Method and system for surface matching registration of a patient in image guided navigation

Abstract

A computer-implemented medical method for surface matching registration of a patient (O) in image guided navigation comprises the steps of obtaining (81) registration data (Dr) of a surface of a patient (O), wherein the registration data (Dr) comprises a plurality of registered points (P), determining (S2), for each of the plurality of points (P) a matching weight (W) based on at least one quality criteria value (Q1, Q2, Q3), wherein the matching weight (W) reflects a quality of the respective point (P) and performing (S3) a matching algorithm between image data (Di) of at least part of the patient (O) and the registration data (Dr), using the matching weight (W) of each of the plurality of points (P), thereby determining a matching solution (Ms). Thus, for each point a quality can be determined that is considered in the matching algorithm. In other words, the matching weight (W) leads to a more accurate intraoperatively acquired point cloud and consequently to an improved matching accuracy.

Description

[0001] Brainlab AG

[0002] Attorney’s File: B18879WO

[0003] METHOD AND SYSTEM FOR SURFACE MATCHING REGISTRATION OF A PATIENT IN IMAGE GUIDED NAVIGATION

[0004] FIELD OF THE INVENTION

[0005] The present invention relates to a computer-implemented method for surface matching registration of a patient in image guided navigation, a corresponding data processing apparatus, a patient registration system, a surgical navigation system, computer program and a computer-readable medium.

[0006] TECHNICAL BACKGROUND

[0007] Before performing surgery that uses image guided navigation, a patient has to be registered. Registering the patient comprises a surface matching registration where an intraoperatively acquired point cloud (in cranial use case on the head) is matched with the surface of a pre-operative image set e.g., CT or MRI.

[0008] The present invention has the object of providing a method for surface matching registration with an improved matching accuracy.

[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. EXEMPLARY SHORT DESCRIPTION OF THE INVENTION

[0011] 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.

[0012] The disclosed computer-implemented medical method for surface matching registration of a patient in image guided navigation determines a matching weight for each point of the intraoperatively acquired point cloud based on at least one quality criteria value. Thus, for each point a quality can be determined that is considered in the matching algorithm. In other words, the matching weight leads to a more accurate intraoperatively acquired point cloud and consequently to an improved matching accuracy.

[0013] GENERAL DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] According to an aspect of the invention, a computer-implemented medical method for surface matching registration of a patient in image guided navigation comprises the following steps: A step comprises obtaining registration data of a surface of a patient, wherein the registration data comprises a plurality of registered points. Another step comprises determining for each of the plurality of points a matching weight based on at least one quality criteria value, wherein the matching weight reflects a quality of the respective point. Another step comprises performing a matching algorithm between image data of at least part of the patient and the registration data, using the matching weight of each of the plurality of points, thereby determining a matching solution.

[0016] In the known approaches, the matching algorithm is only performed between the image data and the registration data, wherein each point of the registration data has the same weight. In other words, each point of the registration data is considered equally important or meaningful in a matching algorithm. In contrast, the provided method also considers a matching weight for each of the plurality of points. In other words, each point of the registration data has a determined importance or meaningfulness indicating a quality of the point expressed by the matching weight. Thus, the matching weight introduces an enhanced accuracy of the plurality of points and as such the registration data. Finally, a more accurate matching solution for the surface matching registration can be determined.

[0017] 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 lowest 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 “quality criteria value”, as used herein, relates to a value based on a quality criteria reflecting a quality of a point of the registration data. The quality of a point reflects an expected deviation or concordance between a point in the registration data and a corresponding point in the image data. The quality criteria value thus is a single value in a predetermined range of values, in particular between 0 and 1 , that reflects the quality of the point with respect to a predetermined criteria.

[0023] The term “matching weight”, as used herein, combines the quality criteria values. The matching weight thus is a single value in a predetermined range of values, in particular between 0 and 1 , that reflects the quality of the point with respect to all processed criteria.

[0024] Preferably, determining the matching weight based on the at the least one quality criteria value comprises (in its most simple form) determining an average of the quality criteria values (if only one quality criteria value has been processed the matching weight is the quality criteria value). In other words, each of the quality criteria values is equally important for determining the matching weight. Alternatively, for each quality criteria value a weighting factor is calculated in, which is predetermined based on the quality criteria that has lead to the quality criteria value. Thus, the quality criteria values are not equally important for determining the matching weight. Preferably, additional importance rules are implemented. For example, if one quality criteria value is considered especially important, it is determined that if said quality criteria value is smaller than a predetermined threshold, the value of the matching weight for this point is automatically set to zero. Thus, it is determined that the quality of the point is so bad that the point is not considered in the determination of the matching solution.

[0025] Preferably, the image data is acquired from a medical database.

[0026] Preferably, the plurality of registered points is also referred to as point cloud.

[0027] Known surface matching registration faces for example the following issues. Ambiguous point clouds (e.g., points acquired mainly in the dome of the head) which can be matched on several positions on the surface of the patients image set. A relative difference between the real patient surface during point acquisition and the surface of the patient image set e.g., due to skin shift. Previous solutions didn’t consider the individual quality of each acquired point. For example points acquired in changed areas (e.g. due to skin shift) were equally considered as points in distinctive and rigid areas (e.g. bridge of the nose). Therefore, it is likely that a less then optimal registration match is calculated by the ICP algorithm.

[0028] The described method however gives each point of the registration data a specific weighting, referred to as matching weight, corresponding to its quality. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0029] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0030] In a preferred embodiment, the at least one quality criteria value reflects a distribution of the plurality of points. Preferably, the quality criteria value reflecting the distribution of the plurality of points of the registration data is independent of where the point was obtained on the patient.

[0031] Thus, the quality criteria value reflecting the distribution of the plurality of points is a single value in a predetermined range of values, in particular between 0 and 1 , that is determined on at least one distribution property of the point. The at least one distribution property preferably comprises a point cluster, an extend and a shape.

[0032] For each distribution property, a distribution property value is determined based on a predetermined association. In other words, the distribution property of a point is determined and based on the determined distribution property a distribution property value is determined. All of the distribution property values for the point are then combined to the quality criteria value reflecting the distribution of the plurality of points. This can be done the same way as the combination of quality criteria values to the matching weight.

[0033] Preferably, the distribution property point cluster reflects if a point is part of a point cluster. Further preferably, the distribution property value for the distribution property point cluster is lower for points that are part of a point cluster. Point clusters usually have too many points that do not provide unique information about the surface of the patient and as such are considered to be of lower quality.

[0034] Preferably, the distribution property extend reflects if a point extends the point cloud. Further preferably, the distribution property value for the distribution property extend is higher for points that extend the point cloud. Points extending the point cloud are considered higher quality as they are more likely to provide unique information about the surface of the patient.

[0035] Preferably, the distribution property shape reflects the shape of the point cloud. Further preferably, the distribution property value for the distribution property shape is higher for points that add uniqueness to the point cloud. Points adding uniqueness to the point cloud are considered higher quality as they are more likely to provide unique information about the surface of the patient even as points just extending the point cloud.

[0036] Thus, each point of the registration data gets a specific weighting, referred to as matching weight, corresponding to its quality based on the distribution of the points of the registration data. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0037] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0038] In a preferred embodiment, the at least one quality criteria value reflects a registration device type.

[0039] Preferably, the quality criteria value reflecting the registration device type of the registration device used for obtaining the point of the registration data is independent of where the point was obtained on the patient.

[0040] There is a plurality of types of registration devices. For example, the registration device type can be a contact-based type or a contact-free type. Due to the nature of the patient not all points of the registration data of the patient can be obtained by a registration device of the same type. For example, if hair is blocking the view of some parts of the patient, instead of a contact-free registration device, a contact-based registration device has to be used.

[0041] Preferably, points acquired by a contact-free registration device are considered higher quality as points acquired by a contact-based registration device. The contact-based registration device often leads to skin shift due to the nature of making contact to the patient during the registration. Further preferably, points acquired by a contact-free registration device are considered lower quality as points acquired by a contact-based registration device if it is known that the contact-less registration device provides points with less accuracy than the contact-based registration device. For each registration device type a predetermined quality criteria value reflecting the registration device type is determined for the respectively acquired point.

[0042] Thus, each point of the registration data gets a specific weighting, referred to as matching weight, corresponding to its quality based on the type of the registration device used to obtain the points of the registration data. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0043] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0044] In a preferred embodiment, the at least one quality criteria value reflects a location data of the respective point.

[0045] Preferably, the quality criteria value reflecting the location data of the plurality of points of the registration data is dependent of where the point was obtained on the patient.

[0046] There is a plurality of factors that have impact on the quality of a point of the registration data that is based on the location of the point on the surface of the patient. For example, it is determined that points of a specific region have a lower or higher quality and as such lead to a lower or higher quality criteria reflecting the location data. For example, points acquired from the bridge of the nose are considered to have a higher quality than the dome of the head.

[0047] Thus, each point of the registration data gets a specific weighting, referred to as matching weight, corresponding to its quality based on the location data of the points of the registration data. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0048] Thus, a method for surface matching registration with an improved matching accuracy is provided. In a preferred embodiment, the location data comprises difference data, wherein the difference data reflect a difference between the registered patient and the image data of the patient.

[0049] Due to several factors, it can be known that there are differences between the registered patient and the image data of the patient, in particular in specific regions of the patient. Consequently, a matching based on those registration data and image data inherently introduces errors, which reflect in a lower accuracy. Thus, points of the registration data that are located in those regions of the patient, in particular the surface of the patient, are considered of a lower quality. This is reflected in the difference data.

[0050] Thus, each point of the registration data gets a specific weighting, referred to as matching weight, corresponding to its quality based on the location data comprising difference data of the points of the registration data. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0051] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0052] In a preferred embodiment, the difference data comprise patient orientation data.

[0053] The patient orientation data comprises relative change of a position and / or orientation of the patient in the operation room setup, where the registration data is obtained, and compared to the image data.

[0054] There are regions of the surface of the patient that are prone to skin shift due to gravity. If those regions are the same in the image data and the registration data, no reduces accuracy of the matching is induced. Due to the change of position and / or orientation of the patient in either the image data or the patient in the operation room setup, where the registration data is obtained, different locations of the image data compared to the registration data have been affected by skin shift, leading to an inherent error in the matching algorithm, reducing the accuracy of the matching. Consequently, the difference data comprising patient orientation data indicate location data or in other words a region with a lower quality that is then reflected in the quality criteria value.

[0055] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0056] In a preferred embodiment, the difference data comprise changes to the patient in the image data of the patient.

[0057] Preferably, the changes to the patient in the image data of the patient are initiated by imprints of headphones or pillows and / or cut-offs in the image data.

[0058] In other words, the changes to the patient in the image data of the patent are not reflected by the registration data, as the reasons for those changes are not present when obtaining the registration data. This inherently leads to an error in the matching algorithm, reducing the accuracy of the matching.

[0059] Consequently, the difference data comprising changes to the patient in the image data of the patient indicate location data or in other words a region with a lower quality that is then reflected in the quality criteria value.

[0060] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0061] In a preferred embodiment, the difference data comprise changes to the registered patient.

[0062] Preferably, the changes to the registered patient are initiated by tapes, tubes and / or pins of head clamps.

[0063] In other words, the changes to the registered patient are reflected by the registration data but not the image data, as the reasons for those changes are not present when obtaining the image data. This inherently leads to an error in the matching algorithm, reducing the accuracy of the matching.

[0064] Consequently, the difference data comprising changes to the registered patient indicate location data or in other words a region with a lower quality that is then reflected in the quality criteria value.

[0065] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0066] In a preferred embodiment, the location data comprises a distance between the surface of the patient and an underlying bone of the patient to the respective point.

[0067] In other words, the closer the point on the surface is to an underlying bone, the higher the quality of the point is considered. Thus, each point of the registration data gets a specific weighting, referred to as matching weight, corresponding to its quality based on the location data of the points of the registration data, wherein the location data comprises a distance between the surface of the patient and an underlying bone of the patient to the respective point. This enhances the accuracy of the registration data, which is used for the surface matching registration.

[0068] Preferably, the quality criteria value gradually increases with a decrease of the distance between the surface of the patient and an underlying bone of the patient to the respective point.

[0069] Thus, a method for surface matching registration with an improved matching accuracy is provided.

[0070] In a preferred embodiment, the location data is determined by user input.

[0071] Preferably, the user-input comprises a guided registration data acquisition by the user, for example first nose then forehead.

[0072] In a preferred embodiment, the location data is determined by performing a prematching algorithm. Preferably, the pre-matching comprises a matching algorithm based on the registration data and thus without the weighting of the registered points.

[0073] In a preferred embodiment, the location data is determined by performing a landmark matching algorithm.

[0074] Preferably, the landmarks comprise a nasion, a nasal spine, a cantus lateralis left, a cantus lateralis right and / or inion.

[0075] In a preferred embodiment, the location data is determined by an analysis of live video data of the patient.

[0076] In a preferred embodiment, the matching algorithm comprises determining a matching distance between the image data and the registration data.

[0077] Preferably, the matching distance comprises an average distance of distances of each of the registered points to the surface of the patient according to the image data. Average distance comprises a root mean square, RMS, of the distances of each of the registered points and the surface of the patient according to the image data.

[0078] In a preferred embodiment, the matching algorithm comprises an iterative closest point, ICP, algorithm.

[0079] In a preferred embodiment, the ICP algorithm determines a matching distance by iteratively reducing a distance between the registration data and the image data until a minimal matching distance is identified.

[0080] In a preferred embodiment, the image data comprises CT and / or MRI data.

[0081] In a preferred embodiment, wherein the registration data is determined by a registration device. According to another aspect of the invention a data processing apparatus comprises 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.

[0083] According to another aspect of the invention, a patient registration system comprises a registration device, configured to provide registration data of a patient, and the data processing apparatus, as described herein.

[0084] According to another aspect of the invention, a surgical navigation system for computer assisted surgery is provided, the system comprising a patient registration system. As described herein.

[0085] Preferably, the surgical navigation system is also referred to as image guided navigation system.

[0086] According to another aspect of the invention, a computer program is provided, 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.

[0087] 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.

[0088] According to another aspect of the invention, a computer-readable medium is provided, comprising instructions which, when executed by a computer, cause the computer to carry out the method, as described herein.

[0089] 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, in particular a patient registration 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.

[0090] The present invention also relates to the use of the surgical navigation system in computer assisted surgery.

[0091] DEFINITIONS In this section, definitions for specific terminology used in this disclosure are offered which also form part of the present disclosure.

[0092] Computer implemented method

[0093] 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.

[0094] 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. 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.

[0095] 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.

[0096] Acquiring data

[0097] 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.

[0098] Registering 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 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.

[0104] 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.

[0105] 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.

[0106] Marker holder

[0107] 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. Pointer

[0108] 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.

[0109] Reference star

[0110] 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.

[0111] Navigation system

[0112] 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.

[0113] 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.

[0114] Surgical navigation system

[0115] 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.

[0116] Landmarks

[0117] 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.

[0118] Imaging geometry

[0119] 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:

[0120] 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

[0121] 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.

[0122] 3. "Fluoroscopic X-ray Image Processing and Registration for Computer-Aided Orthopedic Surgery", Ziv Yaniv

[0123] 4. EP 08 156 293.6

[0124] 5. US 61 / 054,187

[0125] Shape representatives

[0126] 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.

[0127] 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. Mapping

[0135] 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).

[0136] Medical Workflow

[0137] 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.

[0138] BRIEF DESCRIPTION OF THE DRAWINGS

[0139] 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

[0140] Fig. 1 illustrates the registration data and the image data;

[0141] Fig. 2 illustrates schematically an acquiring of the registration data;

[0142] Fig. 3 is a schematic illustration of the method for surface matching registration of a patient in image guided navigation; and

[0143] Fig. 4 is a schematic illustration of a surgical navigation system. DESCRIPTION OF EMBODIMENTS

[0144] 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, MRI, data and / or computer tomography, CT, data.

[0145] 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.

[0146] 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.

[0147] 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 O 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.

[0148] 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 O. Thus, the plurality of registered points P form a three- dimensional point representation of the surface of the object.

[0149] Fig. 3 is a schematic illustration of the method for surface matching registration of a patient in image guided navigation. In a first step S1 registration data Dr of a surface of a patient 0 are obtained, wherein the registration data Dr comprises a plurality of registered points P. In a second step S2, for each of the plurality of points P a matching weight W is determined based on at least one quality criteria value Q1 , Q2, Q3, wherein the matching weight W reflects a quality of the respective point P. In a third step S3 a matching algorithm between image data Di of at least part of the patient 0 and the registration data Dr is performed, using the matching weight W of each of the plurality of points P, thereby determining a matching solution Ms.

[0150] Fig. 4 is a schematic illustration of a surgical navigation system 1000 comprising a patient registration system 100. The patient registration system 100 comprises a matching weight determination unit 110 and a processing unit 120.

[0151] The matching weight determination unit 110 is configured to receive the registration data Dr as well as a first quality criteria value Q1 , a second quality criteria value Q2 and a third quality criteria value Q3. The matching weight determination unit 110 is further configured to determine a matching weight W for each of the points P of the registration data Dr based on the first quality criteria value Q1 , the second quality criteria value Q2 and the third quality criteria value Q3. Consequently, each of the first quality criteria value Q1 , the second quality criteria value Q2 and the third quality criteria value Q3 are used to determine the matching weight W, which is represented by a single value, preferably between 0 and 1 , for the respective point P of the registration data Dr.

[0152] The matching weight determination unit 110 is configured to provide the registration data Dr and the respective matching weight W to the processing unit 120.

[0153] The processing unit 120 is configured to receive image data Di of the patient. Furthermore, the processing unit 120 is configured to perform a matching algorithm between the image data Di of the patient O and the registration data Dr, using the matching weight W of each of the plurality of points P, thereby determining a matching solution Ms. Thus, for determining the matching solution Ms, each of the points P of the registration data Dr is not considered equally. The matching weight W of each of the plurality of points P allows to consider different quality factors, leading to a more accurate method for surface matching registration.

Claims

Brainlab AGAttorney’s File: B18879WOCLAIMS1. A computer-implemented medical method for surface matching registration of a patient (0) in image guided navigation, comprising: obtaining (S1 ) registration data (Dr) of a surface of a patient (0), wherein the registration data (Dr) comprises a plurality of registered points (P); determining (S2), for each of the plurality of points (P) a matching weight (W) based on at least one quality criteria value (Q1 , Q2, Q3), wherein the matching weight (W) reflects a quality of the respective point (P); performing (S3) a matching algorithm between image data (Di) of at least part of the patient (0) and the registration data (Dr), using the matching weight (W) of each of the plurality of points (P), thereby determining a matching solution (Ms).

2. The method of claim 1 , wherein the at least one quality criteria value (Q1 , Q2, Q3) reflects a distribution of the plurality of points (P).

3. The method of any one of the preceding claims, wherein the at least one quality criteria value (Q1 , Q2, Q3) reflects a registration device type.

4. The method of any one of the preceding claims, wherein the at least one quality criteria value (Q1 , Q2, Q3) reflects location data of the respective point (P).

5. The method of claim 4, wherein the location data comprises difference data, wherein the difference data reflect a difference between the registered patient (0) and the image data (Di) of the patient (0).

6. The method of claim 5, wherein the difference data comprise patient orientation data.

7. The method of any one of the claims 5 or 6, wherein the difference data comprise changes to the patient (0) in the image data (Di) of the patient (0).

8. The method of any one of the claims 5 to 7, wherein the difference data comprise changes to the registered patient(O).

9. The method of any one of the claims 4 to 8, wherein the location data comprises a distance between the surface of the patient (0) and an underlying bone of the patient (0) at the respective point(P).

10. The method of any one of the claims 4 to 9, wherein the location data is determined by user input.11 . The method of any one of the claims 4 to 10, wherein the location data is determined by performing a pre-matching algorithm.

12. The method of any of the claims 4 to 11 wherein the location data is determined by performing a landmark matching algorithm.

13. The method of any one of the claims 4 to 12, wherein the location data is determined by an analysis of live video data of the patient (0).

14. The method of any one of the preceding claims, the matching algorithm comprises determining a matching distance between the image data (Di) and the registration data (Dr).

15. The method of claim 14, wherein the matching algorithm comprises an iterative closest point, ICP, algorithm.

16. The method of claim 15, wherein the ICP algorithm determines a matching distance by iteratively reducing a distance between the registration data (Dr) and the image data (Di) until a minimal matching distance is identified.

17. Method of any one of the preceding claims, wherein the image data (Di) comprises CT and / or MRI data.

18. Method of any one of the preceding claims, wherein the registration data (Dr) is determined by a registration device (20)19. A data processing apparatus comprising means for carrying out the method of any of the claims 1 -18.

20. A patient registration system comprising a registration device (20), configured to provide registration data (Dr) of a patient (0), and the data processing apparatus of claim 19.21 . A surgical navigation system for computer assisted surgery, the system comprising a patient registration system according to claim 20.

22. 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 1-18.

23. 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 1-18.

Images