PLANNING DEVICE FOR POSITIONING A TREATMENT APPLICATOR - Patent application

Abstract

The present invention relates to a planning device 2 for positioning a treatment applicator 8. The device 2 comprises a delivery unit 4 configured to provide a distance r to be considered between applicator entry points 10, and a processor 6. It has been found that when using treatment applicators, in particular ablative treatment applicators, placing two or more applicators close together increases the risk of ablation, damaging more tissue than necessary for a successful therapy. By configuring the processor 6 to disable all entry points within a distance r from a currently selected entry point, such that these entry points cannot be used in further optimization iterations, and by subsequently performing further optimization iterations, an optimal applicator configuration can be provided that is effective with respect to a successful therapy, but with a reduced risk of ablating more tissue than necessary.

Description

[Technical field]

[0001] The present invention relates to a planning device and system for positioning a treatment applicator, and to a planning method and a computer program for positioning a treatment applicator. [Background technology]

[0002] The use of therapeutic applicators utilizing a form of thermal ablation or radiation therapy has become increasingly popular in cancer treatment due to its applicability to unresectable tumors and rapid patient recovery.

[0003] For such treatments, optimal positioning of the applicator is essential and is often determined based on the location and size of the tumor, the device manufacturer's specifications, and the physician's experience. While ablation devices typically use manual positioning, image-guided systems are also known, for example, per US Patent Application Publication No. 2019 / 0008591(A1) and EP 3777748 A1.

[0004] However, it has been found that during such therapeutic efforts, physical interference between the applicators can occur, causing more tissue damage than is necessary for successful therapy. Summary of the Invention [Problem to be solved by the invention]

[0005] It is an object of the present invention to provide a planning device and system for positioning a treatment applicator, as well as a planning method and computer program for positioning a treatment applicator, such as an ablation treatment applicator, in order to improve applicator positioning. [Means for solving the problem]

[0006] In a first aspect, a planning device for positioning a treatment applicator is presented, said planning device comprising: a providing unit configured to provide distances to be considered between applicator entry points; and a processor configured to perform an optimization iterative step of determining an optimal applicator configuration comprising a first applicator entry point, to disable all entry points within said distance from the first applicator entry point such that all these entry points are unavailable for further optimization iterative steps, and to perform a further optimization iterative step of determining an optimal applicator configuration comprising further applicator entry points.

[0007] This approach has the advantage that the device's freedom to select entry points is simply gradually reduced with each iteration. Initially, all entry points are valid and can be selected by the algorithm described in more detail below. However, the more entry points are selected during the iterations, the more invalid the search and solution space becomes, becoming more constrained. The solution produced by such a technique guarantees that the distances defined by the user are respected.

[0008] It has been found that by utilizing the above device, treatments such as thermal ablation or brachytherapy can be improved. In particular, reduced physical interference between the applicators used is achieved. This reduces the overlap between the areas of ablation delivered, thus reducing the risk of ablating the same tissue multiple times. Furthermore, the number of ablation or brachytherapy applicators that need to be deployed is reduced compared to known treatment procedures.

[0009] In one embodiment, the planning device provides positioning assistance to the ablation therapy applicator, and the processor is further configured to receive three-dimensional medical image data depicting the object, and to receive a desired ablation volume registered to the three-dimensional medical image data. The term desired ablation volume is a label for a particular ablation volume. The desired ablation volume is registered to the three-dimensional medical image data. That is, the desired ablation volume indicates or identifies a region of the three-dimensional medical image data.

[0010] The processor is further configured to receive one or more protection volumes registered to the three-dimensional medical image data. The one or more protection volumes are, for example, volumes that may harm a subject if they are oversonicated or ablated. For example, the one or more protection volumes are used to protect an organ or a critical anatomical structure.

[0011] The processor is further configured to generate a discrete set of ablation applicator locations registered to the three-dimensional medical image data, where sites around the desired ablation volume are used to generate a pattern or set of discrete ablation applicator locations, which may be generated, for example, using a predetermined pattern or algorithm to generate the discrete set.

[0012] The processor is further configured to receive a discrete set of ablation patterns. The discrete set of ablation patterns includes a plurality of ablation patterns. The discrete set of ablation patterns may be ablation patterns for a single applicator, e.g., oriented in different directions and / or having different power delivery. In other examples, the discrete set of ablation patterns are from a plurality of applicators.

[0013] The processor is further configured to initialize a composite ablation binary mask registered to the three-dimensional medical image data. For example, initializing the composite ablation binary mask is creating an empty or unused composite ablation binary mask. The term composite ablation binary mask is used to identify a specific binary mask. The composite ablation binary mask in this case is an ablation binary mask that stores a composite or intersection of all ablation patterns used.

[0014] The processor is further configured to determine a non-ablation volume registered to the three-dimensional medical image data by comparing the composite ablation binary mask to the desired ablation volume. The composite ablation binary mask is used to identify regions that have already been ablated or are scheduled for ablation using configurations of the successive ablation applicator configuration list. The comparison between the desired ablation volume and the composite ablation binary mask may be used to find regions that still need to be ablated or that require the addition of additional applicators for ablation to occur.

[0015] The processor is further configured to determine an applicator configuration including at least one applicator entry point using an objective function selected depending on the one or more protection volumes, the non-ablative volumes, the discrete set of ablation patterns, the discrete set of ablation applicator positions, and the disabled applicator entry points.

[0016] The selected applicator configuration specifies one of a discrete set of applicator entry points and one of a discrete set of ablation patterns, and in this step the selected objective function may be used to evaluate various combinations of the discrete set of ablation patterns at each of the discrete set of applicator entry points.

[0017] The selected objective function is used to select the best option, which is used to objectively measure the extent of the non-ablative volume, as well as avoid one or more protection volumes.

[0018] The processor is further configured to update the composite ablation binary mask by calculating a union of the composite ablation binary mask and one of the discrete set of ablation patterns positioned at one of the discrete set of ablation applicator positions.

[0019] After the configuration of the applicator entry points is complete, this selection is used to update the composite ablation binary mask. The above-described embodiments apply to both multiple applicators inserted simultaneously into a subject, and to applicators inserted sequentially.

[0020] The selected objective functions include an ablation range based object quadratic function, a min / max ablation range function, and a uniform quadratic range function. The use of any of these functions is beneficial as they are effective in both selecting the ablation region and protecting the critical region as specified by one or more protection volumes.

[0021] In a preferred embodiment, the entry points are at least one of lattice holes in a lattice template, skin entry points. The lattice template may be one of a rigid lattice template, a flexible lattice template, in particular a transperineal lattice template, a breast template, a flexible lead template, a lattice attached to a gynecological (GYN) applicator. The template is used to segment regions of interest such as target lesions and organs at risk.

[0022] In one embodiment, no template is used. In this embodiment, the entry point is the skin entry point.

[0023] In one embodiment, the distance may be at least one of a minimum distance, in particular a minimum Euclidean distance, in the template or skin surface plane, a maximum distance, in particular a maximum Euclidean distance, in the template or skin surface plane. Said distance may further be a minimum and / or maximum distance between applicator trajectories. In general, the invalidation of all entry points is based on the first applicator entry point and said distance from the first applicator entry point.

[0024] According to one embodiment, further optimization iterations are performed taking into account disabled lattice holes or entry points until no further optimal applicator configuration can be determined.

[0025] In a preferred embodiment, the processor is further configured to perform a refinement iterative step in which applicator configurations are removed from the solution and replaced by other applicator configurations, and a further optimization iterative step is performed to determine an optimal applicator configuration.

[0026] With this help, the iterative optimization becomes less greedy: when an applicator configuration is removed, all nearby entry points or lattice holes that were previously disabled are enabled and become available again for solver selection.

[0027] In one embodiment, in each refinement iteration step, applicator configurations to be removed are selected in order, i.e., one by one, starting from the best applicator configuration. For example, applicator configurations to be removed may be selected based on a list of all applicators, and applicator configurations to be removed may be automatically selected successively, i.e., one after the other, from the list of applicators, e.g., until all applicator configurations in the list of applicators have been removed at least once or any other failure criterion is met.

[0028] In other embodiments, the refinement iterations are stopped when every applicator configuration has been removed once during the iteration, or when a removed applicator configuration is redetermined as a result of the iteration step.

[0029] In one embodiment, at each refinement iteration step, the applicator configuration to be removed is selected by the range-based function value or the derivative to select the smallest optimal applicator configuration. The range-based function value is a numerical value that quantifies the coverage that a set of applicator configurations provides to a region of interest, e.g., a tumor. Thus, an applicator configuration may be removed from the solution and a range-based function value may be determined for the remaining applicator configurations. This may be repeated and the remaining applicator configuration may be determined for which the range-based function value, and thus the coverage of the region of interest, is the least perturbed relative to the complete set of applicator configurations. The applicator configuration to be removed is then the least suitable applicator configuration and may be selected. Thus, the least suitable applicator configuration means the applicator configuration that least perturbs the range-based function value relative to the range-based function value of the complete set of applicator configurations.

[0030] The providing unit may include at least one of a graphical user interface, a configuration file, and a database. In general, the providing unit is configured to provide the processor with the distances considered between the applicator entry points. Furthermore, the providing unit may also be a receiving unit for receiving the distances, for example from a storage or a user input, and providing the distances to the processor for further processing.

[0031] The device may further comprise a graphical user interface configured to visualize at least one of an optimal set of applicator positioning configurations, disabled entry points, and distances considered between entry points.

[0032] The graphical user interface of the providing unit and the graphical user interface of said device may be the same graphical user interface or may be different from each other.

[0033] The graphical user interface comprises at least one of a text field, a continuous slider, a discrete slider, a radio button or pull down menu, scroll wheel interaction while visualizing the radius on top of the grid / patient anatomy.

[0034] The device is particularly adapted to be updated on demand after configuration changes have been implemented and / or in real time upon configuration changes.

[0035] In another aspect of the present invention an ablation system is presented, said system comprising an ablation therapy applicator and a planning device as defined in any one of claims 1 to 12.

[0036] The ablation therapy applicator utilizes at least one of the following ablation principles: focused ultrasound (FU), microwave (MW), radio frequency (RF), focal laser ablation (FLA), cryoablation, irreversible electroporation (IRE), or brachytherapy.

[0037] Applicable treatment organs may be the prostate, breast, kidney, liver, and cervix.

[0038] Applicable treatment workflows may be localized, quadrantal, unilateral prostate, full prostate, and partial breast treatment. The classification depends on the goal of the ablation procedure. Localized treatments only affect a small area within the prostate, quadrant treatments affect a quadrant of the prostate, unilateral prostate treatments typically affect half of the gland, and full prostate treatments affect the entire gland. Partial breast treatments are geared towards partial ablation of the breast.

[0039] In a further aspect of the present invention, a planning method for positioning a treatment applicator is presented, said planning method comprising the steps of providing distances to be considered between entry points and geometric information of a target to be treated, performing an optimization iteration step of determining an optimal applicator configuration comprising a first applicator entry point based on the geometric information of the target to be treated, disabling all applicator entry points within said distance from the first applicator entry point such that these entry points are not available for further optimization iteration steps, and performing a further optimization iteration step of determining a further optimal applicator configuration comprising further applicator entry points based on the geometric information of the target to be treated.

[0040] It has been found that by utilizing the above method, treatments such as thermal ablation or brachytherapy can be improved. In particular, reduced physical interference between the applicators used is achieved. This reduces the overlap between the ablation areas delivered, thus reducing the risk of ablating the same tissue multiple times. Furthermore, the number of ablation or brachytherapy applicators that need to be deployed is reduced compared to known treatment procedures.

[0041] In one embodiment, the entry points are at least one of lattice holes in a lattice template and skin entry points.

[0042] In yet another aspect, a planning computer program for positioning a treatment applicator is presented, said planning computer program comprising program code means for causing a computer to perform the steps of the method as defined in claim 14.

[0043] It will be understood that the device of claim 1 for providing positioning assistance to a treatment applicator, the ablation system of claim 13, the method of claim 14 for providing positioning assistance to a treatment applicator, and the computer program of claim 15 have similar and / or identical preferred embodiments, in particular as defined in the dependent claims.

[0044] It will be understood that a preferred embodiment of the invention may also be any combination of the dependent claims or the above embodiments with the respective independent claim.

[0045] These and other aspects of the invention will be apparent from and elucidated with reference to the embodiment(s) described hereinafter. [Brief description of the drawings]

[0046] [Figure 1] 1 is a schematic illustrative diagram of an embodiment of an ablation system comprising an ablation therapy applicator and a positioning assistance device. [Diagram 2] 1 is a schematic illustration of an embodiment of a positioning assistance device; [Diagram 3] 13A-13C illustrate the use of a lattice template to provide positioning assistance for treating an organ. [Figure 4] 13A-13D illustrate a method for providing positioning assistance to a treatment applicator. [Diagram 5] FIG. 2 illustrates the steps of the method. [Figure 6] 1A-1D provide alternative illustrations of the different steps of the method. [Figure 7] 13 illustrates a computer program for providing positioning assistance to a treatment applicator. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0047] FIG. 1 shows an ablation system 100. The ablation system 100 comprises a treatment applicator 8 configured as an ablation treatment applicator 20 for percutaneous cancer treatment. The ablation treatment applicator 20 utilizes, for example, focused ultrasound (FU), microwave (MW), radio frequency (RF), focal laser ablation (FLA), cryoablation, irreversible electroporation (IRE), or brachytherapy. All these treatment modalities require the insertion of an applicator 8 into the tumor or affected organ. To facilitate the positioning of the applicator 8, a lattice template 14 with lattice holes 12 is utilized. The applicator 8 is inserted into the lattice holes 12 of the lattice template 14. The lattice holes 12 are examples of entry points 10. Depending on the treatment plan, the applicator 8 is inserted into a particular lattice hole 12 to treat the affected organ or tumor.

[0048] To assist the user in positioning the applicator 8, the ablation system 100 comprises a device 2 for providing positioning assistance for the treatment applicator 8. The device 2 comprises a delivery unit 4 as shown in FIG. 2. The delivery unit 4 is configured to provide distances r to be considered between applicator entry points 10. These applicator entry points 10 are either lattice holes 12 of a lattice template 14 as shown in FIG. 1 or any skin entry points, also called freehand entry points. The delivery unit comprises a graphical user interface, a configuration file or a database. The device 2 further comprises a graphical user interface 21 configured to visualize a set of applicator positioning configurations to ultimately assist the user in inserting the applicator 8 into the lattice holes 12 of the lattice template 14 or any skin entry points.

[0049] The distance r considered between the applicator entry points 10 is the distance in the surface plane 16 of the grid template 14, as shown in Figure 1. Figure 3 shows examples of different applicator trajectories 18 leading towards the organ 24 to be treated.

[0050] 2, the device 2 further comprises a processor 6. The processor 6 of the device 2 is configured to perform an optimization iteration step to determine an optimal applicator configuration comprising a first applicator entry point 10, and disables all entry points 22 within a distance r from the currently selected entry point 10 such that these entry points 22 cannot be used in further optimization iterations. The processor 6 is further configured to perform a further optimization iteration step to determine further optimal applicator configurations comprising further applicator entry points 10.

[0051] An example of an optimization iterative step for determining the optimal applicator configuration is then described. Herein, this known technique is extended by considering and disabling all approach points 22 within a distance r from the currently selected approach point 10, so that these approach points 22 are not available for further optimization iterations. As an example, a discrete inverse planning optimization or a mixed discrete-continuous inverse planning optimization technique is provided. The clinical expert is only required to provide this with the following inputs: a set of partitioned regions of interest, such as three-dimensional medical image data and one or more protection volumes, a set of clinical goals to be achieved, such as a desired ablation volume, and ablation specifications, such as a discrete set of ablation patterns for the applicator to be used. A set of iterative greedy algorithms is then used to search for the optimal minimum number of ablation device configurations that can meet the established clinical protocol goals as much as possible. The greedy algorithm searches for a local minimum.

[0052] The application of the above example comprises one or more of the following steps: In step 0, the planner is reminded to provide a set of segmented regions of interest. This step is performed automatically or based on user input. The segmentation into cancerous tissue, tissue at risk, and healthy tissue is of interest. The planner further takes care to convert the defined clinical protocol (i.e., a list of defined clinical goals / constraints) into a corresponding ablation-based mathematical object function (e.g., a convex quadratic function) that is optimized during a later optimization step. Considering the supplier data of the ablation device, a corresponding ablation binary mask is generated for all power-time settings provided in the device datasheet. A 3D grid of potential applicator tip positions and applicator orientations is sampled. Finally, a discrete set of ablation device configurations is generated by combining the sampled set of 3D applicator tip positions / orientations with all supplier-database ablation binary masks for all possible power-time settings.

[0053] According to step 1, the current best applicator configuration that minimizes the ablation-based objective function value is selected from the discrete set generated in step 0. In step 2, given the new set of applicator configurations selected in step 1, the positions of the corresponding applicators are locally refined together with the corresponding ablation binary masks. As was done previously in step 1, the ablation-based objective function is now used to guide the optimizer towards an optimal set of applicator tip positions, orientations and delivered ablation binary masks. According to step 3, steps 1 and 2 are performed iteratively until a solution satisfying all clinical constraints is reached and / or a maximum number of applicators defined by the user has been selected.

[0054] Another aspect of the example is that the optimization problem definition is described in the following steps: First, the optimization problem is defined: the tissue volume under consideration is partitioned into structures and a binary mask S is calculated for each kth structure of interest (i.e., tumor, organ at risk, normal tissue, etc.). k is provided.

[0055] Physicians define clinical protocols with a list of all ablation area-based goals that need to be met. A typical goal of thermal ablation therapy is to deliver tailored ablation over localized cancer cells while sparing as much of the nearby healthy tissue as possible. This goal is challenging because ablation of large target volumes competes with the ablation-induced damage tolerance of normal tissues.

[0056] Before introducing a set of ablation range-based objective functions, mathematical expressions are required for the delivered ablation area of ​​a single applicator configuration, and for the composite ablation produced by the delivery of multiple applicator configurations.

[0057] Then, an initial discrete set of ablation applicator configurations is generated: Taking into account the supplier device datasheet, the predicted ablation area for each jth combination of ablation power and delivery time values ​​is converted into a corresponding 3D ablation binary mask.

number

[0058] N c A discrete set C of applicator configurations is constructed, where each applicator configuration is a combination of discretized applicator paths (i.e., skin insertion point, direction, and final applicator tip position) and a predicted binary mask A generated by the power and delivery time settings. c It consists of a moderately low concentration of N cTo preserve , only applicator configurations with binary masks that at least partially overlap the tumor region are considered for set C. Furthermore, a moderate sampling factor and coarse spatial discretization of applicator positions and orientations can be used to populate set C, with subsequent local refinement of the discrete positions and orientations of the selected best applicator.

[0059] Finally, when multiple ablation device configurations are delivered, the composite ablation binary mask A is a composite of all associated ablation binary masks A s It is defined as the union of A=∪ s A s (E2)

[0060] The concept of ablation fraction volume is used below to quantify the volume of a structure affected by ablation. Considering the kth segmented structure, its current ablation fraction structure volume v generated by the composite ablation binary mask A is k but,

number

[0061] Here, S k denotes the binary mask of the kth segmented region of interest, |·| is the cardinality (i.e., size) operator, and |A∩S k | denotes the cardinality of the intersection between the cumulative ablation volume and the structure (ie, the portion of the segmented structure currently being ablated).

[0062] In general, the clinical goals for all areas of interest are

number

[0063] To achieve the required ablation range of the tumor while sparing as much nearby healthy tissue as possible, the physician may specify a threshold for the ablation range in every region of interest. For example, a given minimum (and / or maximum) volume fraction t of the kth structure is ablated. For this purpose, the following ablation range-based quadratic objective function is used as an optimization constraint:

number

[0064] where H(·) is the Heaviside step function, t is the prescribed minimum (maximum) ablation fraction volume threshold, and v k denotes the current ablation fractional structural volume generated by composite ablation A (see Equation E3).

[0065] All mathematical objectives i (A) is given as a function of the composite ablation zone binary mask A generated by all selected ablation applicator configurations (see Eq. 2). The composite objective function

number

[0066] Quantity w i and w Rand R(A) denote the manually set importance weights for all ablation range-based objective functions and the regularization term, respectively. The regularization term R(A) is optional and can have various forms. It can be used to implement some required specific ablation zone characteristics. For example, Tikhonov regularization to avoid excessively high concentration (i.e., size) for the composite ablation binary mask A, or a regularization term to control (e.g., reduce) the size of the overlap (i.e., intersection) between the ablation binary masks of all delivered applicators that leads to excessive ablation of tumor subregions, may be used.

[0067] As a next step, the optimal discrete applicator configuration is selected as follows: N c Given a discrete set C of applicator configurations, for each c-th configuration we define an ablation binary mask A c It is assumed that P is defined as the set of currently selected applicator configurations and A is the corresponding composite (i.e. union) ablation area binary mask, calculated as shown in equation E2.

[0068] The goal of some examples using the set including the greedy iterative algorithm is to find new applicator configurations c that can potentially yield improved values ​​of the ablation-based function F(A) in equation (E6). * For the selection of the best applicator configuration at the current step n, the objective function value f c , i.e., the range-based function value is f c =F(A∪A c ),c=1,...,N c (E7) for each possible c-th applicator configuration by

[0069] where c represents the exponent of the ablation applicator configuration and N cis the total number of applicator configurations in the discrete set C. At each iteration n, the applicator configuration c * are selected and added to the optimal set P of applicator configurations. c * =min{f c},c=1,...,N c (E8)

[0070] In this example, a greedy iterative algorithm is disclosed in which multiple optimal applicator configurations are successively selected. The selection of the best applicator configuration is performed by iterating through all N c For each ablation device configuration, the objective function f c =F(A∪A c ) is calculated. As a result, all the ablation fraction structural volumes v k (Equation E3) is the iteration of the algorithm for all N c These volumetric calculations may represent a serious bottleneck for algorithm performance. k To speed up the calculation of v k (A∪A c ,S k )=v k (A,S k )+v k (A c ,S k )-v k (A∩A c ,S k ) (E9) may be used.

[0071] Here, on the right side of Equation E9, v k (A c ,S k ) may be initially pre-calculated, and v k (A,S k ) are known from previous algorithm iterations, while the last term, v k (A∩A c ,Sk ) for all N c New v for each ablation device configuration k (A∪A c ,S k ) is calculated to obtain the value of the intersection (A ∩ A c ) is the union set (A ∪ A c ), so using equation E9, v k (A∪A c ,S k ) can significantly reduce the required calculation time.

[0072] Then, an iterative local refinement of the selected applicator configurations is performed. Given the new set P of selected applicator configurations from the previous step, the spatial locations of the corresponding applicators and the delivered ablation binary masks can be locally refined by solving a constrained optimization problem of discrete-continuous mixed iterations. This local refinement step is not mandatory and may be omitted if the spatial discretization used for the generation of the initial set C of applicator configurations in the first step is deemed sufficiently accurate by the physician.

[0073] However, if an extremely coarse spatial discretization is used to reduce the computational burden, iterative local refinement steps can be performed to improve the accuracy of the delivered applicator configuration and consequently increase the tumor ablation range. In each refinement iteration, first, the optimal local rigid transformation of the ablation masks of all currently selected applicators is determined by minimizing an ablation-based functional with respect to the treatment target.

[0074] Starting from the initially selected discrete applicator positions and ablation binary mask, an optimal roto-translation transformation (R,t) is calculated and applied to the applicator positions / orientations to extend the tumor ablation range. This is done by using the parameters R of the rigid body transformation used to transform the selected applicator tip position and orientation and the associated binary mask at each grid position x. p ,t p The function introduced in E6 for

number

[0075] Optionally, lower and upper bounds may be applied to the optimization problem to restrict the optimization search to a set of feasible solutions that include only rotation-translation transformations of clinically deliverable applicators. Finally, the result is the optimal rigid body transformation parameters (R * p ,t * p ) is obtained. As a second local refinement step, the rigidly transformed applicator position is kept fixed and the corresponding delivered ablation binary mask A p is optimally updated, where all possible binary masks provided by the device manufacturer's specifications are evaluated at the current rotated and translated applicator position. Finally, the ablation mask A that results in the minimum object function value of the ablation range, i.e. the minimum range-based function value, is p A new set of is selected.

[0076] To reduce the computational cost of this local refinement step, the object function evaluation may be limited to only update the ablation binary mask of the last selected applicator configuration while the binary masks of all other selected applicators remain fixed. This iterative local refinement step switches between optimizing applicator positioning and selecting the optimal binary mask until a user-specified ablation-based object function accuracy and / or maximum number of local refinement iterations is achieved.

[0077] An iterative optimization strategy is then described. The procedure parameters (P, R) are optimized in an iterative strategy that switches between optimal selection of an applicator configuration from an initial discrete set and local refinement of the currently selected applicator configuration using a continuous optimization method. * p ,t * p ) is optimized. First, the best applicator configuration is selected from a large set of discrete applicator configurations, and then during a second step, all currently selected applicators are locally repositioned to improve, for example, the total ablation coverage of the tumor.

[0078] The algorithm iterations are stopped if / when a given relative ablation range based function F accuracy is achieved and / or a given maximum number of ablation applicators to be selected is reached. In these cases, the last achieved solution is N sel The selected applicators are returned.

[0079] Finally, to improve the overall computation time, two or more new applicator configurations may be selected at each iteration, which reduces the number of function calculations required to search for the optimal solution, at the expense of some degradation in the quality of the ablation coverage.

[0080] The set of selected applicator configurations P and the corresponding composite ablation zones A can be suitably initialized by utilizing the a priori knowledge of clinical experts. If such initialization is not available, a completely empty initial setting (i.e.

number

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[0081] An optimized plan may be delivered by a clinical expert, where the implanted applicators are continuously tracked in real time, and if the detected applicator tips are misplaced with respect to the planned locations, these tracked misplacements may be taken into account to adaptively re-optimize the remaining set of applicators and the corresponding composite ablations to re-establish the expected plan quality.

[0082] 4 shows a related method 200 for providing positioning assistance for a treatment applicator 8. The method 200 comprises a step 202 of providing a distance r to be considered between entry points 10 and geometric information of the target to be treated, a step 204 of performing an optimization iteration step and a decision step of determining an optimal applicator configuration comprising selecting a first applicator entry point 10 based on the geometric information of the target to be treated, a step 206 of disabling all applicator entry points 22 within said distance r from the currently first selected applicator entry point 10 such that these entry points 22 are not available for further optimization iteration steps, and a step 208 of performing a further optimization iteration step and a decision step of determining a further optimal applicator configuration comprising selecting further applicator entry points 10 based on the geometric information of the target to be treated.

[0083] The iterative step of determining the optimal applicator configuration, which includes selecting a first applicator entry point 10, is again based on the algorithm described above.

[0084] The steps performed by the device 2 and the method 200 are illustrated with respect to Figures 5 and 6. An optimization iteration is performed, which includes an optimal applicator configuration, which comprises selecting an applicator entry point 10, as shown by the grid template 14 on the left side of Figure 5. As a next step, all entry points 22 within a distance r from the entry point 10 are disabled, so that these entry points 22 cannot be used in further optimization iterations. Then, as shown by the grid template 14 located in the center of Figure 5, a further optimization iteration is performed, in which a further optimal applicator configuration is determined, which comprises selecting a further applicator entry point 10. Again, all entry points 22 within a distance r from the currently selected entry point 10 are disabled. The iterative steps are performed until no further optimal applicator configuration can be determined taking into account the disabled entry points 22.

[0085] In other words, the disabled entry points 22 form a circular invalidation mask, as indicated by the grey color applied around each currently selected entry point 10. The above method has the advantage that the optimization degrees of freedom, also called solver degrees of freedom, are simply gradually reduced with each iteration. Initially, all entry points 10 or lattice holes 12 are valid and selectable, but the more entry points 10 or lattice holes 12 the solver selects during an iteration, the more entry points 10 become invalid, and so the more constrained the search and solution space of the solver becomes. Solutions delivered by solvers using such techniques guarantee that a minimum distance r defined by the user is enforced.

[0086] FIG. 6 shows another example where the proposed iteration is applied to a grid template 14 with grid holes 12 spaced apart from each other. For a small distance r1, the applicator 8 can be inserted into every grid hole 12. This is shown on the left side of FIG. 6. If the distance is increased to approach r2 or r3, as shown in the center template 14 or the right template 14, the grid holes 12 are disabled. As shown in the center of FIG. 6, only the grid holes 22 along the imaginary vertical and horizontal axes (not shown in FIG. 6) passing through the entry point 10 are disabled. In the right grid template 14, the grid holes 22 at an angle to the imaginary horizontal or vertical axis passing through the entry point 10 are also disabled.

[0087] In general, in the template or skin surface plane 16 (see FIG. 1), the distance r may be a minimum distance, in particular a minimum Euclidean distance. In the template or skin surface plane 16, the distance r may also be a maximum distance, in particular a maximum Euclidean distance. The distance r may also be a minimum and / or maximum distance between applicator trajectories 18 as shown in FIG. 3.

[0088] The optimization includes heuristic, iterative and discrete optimization. If the applicator 8 is an ablation applicator 20, the optimization iterations consider at least one of the following: the lattice geometric information of the lattice template 14, the local ablation coverage target, the manufacturer ablation zone for the selected applicator 8 to be used for treatment, and a piecewise mesh considering the region of interest that guides the lattice location, in particular the lesion and the organ at risk. The lattice geometric information of the lattice template 40 includes at least one of the following: the lattice model, the lattice size, the number of lattice holes, the distance between the lattice holes. The processor 6 of the device 2 shown in FIG. 2 is further configured to perform refinement iterations, in which the optimal applicator configuration is removed from the solution and replaced by a new optimal applicator configuration. In one embodiment, the refinement iterations may comprise removing the optimal applicator configurations one by one, i.e. in sequence, at each iteration step. For example, a list of applicators may be used, and at each iteration step of the refinement iterations, the applicator configurations corresponding to certain applicators of the list of applicators are removed. In this case, applicator configurations may be removed successively, i.e., as defined by the order of the applicators in the list. Alternatively, an applicator configuration that degrades the functional as little as possible when removed from the current solution may be selected and removed. This applicator configuration may be considered as the minimum optimal, i.e., the applicator configuration for which the remaining applicator configurations have a greater impact on the respective optimization goal. Each minimum optimal applicator configuration may be determined based on range function values, which may be calculated for a given set of applicators and ablation zones, for example, using the above formula E7. Furthermore, functional derivatives may also be analytically calculated or discretely estimated, if necessary, based on range-based function values, to determine the minimum optimal applicator configuration.

[0089] 7 shows a computer program 300. The computer program 300 provides assistance for position planning for the treatment applicator 8. The computer program 300 comprises program code means 302 for causing a computer to perform the steps of the method as shown in FIG.

[0090] The device 2 or method 200 is further configured to provide at least one of the following delivery parameters: brachytherapy catheter position, thermal ablation proposal, dual time, ablation time / power value. With the help of the method 200 or the device 2, delivery of ablation zones over lattice holes 12 at a distance shorter than the user-defined distance r is avoided. This also leads to a reduction in treatment execution complexity due to reduced physical interference between applicators 8 and reduced overlap between ablation zones, which means a reduced risk of treating the same tissue multiple times.

[0091] Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed invention, from a study of the drawings, the disclosure, and the appended claims.

[0092] In the claims, the word "comprising" does not exclude other elements or steps and the word "a" or "an" does not exclude a plurality.

[0093] A single unit or device may fulfill the functions of several items recited in the claims. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

[0094] Procedures such as performing optimization iterations or invalidating all applicator entry points within a distance from a currently selected entry point, performed by one or several units or devices, may be performed by any other number of units or devices. These procedures may be implemented as program code means of a computer program and / or as dedicated hardware.

[0095] The computer program may be stored / distributed on a suitable medium, such as an optical storage medium or a solid-state medium, provided together with or as part of other hardware, but also distributed in other forms, such as via the Internet or other wired or wireless telecommunications systems. Any reference signs in the claims should not be construed as limiting the scope.

[0096] The present invention relates to a planning device for providing positioning assistance to a treatment applicator. The device comprises a providing unit configured to provide distances to be considered between applicator entry points, and a processor. When using treatment applicators, particularly ablative treatment applicators, it has been found that placing two or more applicators close together increases the risk of ablation and damaging more tissue than necessary for successful therapy. By configuring the processor to disable all entry points within a distance r from a currently selected entry point, such that these entry points cannot be used in further optimization iterations, and by subsequently performing further optimization iterations, an optimal applicator configuration can be provided that is effective with respect to successful therapy, but with a reduced risk of ablating more tissue than necessary.

Claims

1. 1. A planning device for positioning a treatment applicator, the planning device comprising: a providing unit for providing a distance to be considered between applicator entry points; and a processor, the processor comprising: performing an iterative optimization step of determining an optimal applicator configuration comprising a first applicator entry point; Disabling all entry points within the distance provided by the providing unit from the first applicator entry point such that all entry points are unavailable for further optimization iteration steps; performing a further optimization iteration step to determine an optimal applicator configuration with further applicator entry points; the processor further performs a refinement iteration step in which applicator configurations are removed from the solution and replaced by other applicator configurations; In each refinement iteration step, the applicator configuration to be removed is selected by a range-based function value or derivative to select a minimal optimal applicator configuration; the range-based function value is a numerical value that quantifies the range that a set of applicator configurations provides to a region of interest; The applicator configuration removed is the minimum optimal applicator configuration. Planning device.

2. the planning device providing positioning assistance to an ablation therapy applicator, the processor further comprising: receiving three-dimensional medical image data describing the subject; receiving a desired ablation volume registered with the three-dimensional medical image data; receiving one or more protection volumes registered with the three-dimensional medical image data; generating a discrete set of ablation applicator positions registered to the three-dimensional medical image data; receiving a discrete set of ablation patterns; initializing a composite ablation binary mask registered to the three-dimensional medical image data; determining a non-ablation volume registered to the three-dimensional medical image data by comparing the composite ablation binary mask with the desired ablation volume; determining an applicator configuration including an applicator entry point using an objective function selected depending on the one or more protection volumes, the non-ablative volume, the discrete set of ablation patterns, the discrete set of ablation applicator positions, and a disabled applicator entry point, wherein the applicator configuration specifies one of the discrete set of ablation applicator positions and one of the discrete set of ablation patterns; 2. The planning device of claim 1, wherein the composite ablation binary mask is updated by calculating a union of the composite ablation binary mask and the one of the discrete set of the ablation patterns positioned at the one of the discrete set of the ablation applicator positions.

3. The planning device of claim 2 , wherein the selected objective functions include an ablation range-based object quadratic function, a minimum / maximum ablation range function, and a uniform quadratic range function.

4. The planning device of claim 1 , wherein the entry points are at least one of lattice holes of a lattice template and skin entry points.

5. The planning device of claim 1 , wherein the distance is provided by a user and is at least one of a minimum distance in a template or skin surface plane, a maximum distance in the template or skin surface plane.

6. The planning device of claim 1 , wherein the distance is provided by a user and is a minimum and / or maximum distance between different intrusions of an applicator trajectory.

7. The planning device of claim 1 , wherein further optimization iterations are performed taking into account disabled entry points until no further optimal applicator configurations can be determined.

8. The processor further comprises: The planning device of claim 1 , further performing said optimization iteration steps, if available, to determine said optimal applicator configuration.

9. The planning device of claim 1 , wherein the refinement iterations are stopped when any applicator configuration has been removed once during the iterations or when the removed applicator configuration is re-determined as a result of the iteration step.

10. an optimal set of applicator positioning configurations; Disabled entry points, The distance to be considered between the entry points The planning device of claim 1 , further comprising a graphical user interface that visualizes at least one of:

11. An ablation system comprising the ablation therapy applicator and the planning device of claim 1 .

12. 1. A planning method for positioning a treatment applicator, the planning method comprising: a providing unit providing distances to be considered between entry points and geometric information of a target to be treated; a processor performing an iterative optimization step of determining an optimal applicator configuration comprising a first applicator entry point; the processor disabling all applicator entry points within the distance from the first applicator entry point such that all of the entry points are unavailable for further optimization iterations; performing further optimization iterations by the processor to determine further optimal applicator configurations with further applicator entry points; a refinement iteration step in which applicator configurations are removed from the solution and replaced by other applicator configurations; in each refinement iteration step, selecting an applicator configuration to be removed by a range-based function value or derivative to select a minimal optimal applicator configuration; and the range-based function value is a numerical value that quantifies the range that a set of applicator configurations provides to a region of interest; The applicator configuration removed is the minimum optimal applicator configuration. Planning methods.

13. 13. A planning computer program for positioning the treatment applicator, the planning computer program comprising program code means for causing a planning device according to claim 1 to perform the steps of the planning method according to claim 12.

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