reached within the bone to be treated is identified (102), Perform the following repeated steps at least once: The processor is configured such that, in the repeated procedure, On the skin (21) of the patient's body, a sampling area and sampling resolution are determined (103) for sampling candidate entry points (22) in the anatomical model, each candidate entry point (22) belongs to the sampling area, the sampling resolution represents the minimum distance separating two candidate entry points (22), and each pair is formed by the target point and the candidate entry points (22), forming one candidate path. Candidate routes are eliminated by considering at least one predetermined validity criterion (104), For each of the remaining candidate pathways, a plurality of predetermined characteristics are quantified, including the minimum distance between the portion of the candidate pathway located inside the bone to be treated and the cortical wall of the bone, and at least one cost function is calculated from the quantified characteristics (105). On the display screen (13), at least some of the remaining candidate pathways are displayed (106) superimposed on the anatomical model, and means are provided for quickly visualizing the value of the cost function for each displayed candidate pathway (31). For at least one of the target points, the user selects the route that is considered optimal from among the displayed candidate routes (31) (107). A data processing device (10) configured as follows.

2. The processor (11) is configured to perform at least two iterative procedures, and for an iterative procedure of rank n where n is an integer strictly greater than 1, The sampling area of ​​the repeating procedure of rank n is determined to be smaller than the sampling area determined for the repeating procedure of rank (n-1), and to include candidate entry points (22) corresponding to the path selected in the repeating procedure of rank (n-1). The sampling resolution of the repeated procedure of rank n is determined to be finer than the sampling resolution determined for the repeated procedure of rank (n-1). The apparatus (10) according to claim 1.

3. The apparatus (10) according to claim 2, wherein the sampling resolution of the rank n iterative procedure is determined taking into account the variability of the cost function calculated for the rank (n-1) iterative procedure in the vicinity of the path selected in the rank (n-1) iterative procedure.

4. The apparatus (10) according to claim 2 or 3, wherein the sampling region of the rank n iterative procedure is determined in the vicinity of the path selected in the rank (n-1) iterative procedure, taking into account the variability of the cost function calculated for the rank (n-1) iterative procedure.

5. To sample the candidate entry points (22), the processor (11) is configured to determine a set of points on the patient's body skin (21) that are defined in the anatomical model by a spherical coordinate system centered on the target point, where each point is at a distance r from the target point. ｉ，ｊ And, two angles α ｉ and β ｊ Determined by, the angle α ｉ and β ｊ When the subscripts i and j correspond strictly to positive integers, [Math 1] and [Math 2] It is defined as follows: In the formula, R represents the minimum distance separating the two candidate entry points (22) on the patient's body skin (21). The apparatus (10) according to claim 1.

6. According to one validity criterion, for a given candidate route, The appropriateness of the length of the candidate pathways relating to the medical devices assumed for the aforementioned procedure, The ability to configure a robotic arm on which a medical device is mounted so that the medical device can follow the candidate path, The presence of an object attached to the patient that obstructs the candidate pathway, The intersection of at least one important anatomical structure within the patient's body and the candidate pathway, The apparatus (10) according to claim 1, which makes it possible to verify at least one of the following.

7. The apparatus (10) according to claim 1, wherein multiple validity criteria are considered to eliminate candidate paths, and the various validity criteria are evaluated in an order determined for each validity criterion, taking into account the estimated computation time required to assess the validity criterion.

8. The aforementioned multiple characteristics of the candidate path are as follows: The minimum distance between the candidate pathway and the important anatomical structure within the patient's body, The length of the intersection between the candidate pathway and the bone to be treated, The minimum angle of incidence between the candidate path and the anatomical interface traversed by the candidate path, The minimum distance between the outer layer of the area to be treated and the outer layer of the excision area estimated for the candidate pathway, The angular difference between the candidate pathway and the principal axis of the area to be treated, The apparatus (10) according to claim 1, comprising at least one of the following.

9. The apparatus (10) according to claim 1, wherein multiple cost functions are calculated from the quantified characteristics, each cost function is calculated using different sets of weights assigned to various quantified characteristics, and the processor (11) is configured to obtain a user representation of the cost functions to be considered when displaying candidate paths.

10. The apparatus (10) according to claim 1, wherein the means for quickly visually confirming the value of the cost function for each displayed candidate route (31) includes a color code that associates different colors with different values ​​of the cost function.

11. The apparatus (10) according to claim 1, wherein the processor (11) is configured to display quantifications determined for the characteristics of the path selected by the user.

12. The apparatus (10) according to claim 1, wherein the processor (11) is configured to obtain a user evaluation of at least one displayed candidate path (31) (110), and to update the cost function (111) taking the evaluation into consideration.

13. The processor (11) In the anatomical model, after the procedure, the actual path taken by the medical instrument during the procedure is determined from the patient's medical image taken when the medical instrument is in a predetermined position (120). The value of the cost function for the actual route is determined (121), The difference between the cost function value of the candidate route selected for the above procedure and the cost function value of the actual route is calculated (122). The apparatus (10) according to claim 1, configured as follows.

14. The apparatus (10) according to claim 13, wherein the processor (11) is configured to compare the calculated difference with a predetermined threshold and to display (123) a display related to the value of the calculated difference.

15. The aforementioned processor, The user's evaluation of the actual route is obtained (124), Taking into consideration the calculated difference and the acquired evaluation, a statistical value representing the variability of the cost function is determined (125), Considering the statistical values ​​obtained in this way, candidate routes that are deemed equivalent are eliminated (126). The apparatus (10) according to claim 13, configured as follows.

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