Apparatus for assisting in planning minimally invasive surgery

Abstract

The invention relates to a data processing device for implementing a method for assisting in selecting at least one optimal trajectory of a minimally invasive medical procedure. Specifically, the device is configured to confirm (102), in a three-dimensional anatomical model, a target point to be reached in a region to be treated within a target anatomy of a patient and to perform at least one iteration, comprising the steps of:-determining (103) a sampling region and a sampling resolution of candidate entry points,-removing (104) candidate trajectories according to at least one predetermined validity criterion, and-determining (104) the target point to be reached in the region to be treated within the target anatomy of the patient. -for each remaining candidate trajectory, calculating (105) at least one cost function from the plurality of quantized features,-displaying (106) the candidate trajectory on the anatomical model using a means of quickly visualizing the cost function,-selecting (107) an optimal trajectory from among the displayed candidate trajectories.

Description

Technical Field

[0001] This patent application relates to the field of minimally invasive medical surgical planning and may involve surgeries assisted by medical robots. Specifically, a data processing device is provided for implementing a method for assisting in selecting an optimal path for a minimally invasive medical surgery in a patient's target anatomical structure. Background Art

[0002] To prepare for a minimally invasive medical surgery aimed at using a medical device to reach a target anatomical region within a patient's target anatomical structure, a doctor typically uses pre-operative or intra-operative medical images to plan the surgery. Specifically, a minimally invasive medical surgery may aim to perform a biopsy or ablation of a tumor in an organ or bone, to perform vertebroplasty or kyphoplasty, or even to stimulate a specific anatomical region. For example, the target anatomical structure can be the lung, kidney, liver, brain, vertebra, tibia, knee, etc. The medical device can be a needle, electrode, probe, etc.

[0003] For example, pre-operative or intra-operative medical images are obtained by computed tomography, magnetic resonance imaging, ultrasound, or positron emission tomography.

[0004] When planning the surgery, the doctor defines a target point in the treatment area of the target anatomical structure. The doctor also defines an entry point of the medical device on the patient's skin. These two points then define the path that the medical device must follow to perform the medical surgery.

[0005] Depending on the type of surgery, certain restrictions must be met. For example, it may be important that the medical device does not pass through high-risk anatomical structures (such as organs, bones, or blood vessels).

[0006] The path is usually defined empirically based on the doctor's knowledge and the information that can be gathered from the pre-operative images. However, this path is not necessarily the optimal one, i.e., the path that ensures effective treatment of the patient with minimal risk.

[0007] For a given surgery, there are theoretically an infinite number of possible paths. However, the doctor must select a path within a limited and sometimes very short time, especially when performing surgery planning just before the surgery.

[0008] In addition, this task must be completed based on the patient's volume image and is therefore in three dimensions, while the traditional method of presenting such images is to provide two-dimensional slices of the volume, which makes it more difficult for the doctor to find the optimal path. Moreover, when the surgery requires defining multiple paths (such as for inserting multiple needles), it even becomes more psychologically complex for the doctor to plan it.

[0009] Ultimately, the quality of path optimization highly depends on the doctor and the time that can be allocated to this task.

[0010] Methods for evaluating the paths proposed by a doctor already exist, for example based on the risk of using a medical device to cross a high-risk area. Such solutions do not enable the doctor to quickly find the optimal or near-optimal path for the medical procedure under discussion.

[0011] Methods for automatically suggesting entry points, target points, and / or paths to a doctor also exist. However, these solutions generally do not give the user the flexibility to view the optimality of a large number of paths and select a different path that may be sub-optimal but preferred for various reasons (which may vary from one patient to another and which do not necessarily need to be considered by the optimality criteria under discussion). Summary of the Invention

[0013] The aim of the solution provided by this patent application is to overcome all or part of the drawbacks of the prior art, in particular those mentioned above.

[0014] To this end, and according to a first aspect, there is provided a data processing device comprising a processor, a computer memory, and a display screen. The computer memory contains program code instructions which, when executed by the processor, configure the processor to implement a method for assisting in selecting at least one optimal path for a minimally invasive medical procedure within a patient's target anatomical structure. The processor is configured to:

[0015] - Obtain a three-dimensional anatomical model of the patient from pre-acquired medical images of the patient, the anatomical model containing a representation of the patient's body outer surface and the target anatomical structure;

[0016] - Identify at least one target point to be reached within the treatment area located within the patient's target anatomical structure in the anatomical model;

[0017] - Perform at least one iteration, wherein the processor is configured to:

[0018] o Determine a sampling area and a sampling resolution for sampling candidate entry points on the patient's body outer surface in the anatomical model, each candidate entry point belonging to the sampling area, the sampling resolution representing the minimum distance between two candidate entry points, and each pair formed by a target point and a candidate entry point forms a candidate path;

[0019] o Remove candidate paths according to at least one pre-determined validity criterion;

[0020] o For each remaining candidate path, quantify a plurality of pre-determined features one by one and calculate at least one cost function from the quantified features;

[0021] o On the display screen, superimposed on the anatomical model, at least some of the remaining candidate paths are displayed, and for each displayed candidate path, there is a means to quickly view the cost function value;

[0022] o Obtain the user's selection of the path to the at least one target point, which path is considered the optimal path among the displayed candidate paths.

[0023] These provisions enable a set of optimal or near-optimal paths for minimally invasive surgery to be suggested to the doctor based on the patient's three-dimensional anatomical model. The suggested paths take into account various types of constraints (such as practical constraints, performance-related constraints, and / or safety constraints). The doctor can select a path that may be suboptimal in terms of predefined criteria but more preferable in terms of constraints not covered by that criterion. The invention can also quickly and optimally explore all solutions to determine candidate paths. It enables the user to quickly view various candidate paths and their optimality criteria. The proposed solution is flexible because it can adapt to the specific requirements of the surgery. In addition, the doctor can also update the optimality criteria (cost function) based on user feedback (evaluation of the selected surgical path).

[0024] In a specific embodiment, the device may further comprise one or more of the following features, implemented individually or in any technically possible combination.

[0025] In a specific embodiment, the processor is configured to perform at least two iterations. For the nth iteration, where n is an integer greater than one:

[0026] - The sampling region for the nth iteration is defined as smaller than the sampling region defined for the (n - 1)th iteration and includes the candidate entry points corresponding to the path selected in the (n - 1)th iteration; and

[0027] - The sampling resolution for the nth iteration is defined as finer than the sampling resolution defined for the (n - 1)th iteration.

[0028] In a specific embodiment, the sampling resolution for the nth iteration is defined based on the variability of the cost function calculated in the (n - 1)th iteration near the path selected in the (n - 1)th iteration.

[0029] In a specific embodiment, the sampling region for the nth iteration is defined based on the variability of the cost function calculated in the (n - 1)th iteration near the path selected in the (n - 1)th iteration.

[0030] In a specific embodiment, to sample the candidate entry points, the processor is configured to determine, in the anatomical model and on the outer surface of the patient's body, 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 i,j and two angles α i and β j such that the angles α i and β j are defined such that the indices i and j correspond to strictly positive integers:

[0031] [Equation.1]

[0032]

[0033] and

[0034] [Equation.2]

[0035]

[0036] where R represents the minimum distance between two candidate entry points on the outer surface of the patient's body.

[0037] In a specific embodiment, a validity criterion allows for verifying at least one of the following for a given candidate path:

[0038] - The validity of the length of the candidate path for the medical device planned for the surgery,

[0039] - The ability to configure the robotic arm with the medical device so that the medical device will be able to follow the candidate path,

[0040] - The presence of an object equipped on the patient obstructs the candidate path,

[0041] - The candidate path intersects at least one key anatomical structure in the patient's body.

[0042] In a specific embodiment, multiple validity criteria are considered to remove candidate paths, and different validity criteria are evaluated in the order defined for each validity criterion according to the estimated computation time required to evaluate each validity criterion.

[0043] In a specific embodiment, multiple features of the candidate path include at least one of the following features:

[0044] - The minimum distance between the candidate path and a key anatomical structure in the patient's body,

[0045] - The length of the intersection part between the candidate path and the target anatomical structure,

[0046] - The minimum incident angle between the candidate path and the anatomical interface traversed by the candidate path,

[0047] - An estimated value representing the stability of the medical device under its own weight, the medical device being placed along the candidate path to the target point,

[0048] - The minimum distance between the outer surface of the treatment area to be treated and the outer surface of the ablation area estimated for the candidate path,

[0049] - The angular difference between the candidate path and the main axis of the treatment area to be treated.

[0050] In a specific embodiment, a plurality of cost functions are calculated based on the quantified features, each cost function being calculated using a different set of weights assigned to different quantified features, and the processor is configured to obtain an indication of the cost function to be considered by the user when displaying the candidate path.

[0051] In a specific embodiment, the means for quickly viewing the cost function values of each displayed candidate path includes color coding that associates different colors with different cost function values.

[0052] In a specific embodiment, the processor is configured to display the quantification determined by the user for the features of the selected path.

[0053] In a specific embodiment, the processor is configured to, for at least one of the displayed candidate paths, obtain the user's evaluation of the candidate path and update the cost function based on the evaluation.

[0054] In a specific embodiment, the processor is configured to:

[0055] - After the surgery, in the anatomical model, determine the actual path followed by the medical device during the surgery based on the medical images of the patient obtained when the medical device was in place;

[0056] - Determine the cost function value of the actual path;

[0057] - Calculate the difference between the cost function value of the candidate path selected for the surgery and the cost function value of the actual path.

[0058] In a specific embodiment, the processor is configured to compare the calculated difference with a pre-determined threshold and display an indication related to the calculated difference value.

[0059] In a specific embodiment, the processor is configured to:

[0060] - Obtain the user's evaluation of the actual path,

[0061] - Determine a statistical value representative of the variability of the cost function based on the calculated differences and the obtained evaluations.

[0062] - Remove the candidate paths considered equivalent based on the statistical value thus obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The present invention will be more readily understood by reading the following description, which is only a completely non - restrictive example and refers to Figures 1-7 , which shows:

[0064] Figure 1 A schematic diagram of a data processing device according to the present invention.

[0065] Figure 2 A schematic diagram of the main steps for assisting in selecting the optimal path for a medical operation.

[0066] Figure 3 A schematic diagram of the definition of the orbital angle and the head - tail angle to determine the sampling area.

[0067] Figure 4 A schematic diagram of a set of candidate entry points on the outer surface of a patient's body in an anatomical model.

[0068] Figure 5 A schematic diagram showing a set of candidate paths for a medical operation, the optimality criterion of which is shown by a scale, here shown as a gray - level display.

[0069] Figure 6 A schematic diagram of an additional step considering the user's evaluation of the candidate paths.

[0070] Figure 7 A schematic diagram of an additional step considering the difference between the selected path and the actual path followed by a medical device.

[0071] Figure 8 A schematic diagram of a bone to be treated and two candidate paths to reach the target point within the bone.

[0072] In these figures, the same reference numerals from one figure to another denote the same or similar elements. For the sake of clarity, unless otherwise stated, the elements shown are not necessarily to the same scale.

[0073] DETAILED DESCRIPTION OF AT LEAST ONE EMBODIMENT OF THE INVENTION

[0074] Figure 1 Shows a data processing device 10 according to the present invention. The data processing device 10 includes at least one processor 11, at least one computer memory 12, and at least one display screen 13.

[0075] ​​​​​​​​Computer memory 12 contains program code instructions which, when executed by processor 11, configure processor 11 to implement a method for assisting in selecting at least one optimal path that a medical device must follow during a minimally invasive medical procedure within a patient's target anatomical structure.

[0076] Specifically, the minimally invasive medical procedure can be aimed at performing a tumor biopsy or ablation in an organ or bone to treat a bone disease, performing vertebroplasty or kyphoplasty, or even stimulating a specific anatomical region. The target anatomical structure can correspond to an organ or a bone, such as the lung, kidney, liver, brain, vertebra, tibia, femur, hip bone, knee, pelvic bone, pelvis, etc. The medical device can be a needle, electrode, probe, drill, trocar, screw, etc.

[0077] Figure 2 The main steps of a method 100 for assisting in selecting an optimal path for a minimally invasive medical procedure implemented by a processing device 10 are shown.

[0078] As Figure 2 shown, the method 100 includes a step 101 of obtaining a three-dimensional anatomical model of the patient. Specifically, the anatomical model contains a representation of the patient's body outer surface (or a part of the body outer surface) and the target anatomical structure.

[0079] The anatomical model is obtained from pre-acquired medical images of the patient. The medical images used for generating the anatomical model can be obtained, for example, by computed tomography, magnetic resonance imaging, ultrasound, or positron emission tomography (any medical imaging technique that allows for the reconstruction of a volume in three dimensions can be used). Typically, multiple images are required to generate a three-dimensional anatomical model. Alternatively, a three-dimensional anatomical model can also be generated from a single image and a statistical model of the patient.

[0080] The anatomical model can be directly generated by the processing device 10, but optionally it can also be generated by another separate device and transmitted to the processing device 10 by means of communication.

[0081] Traditionally, three-dimensional modeling is performed, for example, using methods for segmenting anatomical structures considered relevant to clinical practice in medical images. The result of the segmentation can take the form of a binary image or a mask. For example, in the context of minimally invasive ablation of liver tumors, it may be relevant to segment the liver, the tumor, the lungs, the gallbladder, the bile ducts, the digestive organs, the bones, certain blood vessels (those with a significant diameter), and the outer surface of the patient's body. According to another example, in the context of performing minimally invasive surgery on bones, it may also be relevant to segment the outer surface of the bone to be treated, its cortical part (cortical bone corresponds to the particularly hard peripheral part of the bone; it is the "outer shell" of the bone, i.e., the relatively thick wall of the bone) and / or its cancellous part (cancellous bone corresponds to the bone tissue that forms the porous part of the bone, which is located below the cortical bone). This three-dimensional anatomical modeling can be represented mathematically as follows:

[0082] [Mathematical formula.3]

[0083]

[0084] where M is the three-dimensional anatomical model, and S i is the segmentation representation considering when generating the model, and all segmentations are represented in the same anatomical framework.

[0085] Segmentation methods are typically automatic artificial intelligence methods (requiring no user input), semi-automatic artificial intelligence methods (then requiring a single user input, such as a point or a segmentation), or interactive artificial intelligence methods (by iteratively correcting the segmentation result by the user). Manual segmentation can also be considered, but the time required to perform such segmentation means that this alternative is less relevant to the clinical environment.

[0086] In artificial intelligence methods, convolutional neural networks are currently the most effective. However, other methods may also be advantageous alternatives, such as methods based on, for example, random forests, k-means clustering, watershed segmentation, active contour models, level set methods, etc.

[0087] As Figure 2 shown, the method 100 includes step 102: identifying a target point to be reached in a treatment area (e.g., a tumor) within a patient's target anatomical structure (e.g., the liver) in the anatomical model. It should be noted that the case where multiple target points must be considered can also be considered (e.g., when multiple paths must be selected for the insertion of multiple needles). In this case, the set of target points can be determined from a "virtual" target point and the geometric arrangement that the target points must have relative to each other and relative to the virtual target point (e.g., the target points must correspond to the vertices of a specific geometric shape centered on the virtual target point, or actually have a specific spatial distribution around the virtual target point). The virtual target point and the geometric arrangement that the target points must have are input data provided by the user.

[0088] Specifically, the ablation target area can be segmented automatically or semi-automatically. Manual segmentation or correction tools can be used according to the required precision. Once a satisfactory segmentation of the treatment target area is obtained, the positions of the target points to be reached can be estimated. It should be noted that the treatment target area can be segmented by the processing device 10, or it can also be pre-segmented by another separate device in the anatomical model and then transmitted to the processing device 10 through communication means.

[0089] For example, the segmentation of the treatment target area can be used to automatically determine the target points (such as the center of the circumscribed sphere or ellipsoid determined by calculating the centroid or known parameters). Or, when the surgery involves inserting multiple medical devices, it can be envisaged to use the segmentation to determine a set of multiple target points, such as according to virtual target points (such as corresponding to the center of the treatment target area), the geometric arrangement that the target points must have, and / or the desired ablation coverage (specifically, for a given medical device, the ablation area that may be obtained around the target point can be predicted to ensure that it covers the area to be ablated).

[0090] According to the envisaged type of treatment (radiofrequency ablation, microwave ablation, cryotherapy or electroporation, biopsy, vertebroplasty, etc.), the target points can be located inside or on the periphery of the treatment target area. When the medical device is located at the target point, optionally the position of the target point can be estimated in this way according to a model of the ablation area that is easily obtained.

[0091] The candidate paths of the surgery will be determined based on one or more target points defined in this way and the set of candidate entry points on the patient's skin. Therefore, the set of entry points on the skin forms an initial domain, and the optimal path must be confirmed based on this initial domain. Specifically, each pair consisting of a target point and a candidate entry point forms a candidate path. Therefore, it is necessary to confirm the candidate entry points on the patient's skin. This can be achieved by a method such as the Otsu method, which is very suitable for voxel classification in the case of a bimodal brightness distribution. However, there are infinitely many candidate entry points in the continuous space. Even in the discrete domain, for the image resolution used in clinical practice, there are a large number of candidate entry points. Therefore, sampling is preferably performed to reduce the number of candidate entry points.

[0092] For this purpose, and as Figure 2 shown, the method 100 includes step 103: in the anatomical model, on the outer surface of the patient's body, determine the sampling area and sampling resolution for sampling candidate entry points.

[0093] The sampling resolution represents the minimum distance between two candidate entry points. A compromise must be found between the fineness of the sampling and the computation time required to evaluate the various candidate paths associated with each candidate entry point. A very fine "brute force" sampling method for candidate entry points would require very fast computation of path evaluations, which would lead to strict limitations (limitations of the optimization criteria and / or use of expensive computation means). In addition, the user would need to view a large amount of information.

[0094] Accordingly, a sampling resolution can be advantageously considered such that two consecutive paths are sufficiently different from each other. The sampling resolution can be determined by default (for example, it may be required that the minimum distance between two entry points on the body surface of the patient be at least five millimeters), but can also be adjusted by the user as needed (compromise between computation time and required precision).

[0095] To sample candidate entry points, specifically, in the anatomical model, on the outer surface of the patient's body, a set of points defined by a spherical coordinate system centered on the target point can be determined (when considering multiple target points, it can be envisaged to process each target point successively, with the aim of determining an optimal path for each target point; alternatively, the set of target points can also be processed together, and in this case, candidate entry points can be sampled relative to a "virtual" target point, which, for example, corresponds to the equidistant point of each target point). Then each point can be defined by the distance r i,j from the target point i and two angles α j and β. max Then the sampling area can correspond to the intersection of the outer surface of the body with a conical angular aperture centered on the target point (described by α max and β). 0,0 For example, the calculation can be initialized from the first point of the path on the skin corresponding to α0 = β0 = 0; the distance between this point and the origin of the coordinate system (target point) is denoted as r i . Sampling can be obtained by calculating the angles α i and β (in radians) such that the increment between each angle corresponds to the arc length on the outer surface of the patient's body, which corresponds to a predefined resolution parameter. Specifically, for subscripts i and j corresponding to strictly positive integers, the angles α i and β j can be defined as:

[0096] [Mathematical formula.1]

[0097]

[0098] and

[0099] [Mathematical formula.2]

[0100]

[0101] Where R is a predefined resolution parameter representing the minimum distance between two candidate entry points on the outer surface of the patient's body. The angles α and β are incremented until they cover the desired angular aperture cone.

[0102] As Figure 3 shown, the angle α can correspond to an orbital angle measured with respect to the anteroposterior axis 53 in the transverse plane 51 of the patient 50. The transverse plane 51 is defined by the target point, the anteroposterior axis 53, and the transverse axis 54 of the patient. The angle β can correspond to the cranio-caudal angle formed by the transverse plane 51 and the oblique plane 52. The oblique plane 52 is defined by the target point, the transverse axis 54, and the candidate path 55 formed by the target point and the associated entry point.

[0103] Figure 4 Schematically shows a set of candidate entry points 22 on the outer surface 21 of the patient's body in the anatomical model (the candidate entry points 22 form a "grid" on the outer surface 21 of the patient's body).

[0104] As described above, reducing the number of candidate paths is beneficial for limiting the total computation time of the method for selecting the optimal path. To this end, and as Figure 2 shown, the method 100 includes step 104: removing certain candidate paths according to one or more predetermined validity criteria.

[0105] It is beneficial to consider multiple 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.

[0106] The problem lies in applying successive simple filters to reduce the number of candidate paths. These filters are computationally simple and can quickly rule out certain impossible paths. These filters are applied in order from the simplest to the most complex in terms of computation time, so that the last filter is applied to as few candidate paths as possible.

[0107] As a non-limiting example, the following are validity criteria that can be considered in sequence.

[0108] In the initial stage, if a medical operation is assisted by a robot comprising a robotic arm equipped with a medical device, candidate paths that cannot be configured to move the medical device along the path under discussion can be removed. Once sampling has been carried out, the candidate paths are filtered based on prior knowledge of the robot's ability to reach the orbital and cephalocaudal angles and the target point under discussion. This stage depends on the robot model used, the geometry of the tool used to guide the medical device, and the position of the target point. These parameters are known when determining the candidate paths and can thus significantly reduce the number of candidate paths.

[0109] In the second stage, candidate paths whose path length is greater than the length of the medical device envisaged for the operation can be removed.

[0110] In the third stage, candidate paths blocked by objects worn by the patient can be removed. As Figure 4 shown, for example, such an object may be a patient reference 23 used by an optical navigation system to guide the robotic arm. According to another example, it may also be a catheter. By segmenting the patient and the objects equipped for the operation, and based on prior knowledge of the three-dimensional geometry of the robotic arm, paths that would result in a collision between the robotic arm and the objects equipped by the patient for the operation can be determined. Such paths can be removed.

[0111] In the fourth stage, candidate paths that intersect at least one critical anatomical structure within the patient can be removed. The "critical anatomical structure" refers, for example, to an organ different from the target anatomical structure (e.g., the lung, spleen, gallbladder, or kidney if the target anatomical structure is the liver) or a high-risk vascular structure (artery, vein, bile duct, digestive tract). Generally, segmentation is not parametrically represented. Therefore, in order to detect collisions between high-risk anatomical structures and paths, the latter can be finely sampled, and the segmentation value at each sampling point can be determined. A distance between points on the path equal to half of the image resolution in the acquisition direction (usually on the order of one millimeter) is sufficient to achieve this purpose.

[0112] The validity criteria listed above by way of example may apply to both minimally invasive surgery on soft organs and minimally invasive surgery on bones.

[0113] As Figure 2 shown, method 100 includes step 105: calculating at least one cost function for each remaining candidate path (i.e., each candidate path not removed in removal step 104) based on a plurality of features related to, for example, the safety or performance of the operation. These features are quantifiable, that is, a measurement and normalization value is assigned to each feature under discussion (in other words, each quantified feature corresponds to an index representing a safety or performance criterion).

[0114] As a non-limiting example, all or some of the following features can be considered when calculating the cost function. Unless otherwise stated, each of these features can be considered for minimally invasive surgery of soft organs or minimally invasive surgery of bones.

[0115] According to the first example, the minimum distance between the candidate path and the critical anatomical structures in the patient's body can be considered. To this end, the anatomical model can include the segmentation of the internal critical anatomical structures. Then, the distance transform (also known as the distance map) of the relevant segmentation can be used to calculate the distance between each point of the path and the given anatomical structure. When this transform is applied to a binary image, the result is a new image where each voxel value corresponds to the shortest distance between the voxel and the image edge. The minimum distance between the candidate path and the internal critical anatomical structures corresponds to the safety margin, which must take into account factors affecting the imprecision of the medical robot and / or biomechanical variables that cannot be controlled during the insertion of the medical device.

[0116] According to another example, it can be envisaged to consider the length of the intersection part of the candidate path and the target anatomical structure. This feature is related to the safety standards of medical surgery. Specifically, it may require the minimum distance for the medical device to pass through the target anatomical structure to prevent the spread of tumor cells outside the target anatomical structure when it is removed. This feature is also related to the performance standard, because the longer the intersection part between the candidate path and the target anatomical structure, the better the stability of the medical device in the target anatomical structure during the surgery. Therefore, a compromise needs to be found according to the targeted clinical goal; this compromise may vary for each surgery. In the case where the goal of minimally invasive surgery is to insert a screw into the bone, the minimum depth of the screw inserted into the bone is also related to the stability of the screw in the bone.

[0117] According to another example, the minimum incident angle between the candidate path and the anatomical interface through which the candidate path passes can be considered. When inserting a medical device (such as a needle) in minimally invasive surgery, the device may pass through certain interfaces, such as the skin or the wall of certain organs (such as the liver capsule). The incident angle of the device with these interfaces may affect the effect of the device placement (potential device deflection or organ movement). This parameter also has an impact on safety, because an incident angle that is too tangential to the anatomical interface may promote the formation of a hematoma. Calculating the incident angle requires knowing the surface normal at the path entry point. Methods based on surface meshing exist, but they may be expensive, which limits their use in the clinical environment. To reduce the calculation time, the normal can be calculated as the gradient of the distance change of the segmentation evaluated at the entry point. It is recommended to calculate the gradient by convolution with the derivative of the Gaussian function in order to obtain a more robust normal estimate against noise (sensitivity to noise is a drawback of the finite difference method). In bone surgery, too small an incident angle may increase the risk of the medical device sliding on the bone.

[0118] According to another example, an estimated value representing the stability of a medical device positioned along a candidate path to a target point under the influence of its own weight can be considered. Specifically, this value can be calculated based on the insertion length of the path under discussion: the shorter the insertion length, the easier it will be for the medical device to move under the action of its own weight or external forces. This value can also be calculated using a biomechanical model that provides, for example, information on the viscoelasticity of the organ being traversed. The path that preferably results in the lowest risk of the medical device moving under its own weight is selected. This feature is more relevant to soft organs compared to bones.

[0119] According to another example, when the minimally invasive procedure corresponds to ablation, the minimum distance between the outer surface of the treatment area to be treated and the outer surface of the ablation area estimated for the candidate path can be considered. This minimum distance corresponds to the estimated ablation boundary. For a given medical device, models of the ablation area are available. These models allow the estimation of the theoretical coverage area obtained for each path. For example, it is advantageous to optimize the coverage of the cancer area by ablation (maximizing the boundary between the ablation area and the cancer area) while minimizing damage to healthy parenchymal tissue and nearby organs. Generally, the model of the ablation area is elliptical and eccentric with respect to the distal end of the device. The shape and orientation of the tumor are important aspects to consider (the tumor is not necessarily spherical). Therefore, in order to obtain better coverage of the tumor, certain paths may be preferred depending on their orientation and the position of the distal end of the device at the end of the path.

[0120] According to another example, the angular difference between the candidate path and the major axis of the treatment area to be treated can also be considered. Specifically, in practice, for tumor ablation, it is recommended to attack the tumor along the major axis of the tumor because treatment is more effective along the axis of the medical device.

[0121] According to another example, when the minimally invasive procedure corresponds to ablation, the coverage value of the treatment area to be treated can also be considered. This coverage value can be estimated based on the segmentation of the treatment area to be treated and the estimation of the ablation area obtainable using the candidate path under discussion. Specifically, for example, in the case of tumor ablation, a minimum ablation boundary may be required to minimize the recurrence rate. In the case of bone tumors, another important factor is that the ablation area remains within the bone (therefore, the limitation of the coverage range is determined by the anatomy and position of the lesion).

[0122] According to another example, when the target anatomical structure is bone, for a partial path within the bone, the minimum distance between the partial candidate path and the cortical layer of the bone can also be considered. Specifically, it is advantageous to maximize the minimum distance between the partial candidate path within the bone and the cortical layer of the bone to minimize the risk of re - contacting the cortical layer of the bone after the medical device is inserted into the bone. Specifically, due to the imprecision of the placement of the medical device or biomechanical effects (such as deflection of the medical device, movement of the target anatomical structure due to the patient's breathing or due to effects associated with the insertion of the device, etc.), the actual path followed by the medical device may be different from the selected theoretical path. Maximizing the minimum distance between the partial candidate path within the bone and the cortical layer of the bone is equivalent to maximizing the safety margin of the candidate path.

[0123] Figure 8 Schematically shows a bone 80 to be treated having a cortical layer 81 and a cancellous bone portion 82. Reference 83 represents the target point that the medical device must reach to treat the bone 80. Figure 8 Also shown are two candidate paths 31 - 1 and 31 - 2 reaching the target point 83 from two candidate entry points 22 - 1 and 22 - 2 on the outer surface 21 of the patient's body. Figure 8 In the example shown, candidate path 31 - 1 may be more preferred than candidate path 31 - 2 because the minimum distance d1 between the partial candidate path 31 - 1 within the bone 80 and the cortical layer 81 is greater than the minimum distance d2 between the partial candidate path 31 - 2 within the bone 80 and the cortical layer 81.

[0124] According to another example, when the target anatomical structure is bone, the angle of incidence between the candidate path and the bone wall can also be considered. Specifically, it is advantageous for the candidate path to have an angle of incidence as close as possible to a right angle (90° angle) to prevent the medical device from sliding on the bone wall during its insertion (which may damage the bone and / or cause the medical device to deviate from the selected path during the insertion of the medical device). The "angle of incidence" refers to the angle formed between the candidate path and the bone at the interface where the path penetrates the interior of the bone.

[0125] In Figure 8 the example shown, candidate path 31 - 1 may be more preferred than candidate path 31 - 2 because the angle of incidence i1 formed between candidate path 31 - 1 and the wall of the bone 80 is closer to a right angle than the angle of incidence i2 formed between candidate path 31 - 2 and the wall of the bone 80.

[0126] According to another example, when the target anatomical structure is bone, it may also be considered to estimate the risk of fracture caused by the surgery of the candidate path under discussion. For example, when the surgery aims to insert screws into the bone to reinforce it, a biomechanical model that can model the bone structure (with solid areas and more fragile areas of the bone) and the mechanical stress borne by the bone (e.g., depending on the patient's measurements) can be used to estimate the risk of fracture due to the screws inserted along the candidate path under discussion. Optionally, this risk can be compared with the risk of fracture without surgery (i.e., without the reinforcing screws) to estimate to what extent the surgery reduces the risk of fracture. The risk of fracture can also be determined from a database of past surgeries that includes paths that prevented fractures and paths that failed to prevent fractures.

[0127] The above-described path features can be quantified and used to construct a cost function, thereby allowing the paths to be classified according to objective criteria. Multiple mathematical functions can be considered to combine these features in order to effectively represent the clinical objectives. The simplest function is a linear combination, in which case the cost function C(T) for path T takes the form of a weighted sum of the above features:

[0128] [Mathematical formula.4]

[0129]

[0130] where the weights w i enable the "balance" between the features y i to be controlled.

[0131] Various sets of weights w with predefined values i can form various cost functions, which can be used as they are if they are proven to match well with the doctor's clinical practice. The doctor is also able to create his or her own cost function by modifying the values of the weights w i according to the objectives of the surgery under discussion, in order to assign more or less importance to the features y i . This can be done manually, for example through a user interface, or even automatically based on previous surgeries. In fact, for each previous surgery, an evaluation (score) can be assigned to the paths used and the features calculated. Based on this information, a system of equations (with a number of equations N significantly higher than the number of features K under discussion) can be created and solved using regression methods. Neural networks can also be used to generate the cost function.

[0132] It may be advantageous to use various cost functions to represent various optimization categories (e.g., "performance" or "safety"). This enables predefined criteria or user-customized criteria to be considered. Then, the user can provide an indication of the cost function to be considered when displaying the candidate paths on the display screen 13 through the user interface.

[0133] As Figure 2 shown, method 100 includes step 106: on display screen 13, superimposed on the anatomical model, display at least some of the remaining candidate paths, and for each candidate path displayed, have a means of quickly viewing the cost function value.

[0134] It should be noted that display screen 13 can take various forms: for example, it can be a flat LCD computer screen (LCD monitor), but it can also be an augmented reality head-mounted screen, in which case the information displayed can be directly projected onto the patient.

[0135] It is conceivable not to display candidate paths whose cost function is less than a threshold.

[0136] In a particular embodiment, the means for quickly viewing the cost function value of the candidate paths displayed corresponds to color coding: various colors are associated with various values of the cost function. This display enables quick localization of the optimal path according to established clinical criteria and selection using the user interface. When the user selects a path, the quantification determined for the characteristics of that path can be displayed, with the aim of providing the user with a detailed view of each characteristic used to calculate the path cost function. Of course, the system provides the ability to select any candidate path, which allows management of optimality criteria not captured by the cost function.

[0137] Figure 5 Schematically shows the display of a set of candidate paths 31 on display screen 13. Each candidate path 31 is represented by a color associated with the cost function value calculated for the candidate path 31 under discussion. Also shown is the color coding 30 used. In Figure 5 the example shown, candidate paths 31 have been shown on the outer surface 21 of the patient's body. When the user selects one of these candidate paths 31, various views of the selected path can be displayed in the anatomical model (e.g., in different cross-sections containing the selected path).

[0138] As Figure 2 shown, method 100 includes step 107: the user selects the path considered to be optimal from the paths displayed for the target point under discussion.

[0139] For example, the selected optimal path can be used to configure the movement of a robotic arm suitable for holding or guiding a medical device intended for performing a medical procedure.

[0140] All of the following steps: determining 103 regions and sampling resolution, removing 104 certain candidate paths, calculating 105 at least one cost function, displaying 106 candidate paths, and selecting 107 a path, can be iterated multiple times to optimize the selection of candidate paths to be used for performing a medical procedure. To this end, as Figure 2As shown in step 108, for example, the user may be asked to indicate via a user interface whether they wish to optimize the selected candidate path.

[0141] Thus, in some embodiments, at least two iterations are performed. Advantageously, for a given iteration (different from the first iteration), the new sampling region contains an entry point corresponding to the path selected in the previous iteration, the new sampling region is defined as smaller than the sampling region defined for the previous iteration, and the sampling resolution is defined as finer than the sampling resolution defined for the iteration (which means that the minimum distance between two candidate entry points is smaller).

[0142] In some embodiments, for a given iteration (different from the first iteration), the new sampling resolution and / or the new sampling region are defined based on the variability of the cost function computed for the previous iteration in the vicinity of the path selected in the previous iteration. For example, the variability of the cost function can be measured as a function of angles α and β. The higher the variability in the previous iteration, the more advisable it is to select a fine resolution for the new iteration. The lower the variability, the more advisable it is to select a broad sampling region for the new iteration.

[0143] As Figure 6 shown, method 100 may further include step 110: the user evaluates at least one displayed candidate path, and step 111: updating the cost function based on the obtained evaluation.

[0144] For each displayed path, the user can evaluate the path by assigning it a score. This score can be used to trigger an optional update of the cost function parameters (e.g., if the cost function is a linear combination of multiple quantifiable features, the weights of the linear combination can be modified to accommodate the score provided by the user, e.g., via a regression algorithm or a neural network).

[0145] The path actually followed by the medical device during a medical procedure may deviate from the selected candidate path, particularly due to imprecise needle placement or biomechanical effects (needle deflection, movement of the target anatomy due to breathing or due to effects related to the insertion). Thus, it is related to the retrospective cost function that determines the actual path followed by the medical device, and 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.

[0146] Thus, and as Figure 7 shown, after the medical procedure, method 100 may further include the following steps:

[0147] - Determine the actual path 120 actually followed by the medical device during the surgery in the anatomical model (specifically, this actual path can be determined based on the medical images of the patient acquired when the medical device is in place, i.e., when it reaches the target point and is in the correct position for performing the treatment);

[0148] - Determine the cost function value 121 of the actual path;

[0149] - Calculate the difference 122 between the cost function value of the candidate path selected for the surgery and the cost function value of the actual path.

[0150] The calculation of the cost function value C'(T) of the actual path requires estimating the position of the medical device after insertion from the control image. This can be done manually (by clicking on the entry point and the end of the device), or by automatically segmenting the device using image processing methods and then extracting the required information.

[0151] Specifically, the retrospective determination of the cost function of the actual path can be used as a means to alert the user of an incorrect execution of the surgery (the placement quality of the device is lower than planned). This alert may be triggered when the change ΔC(T) in the cost function is significant. The significance threshold can be determined by retrospective analysis of past paths assigned an evaluation criterion (score).

[0152] Therefore, and as Figure 7 shown, method 100 may include step 123: displaying an indication related to the difference calculated in step 122 on the display screen 13.

[0153] Retrospective analysis can be performed on the statistics of the change ΔC(T) in the cost function of the path followed as planned, aiming to determine the typical value of ΔC(T) to define path equivalence classes. Specifically, for the set of paths judged to be followed as planned, the statistics of ΔC(T) can be derived to determine what is a typical change in the cost function. Then, this value can be used in method 100 to select the optimal path to avoid suggesting paths that are too similar to the user (i.e., paths with not enough difference in their cost functions).

[0154] Therefore, and as Figure 7 shown, method 100 may include the following steps:

[0155] - Obtain 124 the user's evaluation of the actual path;

[0156] - Determine 125 a statistical value representing the variability of the cost function based on the calculated difference and the obtained evaluation;

[0157] - Remove 126 the candidate paths considered equivalent based on the statistical value thus obtained.

[0158] In method 100 for assisting in selecting an optimal path, reference Figure 2 and Figure 6 the steps 101 - 108, 110, and 111 described are all performed before a medical operation; referring to Figure 7 the steps 120 - 126 described are performed after a medical operation. Thus, these steps do not involve performing a surgical operation. Steps 108, 110, 111, and 120 - 126 are optional.

[0159] The above - mentioned data - processing device 10 enables a set of optimal or near - optimal minimally invasive surgical paths to be recommended to a doctor based on a three - dimensional anatomical model of a patient. The recommended paths take into account various types of constraints (e.g., practical constraints, performance - related constraints, and / or safety constraints). A doctor can select a path that may be sub - optimal in terms of a predefined criterion but is preferred in terms of constraints not covered by that criterion.

[0160] The proposed solution allows for a fast and optimized exploration of the solution set to determine candidate paths (a “multi - resolution” method of sampling candidate entry points). It allows the user to quickly view various candidate paths and their optimality criteria. The proposed solution is flexible as it can adapt to the specific requirements of the operation (the need to improve placement accuracy, the need to increase the safety margin due to potential weaknesses of the patient's condition, etc.). A doctor can also update the optimality criterion (cost function) based on user feedback (the evaluation of the path selected for the operation).

[0161] The above description mainly targets the case of a single target point (e.g., a “single - needle” operation), or actually the case of continuously considering multiple target points to separately determine the optimal path for each target point (e.g., a “multi - needle” operation). However, in the case where multiple medical devices must be inserted during an operation (e.g., a “multi - needle” operation), the set of target points (and thus the set of candidate paths associated with various target points) can also be considered simultaneously. Specifically, this can be done by taking into account characteristics that depend on the mutual relationships between candidate paths when calculating the cost function. For example, this can consider the maximum relative angle between two of the various paths (it may be beneficial to minimize this relative angle) when calculating the cost function. According to another example, this can consider the difference between the distance between two of the various paths and a target value (specifically, the target value can be derived from the recommendations of the manufacturer of the medical device used).

Claims

1. A data processing device (10) comprising a processor (11), a computer memory (12) and a display screen (13), The computer memory (12) comprises program code instructions which, when executed by the processor (11), configure the processor to implement a method (100) for assisting in selecting at least one optimal path for a minimally invasive medical procedure within a target anatomical structure of a patient, The processor (11) is configured to: - Obtain (101) a three-dimensional anatomical model of the patient from pre-acquired medical images of the patient, the anatomical model comprising an outer body surface (21) of the patient and a representation of the target anatomical structure, - Identify (102) at least one target point to be reached within a treatment area within the target anatomical structure of the patient in the anatomical model, - Perform at least one iteration, wherein the processor is configured to: o Determine (103) a sampling area and a sampling resolution for sampling candidate entry points (22) on the outer body surface (21) of the patient in the anatomical model, each candidate entry point (22) belonging to the sampling area, the sampling resolution representing the minimum distance between two candidate entry points (22), and each pair formed by the target point and a candidate entry point (22) forming a candidate path; o Remove (104) candidate paths according to at least one pre-determined validity criterion; o For each remaining candidate path, quantify a plurality of pre-determined features one by one and calculate (105) at least one cost function from the quantified features; o On the display screen (13), superimposed on the anatomical model, display (106) at least some of the remaining candidate paths, and for each displayed candidate path (31), there is a means for quickly viewing the cost function value; o Obtain (107) a user's selection of a path to the at least one target point, which path is considered to be the optimal path among the displayed candidate paths (31).

2. The device (10) according to claim 1, wherein the processor (11) is configured to perform at least two iterations; and wherein for the nth iteration, n being an integer strictly greater than one: - The sampling area of the nth iteration is defined as being smaller than the sampling area defined in the (n - 1)th iteration and includes the candidate entry points (22) corresponding to the path selected in the (n - 1)th iteration; and - The sampling resolution of the nth iteration is defined as being finer than the sampling resolution defined in the (n - 1)th iteration.

3. The device (10) according to claim 2, wherein the sampling resolution of the nth iteration is defined based on the variability of the cost function calculated in the (n - 1)th iteration in the vicinity of the path selected in the (n - 1)th iteration.

4. The device (10) according to any one of claims 2 - 3, wherein the sampling region of the nth iteration is defined based on the variability of the cost function calculated in the (n - 1)th iteration in the vicinity of the path selected in the (n - 1)th iteration.

5. The device (10) according to any one of claims 1 - 4, wherein in order to sample the candidate entry point (22), the processor (11) is configured to determine, in the anatomical model and on the outer surface (21) of the patient's body, 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 i,j and two angles α i and β j such that the angles α i and β j are defined such that the subscripts i and j correspond to strictly positive integers: and wherein R represents the minimum distance between two candidate entry points (22) on the outer surface (21) of the patient's body.

6. The device (10) according to any one of claims 1 - 5, wherein a validity criterion allows, for a given candidate path, verification of at least one of the following: - The validity of the length of the candidate path for the medical device planned for the surgery, - The ability to configure a robotic arm with a medical device such that the medical device will be able to follow the candidate path, - The presence of an object equipped on the patient obstructs the candidate path, - The candidate path intersects at least one key anatomical structure within the patient.

7. The device (10) according to any one of claims 1 - 6, wherein multiple validity criteria are considered to remove candidate paths, and different validity criteria are evaluated in the order defined for each validity criterion according to the estimated calculation time required to evaluate each said validity criterion.

8. The device (10) according to any one of claims 1 - 7, wherein multiple features of the candidate path include at least one of the following features: - The minimum distance between the candidate path and a key anatomical structure within the patient, - The length of the intersection portion between the candidate path and the target anatomical structure, - The minimum incident angle between the candidate path and the anatomical interface penetrated by the candidate path, - An estimated value representing the stability of the medical device under its own weight, where the medical device is placed along the candidate path to the target point, - The minimum distance between the outer surface of the treatment area to be treated and the outer surface of the ablation area estimated for the candidate path, - The angular difference between the candidate path and the main axis of the treatment area to be treated.

9. The device (10) according to any one of claims 1 - 8, wherein a plurality of cost functions are calculated based on the quantified features, each cost function being calculated using a different set of weights assigned to different quantified features, and the processor (11) is configured to obtain an indication of the cost function to be considered by the user when displaying the candidate path.

10. The device (10) according to any one of claims 1 - 9, wherein the means for quickly viewing the cost function values of each displayed candidate path (31) includes color coding associating different colors with different cost function values.

11. The device (10) according to any one of claims 1 - 10, wherein the processor (11) is configured to display the quantification determined by the user for the features of the selected path.

12. The device (10) according to any one of claims 1 - 11, wherein the processor (11) is configured to, for at least one of the displayed candidate paths (31), obtain (110) the user's evaluation of the candidate path and update (111) the cost function based on the evaluation.

13. The device (10) according to any one of claims 1 - 12, wherein the processor (11) is configured to: - After the surgery, in the anatomical model, determine (120) the actual path followed by the medical device during the surgery based on the medical images of the patient obtained when the medical device was in place; - Determine (121) the cost function value of the actual path; - Calculate (122) the difference between the cost function value of the candidate path selected for the surgery and the cost function value of the actual path.

14. The device (10) according to claim 13, wherein the processor (11) is configured to compare the calculated difference with a pre - determined threshold and display (123) an indication related to the calculated difference value.

15. The device (10) according to claim 13, wherein the processor (11) is configured to: - Obtain (124) the user's evaluation of the actual path, - Determine (125) a statistical value representative of the variability of the cost function based on the calculated differences and the obtained evaluation, - Remove (126) candidate paths considered equivalent based on the statistical value thus obtained.

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