Method for surface matching registration of an object in image guided navigation

Abstract

The disclosed method encompasses a computer-implemented medical method for surface matching registration of an object in image guided navigation. In particular, the method provides an identification of an optimal matching solution from a plurality of similar matching solutions comprising the lowest distance matching solution based on at least one matching parameter. This method allows finding a better optimal matching solution.

Description

[0001] METHOD FOR SURFACE MATCHING REGISTRATION OF AN OBJECT IN IMAGE GUIDED NAVIGATION

[0002] FIELD OF THE INVENTION

[0003] The present invention relates to a computer-implemented method for surface matching registration of an object in image guided navigation, a data processing apparatus for carrying out the method, a corresponding computer program, a corresponding computer-readable medium, as well as a corresponding surgical navigation system.

[0004] TECHNICAL BACKGROUND

[0005] The invention relates to the technical field of patient registration for image guided surgery, specifically surface matching registration. An intraoperatively acquired point cloud from a patient, for example in Cranial use case on the head, is matched with a surface of an image data set of the patient.

[0006] For the matching of the intraoperatively acquired point cloud with the image data set, a matching algorithm is used, for example an iterative closest point, ICP, like algorithm, for different starting positions of the point cloud.

[0007] For the ICP algorithm a start position needs to be guessed and the algorithm reduces the distances of all points of the point cloud to the surface and calculates these matching distances. The matching algorithm tries to reduce the distance to a minimum for finding a good match. However, the matching algorithm also can end in a local minimum, which does not represent the best overall matching result. Thus, the matching algorithm is started from changed starting positions to find the overall best matching result, represented by the matching distance. The disadvantage of this method is that it can happen that several matching solutions are found and then the one with smallest RMS is selected as the final solution. However, simulations showed that in some cases (~20-30%) another matching solution would have been the more accurate registration but got not selected due to a slightly higher matching distance value.

[0008] The present invention has the object of providing a method for surface matching registration of an object in image guided navigation that finds a more accurate optimal matching solution.

[0009] The present invention can be used for image-guided surgery such as Cranial Navigation.

[0010] Aspects of the present invention, examples and exemplary steps and their embodiments are disclosed in the following. Different exemplary features of the invention can be combined in accordance with the invention wherever technically expedient and feasible.

[0011] EXEMPLARY SHORT DESCRIPTION OF THE INVENTION

[0012] In the following, a short description of the specific features of the present invention is given which shall not be understood to limit the invention only to the features or a combination of the features described in this section.

[0013] The disclosed method encompasses a computer-implemented medical method for surface matching registration of an object in image guided navigation.

[0014] In particular, the method provides an identification of an optimal matching solution from a plurality of similar matching solutions comprising the lowest distance matching solution based on at least one matching parameter. This method allows finding a better optimal matching solution. GENERAL DESCRIPTION OF THE INVENTION

[0015] In this section, a description of the general features of the present invention is given for example by referring to possible embodiments of the invention.

[0016] According to an aspect of the invention, a computer-implemented medical method for surface matching registration of an object in image guided navigation, the method comprising the following steps: A step comprises obtaining a plurality of matching solutions, wherein each of the plurality of matching solutions is determined by performing a matching algorithm between image data of at least part of the object and registration data of registered points of a surface of the object. Each of the plurality of matching solutions comprises a matching distance between the image data and the registration data. Another step comprises determining a lowest distance matching solution, being the matching solution of the plurality of matching solutions with the lowest matching distance. Another step comprises determining at least one similar matching solution from the plurality of matching solutions, wherein the at least one similar matching solution comprises a matching distance with a difference to the lowest matching distance below a first threshold, wherein the at least one similar matching solution comprises the lowest distance matching solution. Another step comprises identifying an optimal matching solution from the at least one similar matching solution based on at least one matching parameter.

[0017] Consequently, compared to the known approaches, wherein the lowest distance matching solution is considered the optimal matching solution, the provided method takes into account further factors besides only considering the matching distance to determine an optimal matching solution that might not be the lowest distance matching solution. In other words, the provided method implements a smart selection of the optimal matching solution beyond just considering the matching distance between the image data and the registration data. In further other words, the provided method comprises an evaluation of similar matching solutions for the optimal matching solution. Thus, a more accurate optimal matching solution can be found that differs from the lowest distance matching solution. The term “matching solution”, as used herein, relates to a correspondence between the image data and the registration data. In other words, the matching solution describes a spatial relation between the image data and the registration data. Preferably, the matching solution is reflected by a transformation matrix between a coordinate system of the image data and a transformation system of the registration data.

[0018] The term “registration data”, as used herein, comprises spatial information of each registered point of an actual object, in this case the surface of the object, within a space. The registration data is preferably determined by a registration device. The registration device preferably comprises a tracked pointer that is brought into contact with the object to perform contact-based registration. Alternatively, the registration device preferably comprises a non-contact based registration device that is configured to perform non-contact-based registration.

[0019] The term “image data”, as used herein, comprises images of the object, in particular a patient, that have been acquired pre-operatively and / or intra-operatively. Preferably, the image data is stored in a data storage of a surgical navigation system for computer assisted surgery.

[0020] The term “matching algorithm”, as used herein, comprises an algorithm that is configured to match the registration data with the image data set. In other words, the matching algorithm tries to match a location of the registration data and the image data set in the same space. This means that the matching solution provided by the matching algorithm relates to a spatial relation between the image data and the registration data. Preferably, the matching algorithm is a iterative closest point, ICP, algorithm. For the ICP algorithm a start position needs guessed and the algorithm reduces the distances of all points of the point cloud to the surface and calculates these matching distances. The matching algorithm tries to reduce the distance to a minimum for finding a good match. However, the matching algorithm also can end in a local minimum, which does not represent the best overall matching result. Even if the matching algorithm finds the global minimum of the lowest distances, it may not be the optimal solution as the image data and registration data are not the same, i.e. the surface has changed. Thus, the matching algorithm is started from changed starting positions to find the overall best matching result, represented by the matching distance.

[0021] The term “matching distance”, as used herein, comprises a single value that reflects the distance of each of the registered points to the image data, in particular to a surface of the object in accordance with the image data. The single value might be an average distance of each of the registered points to the image data. The matching distance therefore relates to the distance between the registered points taken from the surface of the object to the surface of the object according to the image data. The matching distance preferably comprises an average distance of the distances of each of the registered points and the surface of the object according to the image data. Further preferably, the average distance comprises a root mean square.

[0022] The term “similar matching solution”, as used herein, relates to all matching solutions that are considered viable matching solutions. Those so called competitor solutions are defined by having similar matching distances, differing from the matching distance of the matching solution with the lowest matching distance, below a predetermined threshold. In other words, the similar matching solutions comprise the lowest matching solution and all other matching solutions that have matching distances similar to the lowest matching solution. The amount of similar matching solutions preferably is smaller than 20. In average, the amount of similar matching solutions is 10.

[0023] Preferably, the image data is acquired from a medical database.

[0024] Identifying the optimal matching solution based on at least one matching parameter preferably comprises using only one matching parameter. When using a plurality of matching parameters, each matching parameter can be considered equally important. Alternatively, the matching parameters comprise weighting factors, reflecting the different importance of the parameter.

[0025] In addition, the at least one matching parameter can be used to reduce the amount of similar matching solutions until only the optimal matching solution remains. For example, 10 similar matching solutions are determined based on the matching distance. For example, applying a first matching parameter leads to only 3 similar matching solutions considering the matching distance and the first matching parameter. For example, applying a second matching parameter leads to only one matching solution left, which is then considered the optimal matching solution.

[0026] Alternatively, the first matching parameter and the second matching parameter are combined to one single metric that is used to identify the optimal matching solution.

[0027] Preferably, identifying the optimal matching solution from the at least one similar matching solution based on at least one matching parameter comprises selecting the similar matching solution that fits the at least one matching parameter the best as the optimal matching solution. Alternative, identifying the optimal matching solution from the at least one similar matching solution based on at least one matching parameter comprises discarding the similar matching solutions that do not comply with the at least one matching parameter and selecting the remaining similar matching solution with the lowest matching distance as the optimal matching solution.

[0028] Consequently, the method allows finding a better optimal matching solution.

[0029] In a preferred embodiment, each of the plurality of matching solutions comprises a transformation matrix between an image data-coordinate system of the image data and a registration data-coordinate system of the registration data.

[0030] Consequently, determining the matching distance between the image data and the registration data comprises applying the transformation matrix on the registered points of the registration data and determining for each of the transformed registered points a distance to the surface of the object in accordance with the image data. In other words, in order to compare the matching between the registration data and the image data, the data has to be brought into the same coordinate system. For example, each of the registered points are transformed from the registration data-coordinate system into the image data-coordinate system. The transformation matrix works in both directions and thus can also be used to transform the image data from the image data- coordinate system to the registration data-coordinate system. The matching distance of the matching solution then relates to a metric indicating the distances of each of the registered points to the image data. The metric for example comprises a sum of the distances or an average of the distances. The distance of each of the registered points to the image data is a signed value and as such can take negative values.

[0031] In a preferred embodiment, the matching distance between the image data and the registration data comprises an average distance of distances of each of the registered points to the surface of the object according to the image data.

[0032] Using the average distance of the distances of each of the registered points to the surface of the object according to the image data allows to provide a single value reflecting how good the matching of the respective matching solution is. The average distance is preferably determined by adding up the absolute value of the distances of each of the registered points to the surface of the object according to the image data and dividing that number by the amount of registered points.

[0033] In a preferred embodiment, the average distance comprises a root mean square, RMS, of the distances of each of the registered points to the surface of the object according to the image data.

[0034] Using the RMS values higher values of the distances more than smaller values. Thus, the RMS provides a more accurate indication of a quality of a matching solution compared to only the average distance.

[0035] Using the RMS, the first threshold preferably is 4mm or lower.

[0036] In a preferred embodiment, the method comprises the following steps: A step comprises determining for each of the at least one similar matching solution a region of interest, ROI, matching position, being the position of the ROI of the object transformed based on the respective matching solution. Another step comprises determining for each of the at least one similar matching solution a ROI distance, being a difference between the ROI matching position of the at least one similar matching solution and the lowest distance matching solution. If the ROI distance exceeds a second threshold, the method comprises identifying an optimal matching solution from the at least one similar matching solution based on at least one matching parameter. Otherwise, the method comprises determining the lowest distance matching solution as the optimal matching solution.

[0037] In other words, if the at least one similar matching solution do not vastly differ in a region of interest, there is no need to perform the identifying of the optimal matching solution based on the at least one matching parameter and the optimal matching solution is just the lowest distance matching solution. Thus, the ROI matching position indicates an impact of the at least one matching solution on the ROI. Consequently, based on the impact on the ROI, it is decided if the additional step based on the at least one matching parameter is performed. Thus, the more complex determination of a better optimal matching solution is only executed if it seems to be helpful. This reduces the overall computational effort for the method.

[0038] The term “ROI matching position”, as used herein, relates to a position of the ROI of the object in a registration data-coordinate system of the respective matching solution.

[0039] The term “ROI distance”, as used herein, relates to a difference between a position of the ROI in a registration data-coordinate system of the respective matching solution compared to a position of the ROI in the registration data-coordinate system of the lowest distance matching solution.

[0040] In other words, it is enough that only one of the ROI distances exceed the second threshold to perform the additional step based on the at least one matching parameter.

[0041] Preferably, the ROI comprises at least one of a tumor object and a trajectory.

[0042] In a preferred embodiment, the at least one matching parameter comprises at least one pre-registration landmark.

[0043] The at least one landmark defines a specific area of the object, in particular around a concise point.

[0044] The process of obtaining the registration data of a surface of the object does usually not allow a good selection of what part of the surface of the object is in fact registered. Thus, if there are a plurality of similar matching solutions that need to be further evaluated to find the optimal matching solution, considering the matching distance only based on registered points relating to a pre-registration landmark, helps to further reduce the amount of similar matching solutions or determine the optimal matching solution. In other words, a landmark matching distance is determined, which is the matching distance between the image data and the registration data in the at least one landmark.

[0045] For example, the optimal matching solution is determined to be the matching solution of the at least one similar matching solution with the lowest landmark matching distance. Alternatively, the similar matching solutions with a landmark matching distance below a predetermined threshold are discarded.

[0046] Consequently, the method allows finding a better optimal matching solution.

[0047] In a preferred embodiment, identifying the optimal matching solution from the at least one similar matching solution based on the at least one matching parameter comprises the following steps. A step comprises determining a landmark matching distance for each of the similar matching solutions, wherein the landmark matching distance is the matching distance of the registration data at least one pre-registration landmark to the image data. Another step comprises identifying the optimal matching solution from the at least one similar matching solution based on the landmark matching distance.

[0048] In a preferred embodiment, the at least one pre-registration landmark comprises a nasion, a nasal spine, a cantus lateralis left, a cantus lateralis right and / or inion.

[0049] In a preferred embodiment, the at least one matching parameter comprises a distance distribution of each of the plurality of registration points.

[0050] The distance distribution preferably comprises a cluster of registration points with a relatively high or low value of matching distance between the image data and the registration data compared to the matching distance between the image data and the registration data of the remaining registration points.

[0051] In a preferred embodiment, the identifying the optimal matching solution from the at least one similar matching solution based on the at least one matching parameter comprises the following steps. A step comprises determining a distance distribution of the distances of each of the plurality of registration points and the image data for each of the similar matching solutions. Another step comprises discarding outliers of the registration points based on the distance distribution for each of the similar matching solutions. Another step comprises determining the similar matching solution with the lowest matching distance after discarding the outliers as the optimal matching solution.

[0052] In other words, the distance distribution is used to reduce the amount of registration points and discard the registration points that might have falsified the determined matching distances of the similar matching solutions.

[0053] Consequently, the method allows finding a better optimal matching solution.

[0054] In a preferred embodiment, the identifying the optimal matching solution from the at least one similar matching solution based on the at least one matching parameter comprises the following steps. A step comprises determining the distance distribution of the distances of each of the plurality of registration points and the image data for each of the similar matching solutions. Another step comprises identifying the optimal matching solution from the at least one similar matching solution based on distance distribution and an expected distance distribution.

[0055] The term “expected distance distribution”, as used herein, relates to know how in the image guided navigation reflecting properties of the distance distribution that are expected due to the nature of the image guided navigation, in particular the surface matching registration.

[0056] For example, when using a contact-based pointer device, also referred to as soft touch, as the pointer device, when obtaining the registration data from the surface of the object the object might deform. For example, the skin of the patient slightly deforms when obtaining the registration data from the object, which is referred to as skin shift. Thus, an expected distance distribution of the registered points is rather expected to have a majority of negative values.

[0057] In a further example, the expected distance distribution comprises an anomaly. For example, for determining the image data, the patient has to wear a headset that leads to skin shift. Thus, an expected distance distribution of the registered points is expected to have larger matching distance at registered points around the ear section of the patient.

[0058] For example, the optimal matching solution is determined to be the matching solution with a distance distribution closest to the expected distance distribution. Alternatively, the similar matching solutions with a distance distribution differing bigger than a threshold from the expected distance distribution are discarded.

[0059] Consequently, the method allows finding a better optimal matching solution.

[0060] In a preferred embodiment, the at least one matching parameter comprises additional registration points of the surface of the object.

[0061] The term “additional registration points”, as used herein, relates to registration points that are obtained by guiding a user, in particular a medical expert, to add additional registration points, in particular by using a registration device, of the surface of the object.

[0062] If there are a plurality of similar matching solutions that need to be further evaluated to find the optimal matching solution, considering the matching distance only based on additional registered points, helps to further reduce the amount of similar matching solutions or determine the optimal matching solution. In other words, an additional matching distance is determined, which is the matching distance between the image data and the additional registration points.

[0063] For example, the optimal matching solution is determined to be the matching solution of the at least one similar matching solution with the lowest additional matching distance. Alternatively, the similar matching solutions with an additional matching distance below a predetermined threshold are discarded.

[0064] Alternatively, the similar matching solutions are presented to the user via the display device. Reachable areas of the surface of the object with relatively large matching distances are identified and visualized for the user. Alternatively, reachable areas of the surface of the object with relatively large matching differences between different matching solutions are identified and visualized for the user. The user then provides additional registration points in these areas. The optimal matching solution is then identified based on the additional registration points, in particular in combination with the registration points. In other words, an updated matching distance is calculated, without reperfoming the matching algorithm, based on the original registration points and the additional registration points. The optimal matching solution is then identified as the similar matching solution with the lowest updated matching distance.

[0065] Consequently, the method allows finding a better optimal matching solution.

[0066] In a preferred embodiment, identifying the optimal matching solution from the at least one similar matching solution based on the at least one matching parameter comprises the following steps. A step comprises providing, on a display device, a visual representation of the performed matching algorithm to the user. Another step comprises guiding the user to add the additional registration points. Another step comprises determining an additional matching distance for each of the similar matching solutions, wherein the additional matching distance is the matching distance between the additional registration points and the image data. Another step comprises identifying the optimal matching solution from the at least one similar matching solution based on the additional matching distance.

[0067] Preferably, the display device comprises non screen options like VR glasses, holograms or similar.

[0068] Preferably, the additional registration points may be added from areas of the object, which comprise registration points but also may be added from areas of the object, which did not comprise registration points yet. In a preferred embodiment, the at least one matching parameter comprises a user verification.

[0069] For example, the optimal matching solution is determined to be the matching solution of the at least one similar matching solution that is verified as such by the user. Alternatively, the similar matching solutions as determined by the user as discardable are discarded.

[0070] Thus, the user is provided with a virtual representation of the similar matching solutions via a display device. The user provides verification input, referred to as user verification, based on which, the identification of the optimal matching solution is performed.

[0071] Consequently, the method allows finding a better optimal matching solution.

[0072] In a preferred embodiment, the identifying the optimal matching solution from the at least one similar matching solution based on the at least one matching parameter comprises the following steps. A step comprises determining a verification region, comprising at least part of the surface of the object. Another step comprises providing, on a display device, the region for each of the at least one similar matching solution to a user for verification. Another step comprises receiving the user verification from the user. Another step comprises identifying the optimal matching solution from the at least one similar matching solution based on the user verification.

[0073] In a preferred embodiment, the verification region is a region, in which a matching difference between different similar matching solutions exceed a predetermined threshold; or in which the matching distance of one of the at least one similar matching solutions exceeds a predetermined threshold. The matching difference is a difference between a surface matching position of the different similar matching solutions; wherein the surface matching position, being the position of the surface of the object transformed based on the respective matching solution. Preferably, a region is defined by a predetermined systematic. For example, a predetermined amount, for example 10, of neighbouring surface points define a region.

[0074] In a preferred embodiment, the amount of the plurality of matching solutions is predetermined.

[0075] In a preferred embodiment, the matching algorithm comprises an iterative closest point, ICP, algorithm.

[0076] In a preferred embodiment, the ICP algorithm determines the matching distance by iteratively reducing a distance between the registration data and the image data until a minimal matching distance is identified.

[0077] In a preferred embodiment, each matching solution comprises a starting position of the registration data, wherein the starting position comprises a location and an orientation of the registration data in space.

[0078] In a preferred embodiment, the image data comprises CT and / or MRT data.

[0079] In a preferred embodiment, the registration data is acquired by a registration device.

[0080] The registration device can be a contact-based registration device or a contact-free registration device.

[0081] According to another aspect of the invention, a data processing apparatus comprising means for carrying out the method, as described herein.

[0082] In other words, the invention is directed to at least one computer (for example, a computer), comprising at least one processor (for example, a processor) and at least one memory (for example, a memory), wherein a computer program performing the method steps of the method as described herein is running on the processor or is loaded into the memory, or wherein the at least one computer comprises the computer- readable program storage medium that causes the computer to carry out the method, as described herein. According to another aspect of the invention, a computer program which, when running on a computer or when loaded onto a computer, causes the computer to perform the method steps of the method, as described herein.

[0083] In other words, the invention is directed to a computer program which, when running on at least one processor (for example, a processor) of at least one computer (for example, a computer) or when loaded into at least one memory (for example, a memory) of at least one computer (for example, a computer), causes the at least one computer to perform the above-described method according to the first aspect. The invention may alternatively or additionally relate to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, carrying information which represents the program, for example the aforementioned program, which for example comprises code means which are adapted to perform any or all of the steps of the method according to the first aspect. A computer program stored on a disc is a data file, and when the file is read out and transmitted it becomes a data stream for example in the form of a (physical, for example electrical, for example technically generated) signal. The signal can be implemented as the signal wave which is described herein. For example, the signal, for example the signal wave is constituted to be transmitted via a computer network, for example LAN, WLAN, WAN, for example the internet. The invention according to the second aspect therefore may alternatively or additionally relate to a data stream representative of the aforementioned program.

[0084] According to another aspect of the invention, a computer-readable medium comprises instructions which, when executed by a computer, cause the computer to carry out the method, as described herein.

[0085] According to another aspect of the invention, a surgical navigation system for computer assisted surgery, the system comprising a data processing apparatus, as described herein.

[0086] For example, the invention does not involve or in particular comprise or encompass an invasive step which would represent a substantial physical interference with the body requiring professional medical expertise to be carried out and entailing a substantial health risk even when carried out with the required professional care and expertise. For example, the invention does not comprise a step of positioning a medical implant in order to fasten it to an anatomical structure or a step of fastening the medical implant to the anatomical structure or a step of preparing the anatomical structure for having the medical implant fastened to it. More particularly, the invention does not involve or in particular comprise or encompass any surgical or therapeutic activity. The invention is instead directed as applicable to an image guided navigation system. For this reason alone, no surgical or therapeutic activity and in particular no surgical or therapeutic step is necessitated or implied by carrying out the invention.

[0087] The present invention also relates to the use of the surgical navigation system in computer assisted surgery.

[0088] DEFINITIONS

[0089] In this section, definitions for specific terminology used in this disclosure are offered which also form part of the present disclosure.

[0090] Computer implemented method

[0091] The method in accordance with the invention is for example a computer implemented method. For example, all the steps or merely some of the steps (i.e. less than the total number of steps) of the method in accordance with the invention can be executed by a computer (for example, at least one computer). An embodiment of the computer implemented method is a use of the computer for performing a data processing method. An embodiment of the computer implemented method is a method concerning the operation of the computer such that the computer is operated to perform one, more or all steps of the method.

[0092] The computer for example comprises at least one processor and for example at least one memory in order to (technically) process the data, for example electronically and / or optically. The processor being for example made of a substance or composition which is a semiconductor, for example at least partly n- and / or p-doped semiconductor, for example at least one of II-, III-, IV-, V-, Vl-sem iconductor material, for example (doped) silicon and / or gallium arsenide. The calculating or determining steps described are for example performed by a computer. Determining steps or calculating steps are for example steps of determining data within the framework of the technical method, for example within the framework of a program. A computer is for example any kind of data processing device, for example electronic data processing device. A computer can be a device which is generally thought of as such, for example desktop PCs, notebooks, netbooks, etc., but can also be any programmable apparatus, such as for example a mobile phone or an embedded processor. A computer can for example comprise a system (network) of "sub-computers", wherein each sub-computer represents a computer in its own right. The term "computer" includes a cloud computer, for example a cloud server. The term "cloud computer" includes a cloud computer system which for example comprises a system of at least one cloud computer and for example a plurality of operatively interconnected cloud computers such as a server farm. Such a cloud computer is preferably connected to a wide area network such as the world wide web (WWW) and located in a so-called cloud of computers which are all connected to the world wide web. Such an infrastructure is used for "cloud computing", which describes computation, software, data access and storage services which do not require the end user to know the physical location and / or configuration of the computer delivering a specific service. For example, the term "cloud" is used in this respect as a metaphor for the Internet (world wide web). For example, the cloud provides computing infrastructure as a service (laaS). The cloud computer can function as a virtual host for an operating system and / or data processing application which is used to execute the method of the invention. The cloud computer is for example an elastic compute cloud (EC2) as provided by Amazon Web Services™. A computer for example comprises interfaces in order to receive or output data and / or perform an analogue-to-digital conversion. The data are for example data which represent physical properties and / or which are generated from technical signals. The technical signals are for example generated by means of (technical) detection devices (such as for example devices for detecting marker devices) and / or (technical) analytical devices (such as for example devices for performing (medical) imaging methods), wherein the technical signals are for example electrical or optical signals. The technical signals for example represent the data received or outputted by the computer. The computer is preferably operatively coupled to a display device which allows information outputted by the computer to be displayed, for example to a user. One example of a display device is a virtual reality device or an augmented reality device (also referred to as virtual reality glasses or augmented reality glasses) which can be used as "goggles" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device or a virtual reality device can be used both to input information into the computer by user interaction and to display information outputted by the computer. Another example of a display device would be a standard computer monitor comprising for example a liquid crystal display operatively coupled to the computer for receiving display control data from the computer for generating signals used to display image information content on the display device. A specific embodiment of such a computer monitor is a digital lightbox. An example of such a digital lightbox is Buzz®, a product of Brainlab AG. The monitor may also be the monitor of a portable, for example handheld, device such as a smart phone or personal digital assistant or digital media player.

[0093] The invention also relates to a program which, when running on a computer, causes the computer to perform one or more or all of the method steps described herein and / or to a program storage medium on which the program is stored (in particular in a non- transitory form) and / or to a computer comprising said program storage medium and / or to a (physical, for example electrical, for example technically generated) signal wave, for example a digital signal wave, carrying information which represents the program, for example the aforementioned program, which for example comprises code means which are adapted to perform any or all of the method steps described herein.

[0094] Within the framework of the invention, computer program elements can be embodied by hardware and / or software (this includes firmware, resident software, micro-code, etc.). Within the framework of the invention, computer program elements can take the form of a computer program product which can be embodied by a computer-usable, for example computer-readable data storage medium comprising computer-usable, for example computer-readable program instructions, "code" or a "computer program" embodied in said data storage medium for use on or in connection with the instructionexecuting system. Such a system can be a computer; a computer can be a data processing device comprising means for executing the computer program elements and / or the program in accordance with the invention, for example a data processing device comprising a digital processor (central processing unit or CPU) which executes the computer program elements, and optionally a volatile memory (for example a random access memory or RAM) for storing data used for and / or produced by executing the computer program elements. Within the framework of the present invention, a computer-usable, for example computer-readable data storage medium can be any data storage medium which can include, store, communicate, propagate or transport the program for use on or in connection with the instruction-executing system, apparatus or device. The computer-usable, for example computer-readable data storage medium can for example be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, apparatus or device or a medium of propagation such as for example the Internet. The computer-usable or computer-readable data storage medium could even for example be paper or another suitable medium onto which the program is printed, since the program could be electronically captured, for example by optically scanning the paper or other suitable medium, and then compiled, interpreted or otherwise processed in a suitable manner. The data storage medium is preferably a non-volatile data storage medium. The computer program product and any software and / or hardware described here form the various means for performing the functions of the invention in the example embodiments. The computer and / or data processing device can for example include a guidance information device which includes means for outputting guidance information. The guidance information can be outputted, for example to a user, visually by a visual indicating means (for example, a monitor and / or a lamp) and / or acoustically by an acoustic indicating means (for example, a loudspeaker and / or a digital speech output device) and / or tactilely by a tactile indicating means (for example, a vibrating element or a vibration element incorporated into an instrument). For the purpose of this document, a computer is a technical computer which for example comprises technical, for example tangible components, for example mechanical and / or electronic components. Any device mentioned as such in this document is a technical and for example tangible device.

[0095] Acquiring data

[0096] The expression "acquiring data" for example encompasses (within the framework of a computer implemented method) the scenario in which the data are determined by the computer implemented method or program. Determining data for example encompasses measuring physical quantities and transforming the measured values into data, for example digital data, and / or computing (and e.g. outputting) the data by means of a computer and for example within the framework of the method in accordance with the invention. The meaning of "acquiring data" also for example encompasses the scenario in which the data are received or retrieved by (e.g. input to) the computer implemented method or program, for example from another program, a previous method step or a data storage medium, for example for further processing by the computer implemented method or program. Generation of the data to be acquired may but need not be part of the method in accordance with the invention. The expression "acquiring data" can therefore also for example mean waiting to receive data and / or receiving the data. The received data can for example be inputted via an interface. The expression "acquiring data" can also mean that the computer implemented method or program performs steps in order to (actively) receive or retrieve the data from a data source, for instance a data storage medium (such as for example a ROM, RAM, database, hard drive, etc.), or via the interface (for instance, from another computer or a network). The data acquired by the disclosed method or device, respectively, may be acquired from a database located in a data storage device which is operably to a computer for data transfer between the database and the computer, for example from the database to the computer. The computer acquires the data for use as an input for steps of determining data. The determined data can be output again to the same or another database to be stored for later use. The database or database used for implementing the disclosed method can be located on network data storage device or a network server (for example, a cloud data storage device or a cloud server) or a local data storage device (such as a mass storage device operably connected to at least one computer executing the disclosed method). The data can be made "ready for use" by performing an additional step before the acquiring step. In accordance with this additional step, the data are generated in order to be acquired. The data are for example detected or captured (for example by an analytical device). Alternatively or additionally, the data are inputted in accordance with the additional step, for instance via interfaces. The data generated can for example be inputted (for instance into the computer). In accordance with the additional step (which precedes the acquiring step), the data can also be provided by performing the additional step of storing the data in a data storage medium (such as for example a ROM, RAM, CD and / or hard drive), such that they are ready for use within the framework of the method or program in accordance with the invention. The step of "acquiring data" can therefore also involve commanding a device to obtain and / or provide the data to be acquired. In particular, the acquiring step does not involve an invasive step which would represent a substantial physical interference with the body, requiring professional medical expertise to be carried out and entailing a substantial health risk even when carried out with the required professional care and expertise. In particular, the step of acquiring data, for example determining data, does not involve a surgical step and in particular does not involve a step of treating a human or animal body using surgery or therapy. In order to distinguish the different data used by the present method, the data are denoted (i.e. referred to) as "XY data" and the like and are defined in terms of the information which they describe, which is then preferably referred to as "XY information" and the like.

[0097] Registering

[0098] The n-dimensional image of a body is registered when the spatial location of each point of an actual object within a space, for example a body part in an operating theatre, is assigned an image data point of an image (CT, MR, etc.) stored in a navigation system.

[0099] Image registration

[0100] Image registration is the process of transforming different sets of data into one coordinate system. The data can be multiple photographs and / or data from different sensors, different times or different viewpoints. It is used in computer vision, medical imaging and in compiling and analysing images and data from satellites. Registration is necessary in order to be able to compare or integrate the data obtained from these different measurements.

[0101] Marker

[0102] It is the function of a marker to be detected by a marker detection device (for example, a camera or an ultrasound receiver or analytical devices such as CT or MRI devices) in such a way that its spatial position (i.e. its spatial location and / or alignment) can be ascertained. The detection device is for example part of a navigation system. The markers can be active markers. An active marker can for example emit electromagnetic radiation and / or waves which can be in the infrared, visible and / or ultraviolet spectral range. A marker can also however be passive, i.e. can for example reflect electromagnetic radiation in the infrared, visible and / or ultraviolet spectral range or can block x-ray radiation. To this end, the marker can be provided with a surface which has corresponding reflective properties or can be made of metal in order to block the x-ray radiation. It is also possible for a marker to reflect and / or emit electromagnetic radiation and / or waves in the radio frequency range or at ultrasound wavelengths. A marker preferably has a spherical and / or spheroid shape and can therefore be referred to as a marker sphere; markers can however also exhibit a cornered, for example cubic, shape.

[0103] Marker device

[0104] A marker device can for example be a reference star or a pointer or a single marker or a plurality of (individual) markers which are then preferably in a predetermined spatial relationship. A marker device comprises one, two, three or more markers, wherein two or more such markers are in a predetermined spatial relationship. This predetermined spatial relationship is for example known to a navigation system and is for example stored in a computer of the navigation system.

[0105] In another embodiment, a marker device comprises an optical pattern, for example on a two-dimensional surface. The optical pattern might comprise a plurality of geometric shapes like circles, rectangles and / or triangles. The optical pattern can be identified in an image captured by a camera, and the position of the marker device relative to the camera can be determined from the size of the pattern in the image, the orientation of the pattern in the image and the distortion of the pattern in the image. This allows determining the relative position in up to three rotational dimensions and up to three translational dimensions from a single two-dimensional image.

[0106] The position of a marker device can be ascertained, for example by a medical navigation system. If the marker device is attached to an object, such as a bone or a medical instrument, the position of the object can be determined from the position of the marker device and the relative position between the marker device and the object. Determining this relative position is also referred to as registering the marker device and the object. The marker device or the object can be tracked, which means that the position of the marker device or the object is ascertained twice or more over time.

[0107] Marker holder

[0108] A marker holder is understood to mean an attaching device for an individual marker which serves to attach the marker to an instrument, a part of the body and / or a holding element of a reference star, wherein it can be attached such that it is stationary and advantageously such that it can be detached. A marker holder can for example be rodshaped and / or cylindrical. A fastening device (such as for instance a latching mechanism) for the marker device can be provided at the end of the marker holder facing the marker and assists in placing the marker device on the marker holder in a force fit and / or positive fit.

[0109] Pointer

[0110] A pointer is a rod which comprises one or more - advantageously, two - markers fastened to it and which can be used to measure off individual co-ordinates, for example spatial co-ordinates (i.e. three-dimensional co-ordinates), on a part of the body, wherein a user guides the pointer (for example, a part of the pointer which has a defined and advantageously fixed position with respect to the at least one marker attached to the pointer) to the position corresponding to the co-ordinates, such that the position of the pointer can be determined by using a surgical navigation system to detect the marker on the pointer. The relative location between the markers of the pointer and the part of the pointer used to measure off co-ordinates (for example, the tip of the pointer) is for example known. The surgical navigation system then enables the location (of the three-dimensional co-ordinates) to be assigned to a predetermined body structure, wherein the assignment can be made automatically or by user intervention. Reference star

[0111] A "reference star" refers to a device with a number of markers, advantageously three markers, attached to it, wherein the markers are (for example detachably) attached to the reference star such that they are stationary, thus providing a known (and advantageously fixed) position of the markers relative to each other. The position of the markers relative to each other can be individually different for each reference star used within the framework of a surgical navigation method, in order to enable a surgical navigation system to identify the corresponding reference star on the basis of the position of its markers relative to each other. It is therefore also then possible for the objects (for example, instruments and / or parts of a body) to which the reference star is attached to be identified and / or differentiated accordingly. In a surgical navigation method, the reference star serves to attach a plurality of markers to an object (for example, a bone or a medical instrument) in order to be able to detect the position of the object (i.e. its spatial location and / or alignment). Such a reference star for example features a way of being attached to the object (for example, a clamp and / or a thread) and / or a holding element which ensures a distance between the markers and the object (for example in order to assist the visibility of the markers to a marker detection device) and / or marker holders which are mechanically connected to the holding element and which the markers can be attached to.

[0112] Navigation system

[0113] The present invention is also directed to a navigation system for computer-assisted surgery. This navigation system preferably comprises the aforementioned computer for processing the data provided in accordance with the computer implemented method as described in any one of the embodiments described herein. The navigation system preferably comprises a detection device for detecting the position of detection points which represent the main points and auxiliary points, in order to generate detection signals and to supply the generated detection signals to the computer, such that the computer can determine the absolute main point data and absolute auxiliary point data on the basis of the detection signals received. A detection point is for example a point on the surface of the anatomical structure which is detected, for example by a pointer. In this way, the absolute point data can be provided to the computer. The navigation system also preferably comprises a user interface for receiving the calculation results from the computer (for example, the position of the main plane, the position of the auxiliary plane and / or the position of the standard plane). The user interface provides the received data to the user as information. Examples of a user interface include a display device such as a monitor, or a loudspeaker. The user interface can use any kind of indication signal (for example a visual signal, an audio signal and / or a vibration signal). One example of a display device is an augmented reality device (also referred to as augmented reality glasses) which can be used as so-called "goggles" for navigating. A specific example of such augmented reality glasses is Google Glass (a trademark of Google, Inc.). An augmented reality device can be used both to input information into the computer of the navigation system by user interaction and to display information outputted by the computer.

[0114] The invention also relates to a navigation system for computer-assisted surgery, comprising: a computer for processing the absolute point data and the relative point data; a detection device for detecting the position of the main and auxiliary points in order to generate the absolute point data and to supply the absolute point data to the computer; a data interface for receiving the relative point data and for supplying the relative point data to the computer; and a user interface for receiving data from the computer in order to provide information to the user, wherein the received data are generated by the computer on the basis of the results of the processing performed by the computer.

[0115] Surgical navigation system

[0116] A navigation system, such as a surgical navigation system, is understood to mean a system which can comprise: at least one marker device; a transmitter which emits electromagnetic waves and / or radiation and / or ultrasound waves; a receiver which receives electromagnetic waves and / or radiation and / or ultrasound waves; and an electronic data processing device which is connected to the receiver and / or the transmitter, wherein the data processing device (for example, a computer) for example comprises a processor (CPU) and a working memory and advantageously an indicating device for issuing an indication signal (for example, a visual indicating device such as a monitor and / or an audio indicating device such as a loudspeaker and / or a tactile indicating device such as a vibrator) and a permanent data memory, wherein the data processing device processes navigation data forwarded to it by the receiver and can advantageously output guidance information to a user via the indicating device. The navigation data can be stored in the permanent data memory and for example compared with data stored in said memory beforehand.

[0117] Landmarks

[0118] A landmark is a defined element of an anatomical body part which is always identical or recurs with a high degree of similarity in the same anatomical body part of multiple patients. Typical landmarks are for example the epicondyles of a femoral bone or the tips of the transverse processes and / or dorsal process of a vertebra. The points (main points or auxiliary points) can represent such landmarks. A landmark which lies on (for example on the surface of) a characteristic anatomical structure of the body part can also represent said structure. The landmark can represent the anatomical structure as a whole or only a point or part of it. A landmark can also for example lie on the anatomical structure, which is for example a prominent structure. An example of such an anatomical structure is the posterior aspect of the iliac crest. Another example of a landmark is one defined by the rim of the acetabulum, for instance by the centre of said rim. In another example, a landmark represents the bottom or deepest point of an acetabulum, which is derived from a multitude of detection points. Thus, one landmark can for example represent a multitude of detection points. As mentioned above, a landmark can represent an anatomical characteristic which is defined on the basis of a characteristic structure of the body part. Additionally, a landmark can also represent an anatomical characteristic defined by a relative movement of two body parts, such as the rotational centre of the femur when moved relative to the acetabulum.

[0119] Imaging geometry

[0120] The information on the imaging geometry preferably comprises information which allows the analysis image (x-ray image) to be calculated, given a known relative position between the imaging geometry analysis apparatus and the analysis object (anatomical body part) to be analysed by x-ray radiation, if the analysis object which is to be analysed is known, wherein "known" means that the spatial geometry (size and shape) of the analysis object is known. This means for example that three-dimensional, "spatially resolved" information concerning the interaction between the analysis object (anatomical body part) and the analysis radiation (x-ray radiation) is known, wherein "interaction" means for example that the analysis radiation is blocked or partially or completely allowed to pass by the analysis object. The location and in particular orientation of the imaging geometry is for example defined by the position of the x-ray device, for example by the position of the x-ray source and the x-ray detector and / or for example by the position of the multiplicity (manifold) of x-ray beams which pass through the analysis object and are detected by the x-ray detector. The imaging geometry for example describes the position (i.e. the location and in particular the orientation) and the shape (for example, a conical shape exhibiting a specific angle of inclination) of said multiplicity (manifold). The position can for example be represented by the position of an x-ray beam which passes through the centre of said multiplicity or by the position of a geometric object (such as a truncated cone) which represents the multiplicity (manifold) of x-ray beams. Information concerning the above-mentioned interaction is preferably known in three dimensions, for example from a three- dimensional CT, and describes the interaction in a spatially resolved way for points and / or regions of the analysis object, for example for all of the points and / or regions of the analysis object. Knowledge of the imaging geometry for example allows the location of a source of the radiation (for example, an x-ray source) to be calculated relative to an image plane (for example, the plane of an x-ray detector). With respect to the connection between three-dimensional analysis objects and two-dimensional analysis images as defined by the imaging geometry, reference is made for example to the following publications:

[0121] 1. "An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision", Roger Y. Tsai, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Miami Beach, Florida, 1986, pages 364-374

[0122] 2. "A Versatile Camera Calibration Technique for High-Accuracy 3D Machine Vision Metrology Using Off-the-Shelf TV Cameras and Lenses", Roger Y. Tsai, IEEE Journal of Robotics and Automation, Volume RA-3, No. 4, August 1987, pages 323-344.

[0123] 3. "Fluoroscopic X-ray Image Processing and Registration for Computer-Aided Orthopedic Surgery", Ziv Yaniv

[0124] 4. EP 08 156 293.6

[0125] 5. US 61 / 054,187

[0126] Shape representatives

[0127] Shape representatives represent a characteristic aspect of the shape of an anatomical structure. Examples of shape representatives include straight lines, planes and geometric figures. Geometric figures can be one-dimensional such as for example axes or circular arcs, two-dimensional such as for example polygons and circles, or three-dimensional such as for example cuboids, cylinders and spheres. The relative position between the shape representatives can be described in reference systems, for example by co-ordinates or vectors, or can be described by geometric variables such as for example length, angle, area, volume and proportions. The characteristic aspects which are represented by the shape representatives are for example symmetry properties which are represented for example by a plane of symmetry. Another example of a characteristic aspect is the direction of extension of the anatomical structure, which is for example represented by a longitudinal axis. Another example of a characteristic aspect is the cross-sectional shape of an anatomical structure, which is for example represented by an ellipse. Another example of a characteristic aspect is the surface shape of a part of the anatomical structure, which is for example represented by a plane or a hemisphere. For example, the characteristic aspect constitutes an abstraction of the actual shape or an abstraction of a property of the actual shape (such as for example its symmetry properties or longitudinal extension). The shape representative for example represents this abstraction. Referencing

[0128] Determining the position is referred to as referencing if it implies informing a navigation system of said position in a reference system of the navigation system.

[0129] Atlas / Atlas segmentation

[0130] Preferably, atlas data is acquired which describes (for example defines, more particularly represents and / or is) a general three-dimensional shape of the anatomical body part. The atlas data therefore represents an atlas of the anatomical body part. An atlas typically consists of a plurality of generic models of objects, wherein the generic models of the objects together form a complex structure. For example, the atlas constitutes a statistical model of a patient’s body (for example, a part of the body) which has been generated from anatomic information gathered from a plurality of human bodies, for example from medical image data containing images of such human bodies. In principle, the atlas data therefore represents the result of a statistical analysis of such medical image data for a plurality of human bodies. This result can be output as an image - the atlas data therefore contains or is comparable to medical image data. Such a comparison can be carried out for example by applying an image fusion algorithm which conducts an image fusion between the atlas data and the medical image data. The result of the comparison can be a measure of similarity between the atlas data and the medical image data. The atlas data comprises image information (for example, positional image information) which can be matched (for example by applying an elastic or rigid image fusion algorithm) for example to image information (for example, positional image information) contained in medical image data so as to for example compare the atlas data to the medical image data in order to determine the position of anatomical structures in the medical image data which correspond to anatomical structures defined by the atlas data.

[0131] The human bodies, the anatomy of which serves as an input for generating the atlas data, advantageously share a common feature such as at least one of gender, age, ethnicity, body measurements (e.g. size and / or mass) and pathologic state. The anatomic information describes for example the anatomy of the human bodies and is extracted for example from medical image information about the human bodies. The atlas of a femur, for example, can comprise the head, the neck, the body, the greater trochanter, the lesser trochanter and the lower extremity as objects which together make up the complete structure. The atlas of a brain, for example, can comprise the telencephalon, the cerebellum, the diencephalon, the pons, the mesencephalon and the medulla as the objects which together make up the complex structure. One application of such an atlas is in the segmentation of medical images, in which the atlas is matched to medical image data, and the image data are compared with the matched atlas in order to assign a point (a pixel or voxel) of the image data to an object of the matched atlas, thereby segmenting the image data into objects.

[0132] Imaging methods

[0133] In the field of medicine, imaging methods (also called imaging modalities and / or medical imaging modalities) are used to generate image data (for example, two- dimensional or three-dimensional image data) of anatomical structures (such as soft tissues, bones, organs, etc.) of the human body. The term "medical imaging methods" is understood to mean (advantageously apparatus-based) imaging methods (for example so-called medical imaging modalities and / or radiological imaging methods) such as for instance computed tomography (CT) and cone beam computed tomography (CBCT, such as volumetric CBCT), x-ray tomography, magnetic resonance tomography (MRT or MRI), conventional x-ray, sonography and / or ultrasound examinations, and positron emission tomography. For example, the medical imaging methods are performed by the analytical devices. Examples for medical imaging modalities applied by medical imaging methods are: X- ray radiography, magnetic resonance imaging, medical ultrasonography or ultrasound, endoscopy, elastography, tactile imaging, thermography, medical photography and nuclear medicine functional imaging techniques as positron emission tomography (PET) and Single-photon emission computed tomography (SPECT), as mentioned by Wikipedia.

[0134] The image data thus generated is also termed “medical imaging data”. Analytical devices for example are used to generate the image data in apparatus-based imaging methods. The imaging methods are for example used for medical diagnostics, to analyse the anatomical body in order to generate images which are described by the image data. The imaging methods are also for example used to detect pathological changes in the human body. However, some of the changes in the anatomical structure, such as the pathological changes in the structures (tissue), may not be detectable and for example may not be visible in the images generated by the imaging methods. A tumour represents an example of a change in an anatomical structure. If the tumour grows, it may then be said to represent an expanded anatomical structure. This expanded anatomical structure may not be detectable; for example, only a part of the expanded anatomical structure may be detectable. Primary / high-grade brain tumours are for example usually visible on MRI scans when contrast agents are used to infiltrate the tumour. MRI scans represent an example of an imaging method. In the case of MRI scans of such brain tumours, the signal enhancement in the MRI images (due to the contrast agents infiltrating the tumour) is considered to represent the solid tumour mass. Thus, the tumour is detectable and for example discernible in the image generated by the imaging method. In addition to these tumours, referred to as "enhancing" tumours, it is thought that approximately 10% of brain tumours are not discernible on a scan and are for example not visible to a user looking at the images generated by the imaging method.

[0135] Mapping

[0136] Mapping describes a transformation (for example, linear transformation) of an element (for example, a pixel or voxel), for example the position of an element, of a first data set in a first coordinate system to an element (for example, a pixel or voxel), for example the position of an element, of a second data set in a second coordinate system (which may have a basis which is different from the basis of the first coordinate system). In one embodiment, the mapping is determined by comparing (for example, matching) the color values (for example grey values) of the respective elements by means of an elastic or rigid fusion algorithm. The mapping is embodied for example by a transformation matrix (such as a matrix defining an affine transformation).

[0137] Medical Workflow

[0138] A medical workflow comprises a plurality of workflow steps performed during a medical treatment and / or a medical diagnosis. The workflow steps are typically, but not necessarily performed in a predetermined order. Each workflow step for example means a particular task, which might be a single action or a set of actions. Examples of workflow steps are capturing a medical image, positioning a patient, attaching a marker, performing a resection, moving a joint, placing an implant and the like.

[0139] BRIEF DESCRIPTION OF THE DRAWINGS

[0140] In the following, the invention is described with reference to the appended figures which give background explanations and represent specific embodiments of the invention. The scope of the invention is however not limited to the specific features disclosed in the context of the figures, wherein

[0141] Fig. 1 illustrates the registration data and the image data;

[0142] Fig. 2 illustrates schematically an acquiring of the registration data;

[0143] Fig. 3a illustrates an identification of the optimal matching solution based on a matching distance only;

[0144] Fig. 3b illustrates an identification of the optimal matching solution based on a landmark matching distance;

[0145] Fig. 3c illustrates an identification of the optimal matching solution based on an ROI impact;

[0146] Fig. 3d illustrates an identification of the optimal matching solution based on an ROI impact and a landmark matching distance;

[0147] Fig. 4 illustrates an data processing apparatus for surface matching registration;

[0148] Fig. 5 illustrates a method for surface matching registration.

[0149] DESCRIPTION OF EMBODIMENTS

[0150] Fig. 1 is a schematical view of the image data Di and the registration data Dr. The registration data Dr relates to a surface of an object, in this case, a head of a patient. In contrast, the image data Di relate to the same object but does not only cover the surface of the object but the whole object. The image data Di preferably comprises magnetic resonance imaging, MRT, data and / or computer tomography, CT, data. The registration data Dr in this case is segmented into different sections. As such the registration data Dr does not only comprise spatial information of the surface of the object, but in particular also section information to which section of the object the respective registration data Dr refers.

[0151] The registration data Dr is provided in a registration data-coordinate system and the image data Di is provided in an image data-coordinate system. The surface matching registration of the invention has the goal to map the registration data Dr onto the surface of the object in accordance with the image data Di.

[0152] Fig. 2 is a schematical view of acquiring the registration data Dr. A user U, which usually is a medical expert, uses a registration device 20 to acquire the registration data Dr from the surface of the object 0. The registration device 20 in this is noncontact based and allows to scan the surface of the object 0 and provide registration data Dr in form of a point cloud. The registration data Dr comprises a plurality of registered points P, which comprise spatial information of each of the registered points P within a space. In other words, registration data Dr is acquired in a registration data coordinate system.

[0153] As can be seen from fig. 2, the plurality of registered points P form a point cloud in form of the surface of the object 0. Thus, the plurality of registered points P form a three- dimensional point representation of the surface of the object.

[0154] Fig. 3a illustrates an identification of the optimal matching solution MSo based on a matching distance D only. In this example, a plurality of matching solutions MS (first matching solution SO to ninth matching solution S8) that have been determined by performing a matching algorithm between the image data Di of the object 0 and the registration data Dr of registered points P of a surface of the object 0 have been obtained. Each of the plurality of matching solutions MS comprises the respective matching distance D. In this case, the matching distance is indicated by a root mean square, RMS, of an average matching distance D of the plurality of registered points P to the surface of the object 0 of the image data Di. From the plurality of matching solutions MS, usually the matching solution with the lowest matching distance D is considered to be an optimal matching solution MSs, based in which the surgical navigation is performed. In this case, the optimal matching solution MSs is determined to be the sixth matching solution S5 with a matching distance of 0.5 millimeter, mm. The sixth matching solution S5 has the lowest matching distance D from all of the plurality of matching solutions MS.

[0155] Fig. 3b illustrates an identification of the optimal matching solution MSo based on a landmark matching distance. Compared to the identification of the optimal matching solution MSs, in the example of Fig. 3b, the plurality of matching solutions MS are firstly filtered by a first threshold with respect to the matching solution with the lowest matching distance. The first threshold is 4mm. In other words, from the plurality of matching solutions MS, all similar matching solutions MSs with a matching distance of 4.5mm or lower is selected. The remaining matching solutions are considered non optimal. Consequently, within the similar matching solutions MSs automatically, the matching solution MS with the lowest matching distance (the sixth matching solution S5) is also present.

[0156] The proposed method uses at least one matching parameter to find the optimal matching solution MSo from the similar matching solutions MSs. The similar matching solutions MSs thus are a list of candidate matching solutions determined based on the matching distance MS.

[0157] In this example, the at least one matching parameter comprises at least one preregistration landmark. The at least one landmark defines a specific area of the object, in particular around a concise point of the object. Thus, for each of the similar matching solutions MSs a landmark distance L is determined, which is the matching distance between the registration data at least one pre-registration landmark L and the image data.

[0158] In this case, the smallest landmark matching distance has a value of 0.1 mm and is of the first matching solution SO. Thus, the first matching solution SO is identified as the optimal matching solution instead of the sixth matching solution S5. Consequently, the method allows finding a better optimal matching solution.

[0159] Fig. 3c illustrates an identification of the optimal matching MSo solution based on a an impact on the ROI. Compared to the identification of the optimal matching solution MSs, in the example of Fig. 3b, the at least one matching parameter comprises an impact on a region of interest, ROI.

[0160] In the medical workflow, it is especially relevant how good the registration is in a ROI in which the surgery is performed. Thus, not necessarily the matching solution MS with the lowest matching distance D is the most accurate on the ROI.

[0161] In this example, the ROI of the image data Di is transformed from the image data- coordinate system into the registration data-coordinate system using a transformation matrix that each of the matching solutions MS comprise. An impact of the transformation on the region of interest is determined by comparing a position and / or orientation of the region of interest in the registration-coordinate system between each of the at least one similar matching solution MSs. In this example, only the fifth matching solution S4 does not have impact on the ROI. Thus, the fifth matching solution S4 is identified as the optimal matching solution instead of the sixth matching solution S5.

[0162] Consequently, the method allows finding a better optimal matching solution.

[0163] Fig. 3d illustrates an identification of the optimal matching MSo solution based on a an impact on the ROI and a landmark matching distance L. Thus, the example of fig. 3d combines the examples of figs. 3b and 3c.

[0164] The mechanics are similar to the foregoing examples and are as such not repeated in detail again.

[0165] In this example, the first matching solution SO is discarded due to its impact on the ROI. As such, although the first matching solution SO has the lowest landmark matching distance L, the fifth matching solution S4 is identified as the optimal matching solution MSo, as it fulfiles the non-impact on the ROI requirement and has the lowest landmark matching distance L beyond all the matching solutions MS that also fulfill the nonimpact on the ROI requirement.

[0166] Consequently, the method allows finding a better optimal matching solution.

[0167] Fig. 4 illustrates an data processing apparatus 10 for surface matching registration according to the invention. The data processing apparatus 10 comprises an obtaining unit 11 , a similar matching solution determination unit 12 and an optimal matching solution identification unit 13.

[0168] The obtaining unit 11 is configured to obtaining a plurality of matching solutions MS, wherein each of the plurality of matching solutions MS is determined by performing a matching algorithm between image data Di of at least part of the object and registration data Dr of registered points P of a surface of the object 0, wherein each of the plurality of matching solutions MS comprises a matching distance between the image data and the registration data. The obtaining unit 11 provides the plurality of matching solutions MS to the similar matching solution determination unit 12.

[0169] The similar matching solution determination unit 12 is configured to determine a lowest distance matching solution, being the matching solution of the plurality of matching solutions with the lowest matching distance. The similar matching solution determination unit 12 is configured to determine at least one similar matching solution MSs from the plurality of matching solutions MS, wherein the at least one similar matching solution MSs comprises a matching distance with a difference to the lowest matching distance below a first threshold, wherein the at least one similar matching solution MSs comprises the lowest distance matching solution.

[0170] The optimal matching solution identification unit 13 is configured to identify an optimal matching solution from the at least one similar matching solution MSs based on at least one matching parameter Pm. Fig. 5 illustrates a computer-implemented medical method for surface matching registration of an object 0 in image guided navigation according to the invention. The method comprising the following steps: A first step S10 comprises obtaining a plurality of matching solutions MS, wherein each of the plurality of matching solutions MS is determined by performing a matching algorithm between image data Di of at least part of the object and registration data Dr of registered points P of a surface of the object 0. Each of the plurality of matching solutions MS comprises a matching distance D between the image data and the registration data. A second step S20 comprises determining a lowest distance matching solution, being the matching solution of the plurality of matching solutions MS with the lowest matching distance D. A third step S30 comprises determining at least one similar matching solution MSs from the plurality of matching solutions MS, wherein the at least one similar matching solution MSs comprises a matching distance with a difference to the lowest matching distance below a first threshold, wherein the at least one similar matching solution MSs comprises the lowest distance matching solution. A fourth step S40 comprises identifying S40 an optimal matching solution MSo from the at least one similar matching solution MSs based on at least one matching parameter Pm. An optional step after the third step S30 comprises determining S35 for each of the at least one similar matching solution MSs a region of interest, ROI, matching position, being the position of the ROI of the object transformed based on the respective matching solution, determining for each of the at least one similar matching solution MSs a ROI distance, being a difference between the ROI matching position of the at least one similar matching solution and the lowest distance matching solution. If the ROI distance exceeds a second threshold, the method jumps to the fourth step S40 as described. Otherwise, the method jumps to another step S45 comprising determining the lowest distance matching solution as the optimal matching solution. In other words, based on the ROI distance, which indicates an impact of the at least one matching solution on the ROI, it is determined if considering the at least one matching parameter Pm is necessary or if just the lowest distance matching solution is determined to be the optimal matching solution.

Claims

CLAIMS1 . A computer-implemented medical method for surface matching registration of an object (0) in image guided navigation, the method comprising the following steps: obtaining (S10) a plurality of matching solutions (MS), wherein each of the plurality of matching solutions (MS) is determined by performing a matching algorithm between image data (Di) of at least part of the object and registration data (Dr) of registered points (P) of a surface of the object (0); wherein each of the plurality of matching solutions (MS) comprises a matching distance (D) between the image data and the registration data; determining (S20) a lowest distance matching solution, being the matching solution of the plurality of matching solutions (MS) with the lowest matching distance (D); determining (S30) at least one similar matching solution (MSs) from the plurality of matching solutions (MS), wherein the at least one similar matching solution (MSs) comprises a matching distance with a difference to the lowest matching distance below a first threshold, wherein the at least one similar matching solution (MSs) comprises the lowest distance matching solution; identifying (S40) an optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on at least one matching parameter (Pm).

2. The method of claim 1 , wherein each of the plurality of matching solutions (MS) comprises a transformation matrix between an image data-coordinate system of the image data (Di) to a registration data-coordinate system of the registration data (Dr).

3. The method of any one of the preceding claims, wherein the matching distance (D) between the image data and the registration data (Dr) comprises an average distance of distances of each of theregistered points (P) to the surface of the object (0) according to the image data (Di).

4. The method of claim 3, wherein the average distance comprises a root mean square, RMS, of the distances of each of the registered points (P) and the surface of the object (0) according to the image data (Di).

5. The method of any one of the preceding claims, comprising: determining (S35) for each of the at least one similar matching solution (MSs) a region of interest, ROI, matching position, being the position of the ROI of the object transformed based on the respective matching solution; determining for each of the at least one similar matching solution (MSs) a ROI distance, being a difference between the ROI matching position of the at least one similar matching solution and the lowest distance matching solution; if the ROI distance exceeds a second threshold, identifying (S40) an optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on at least one matching parameter (Pm); otherwise, determining (S45) the lowest distance matching solution as the optimal matching solution (MSo).

6. The method of any one of the preceding claims, wherein the at least one matching parameter (Pm) comprises at least one pre-registration landmark.

7. The method of claim 6, wherein identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the at least one matching parameter (Pm) comprises: determining a landmark matching distance (L) for each of the similar matching solutions (MSs), wherein the landmark matching distance (L) is the matching distance (D) between the registration data (Dr) at least one preregistration landmark and the image data (Di);identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the landmark matching distance (L).

8. The method of any one of claims 6 or 7, wherein the at least one pre-registration landmark (L1 , L2) comprises a nasion, a nasal spine, a cantus lateralis left, a cantus lateralis right and / or inion.

9. The method of any one of the preceding claims, wherein the at least one matching parameter (Pm) comprises a distance distribution of each of the plurality of registration points (P).

10. The method of claim 9, wherein identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the at least one matching parameter (Pm) comprises: determining the distance distribution of the distances of each of the plurality of registration points (P) and the image data (Di) for each of the similar matching solutions (MSs); discarding outliers of the registration points (P) based on the distance distribution for each of the similar matching solutions (MSs); determining the similar matching solution (MSs) with the lowest matching distance after discarding the outliers as the optimal matching solution (MSo).11 . The method of claim 9, wherein identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the at least one matching parameter (Pm) comprises: determining the distance distribution of the distances of each of the plurality of registration points (P) and the image data (Di) for each of the similar matching solutions (MSs); identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on distance distribution and an expected distance distribution.

12. The method of any one of the preceding claims, wherein the at least one matching parameter (Pm) comprises additional registration points (Pa) of the surface of the object (0).

13. The method of claim 12, wherein identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the at least one matching parameter (Pm) comprises: providing, on a display device, a visual representation of the performed matching algorithm to the user (U); guiding the user to add the additional registration points (Pa); determining an additional matching distance for each of the similar matching solutions (MSs), wherein the additional matching distance is the matching distance between the additional registration points and the image data (Di); identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the additional matching distance.

14. The method of any one of the preceding claims, wherein the at least one matching parameter (Pm) comprises a user verification.

15. The method of claim 14, wherein identifying (S40) the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the at least one matching parameter (Pm) comprises: determining a verification region, comprising at least part of the surface of the object; providing, on a display device, the region for each of the at least one similar matching solution (MSs) to a user for verification; receiving the user verification from the user; identifying the optimal matching solution (MSo) from the at least one similar matching solution (MSs) based on the user verification.

16. The method of claim 15, wherein the verification region is a region, in which a matching difference between different similar matching solutions (MSs) exceed a predetermined threshold; or in which the matching distance (D) of one of the at least one similar matching solutions (MSs) exceeds a predetermined threshold; wherein the matching difference is a difference between a surface matching position of the different similar matching solutions; wherein the surface matching position, being the position of the surface of the object transformed based on the respective matching solution.

17. The method of any one of the preceding claims, wherein the amount of the plurality of matching solutions (MS) is predetermined.

18. The method of any one of the preceding claims, wherein the matching algorithm comprises an iterative closest point, ICP, algorithm.

19. The method of claim 18, wherein the ICP algorithm determines the matching distance (D) by iteratively reducing a distance between the registration data (Dr) and the image data (Di) until a minimal matching distance is identified.

20. The method of any one the claims 18 or 19, wherein each matching solution (MS) comprises a starting position of the registration data (Dr), wherein the starting position comprises a location and an orientation of the registration data (Dr) in space.21 . Method of any one of the preceding claims, wherein the image data (Di) comprises CT and / or MRT data.

22. Method of any one of the preceding claims,wherein the registration data (Dr) is acquired by a registration device (20).

23. A data processing apparatus comprising means for carrying out the method of any of the claims 1 -22.

24. A computer program which, when running on a computer or when loaded onto a computer, causes the computer to perform the method steps of the method according to any of the claims 1 -22.

25. A computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method of any one of the claims 1 -22.

26. A surgical navigation system for computer assisted surgery, the system comprising a data processing apparatus of claim 23.

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