WO2023011924A1

WO2023011924A1 - 2d/3d image registration using 2d raw images of 3d scan

Info

Publication number: WO2023011924A1
Application number: PCT/EP2022/070356
Authority: WO
Inventors: Florian GLATZ; Marina BASSILIOUS
Original assignee: Brainlab Ag
Priority date: 2021-08-02
Filing date: 2022-07-20
Publication date: 2023-02-09

Abstract

The present invention aids in determining the position of a 3D image dataset of an anatomical structure in a particular reference system, such as a reference system of an imaging device, a tracking system or an operating room. The 3D image dataset is typically obtained by taking a plurality of 2D images from different viewing directions and reconstructing the 3D image dataset therefrom. The 3D image dataset typically represents a 3D array of voxels, wherein the positions of the voxels are defined in an internal reference system of the 3D image dataset. In previous approaches, in order to determine the current position of the 3D image dataset in the reference system, new 2D images are taken from known viewing directions and registration is performed using digitally reconstructed radiographs (DRRs) calculated from the 3D image dataset. The present invention does not use DRRs for comparison with the new 2D images, but rather the original 2D images used for reconstructing the 3D image dataset.

Description

2D/3D IMAGE REGISTRATION USING 2D RAW IMAGES OF 3D SCAN

FIELD OF THE INVENTION

The present invention relates to a computer-implemented method for calculating a transformation of a medical 3D image dataset of an anatomical structure in an external reference system which is external to the 3D image dataset, a method of determining a registration of a 3D image dataset with an external reference system, a method of registering a 3D image dataset of an anatomical structure with a marker device attached to the anatomical structure, a method of determining a registration of a 3D image dataset with an external reference system, a corresponding computer program, a computer-readable storage medium storing such a program and a computer executing the program.

TECHNICAL BACKGROUND

Modern medical procedures use 3D image datasets of anatomical structures, for example for planning. The 3D image dataset can also be re-used at a later point in time. For this, it is necessary to bring the 3D image dataset in alignment with the current position of the anatomical structure in space. The 3D image dataset can be the result of a 3D scan, like a Cone Beam CT scan.

The present invention can be used for procedures e.g. in connection with a system for medical imaging such as LOOP-X®, which is marketed by Brainlab AG. It can as well be used in the context of other imaging devices, such as C-arm imaging devices or imaging devices attached to a robot.

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

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.

The present invention aids in determining the position of a 3D image dataset of an anatomical structure in a particular reference system, such as a reference system of an imaging device, of a tracking system, of an operating room or of a marker device attached to the anatomical structure. The 3D image dataset is typically obtained by taking a plurality of 2D images from different viewing directions and reconstructing the 3D image dataset therefrom. The 3D image dataset typically represents a 3D array of voxels, wherein the positions of the voxels are defined in an internal reference system of the 3D image dataset. In previous approaches, in order to determine the current position of the 3D image dataset in the external reference system, new 2D images are taken from particular viewing directions and the registration in the external reference system is performed with digitally reconstructed radiographs (DRRs) calculated from the 3D image dataset. The particular viewing directions can for example be predefined or set by a user. The present invention does not use DRRs for comparison with the new 2D images, but rather the original 2D images used for reconstructing the 3D image dataset.

In this document, 2D images are also referred to as 2D projections since the images show a projection of an object into an image plane. 2D images or projections are captured or taken using a suitable medical imaging device, like an x-ray imaging system. In this case, x-ray radiation emitted by an x-ray source passes through the anatomical structure, where it is attenuated according to the internal composition of the anatomical structure, and received by an x-ray detector. However, any other suitable imaging modality can be used. GENERAL DESCRIPTION OF THE INVENTION

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.

In general, the invention reaches the aforementioned object by providing, in a first aspect, a computer-implemented medical method of calculating a transformation of a medical 3D image dataset of an anatomical structure in an external reference system which is external to the 3D image dataset. The 3D image dataset was reconstructed from a first 2D image set, which comprises a plurality of initial 2D projections taken at a first point in time from different imaging positions relative to the structure. The first 2D image set further comprises imaging settings for each of the initial 2D projections, wherein the imaging settings are those used for capturing the corresponding initial 2D projection and include the corresponding imaging position in the external reference system. The 3D image dataset thus has an initial position in the external reference system.

The method comprises executing, on at least one processor of at least one computer (for example at least one computer being part of a navigation system), the following exemplary steps which are executed by the at least one processor.

In a (for example first) exemplary step, a second 2D image set is acquired which comprises a plurality of subsequent 2D projections of the anatomical structure taken at a second point of time later than the first in time, wherein each one of the subsequent 2D projections was taken using the imaging settings of one of the initial 2D projections, thus resulting in the plurality of image pairs each comprising one initial 2D projection and one subsequent 2D projection taken with the same imaging settings.

The second 2D image set thus comprises subsequent 2D projections which correspond to corresponding initial 2D projections of the first 2D image set. The number of subsequent 2D projections in the second 2D image set is equal to or smaller than the number of initial 2D projections in the first 2D image set. Preferably, the second 2D image set comprises one subsequent 2D projection at most or each one of the initial 2D projections of the first 2D image set. Typically, the first 2D image set does not comprise two or more initial 2D projections having identical imaging settings, such that there are no two subsequent 2D projections in the second 2D image set having the same imaging settings.

In other words, this first exemplary step re-captures some or all of the initial 2D projections at the second point in time. So if the anatomical structure has, between the first and second points in time, not moved within the external reference system, the initial 2D projection and the subsequent 2D projection of an image pair are identical (assuming that the anatomical structure itself has not changed between the first and second points in time).

In a (for example second) exemplary step, a 2D image subset is calculated which comprises those initial 2D projections of the first 2D image set which are comprised in the image pairs. The 2D image subset is a thinned-out or reduced version of the first 2D image set which only comprises the initial 2D projections for which a corresponding subsequent 2D projection was acquired. The number of projections in the 2D subset and in the second 2D image set is therefore identical. It is understood that it is known which initial 2D projection in the 2D image subset corresponds to which subsequent 2D projection in the second 2D image set, for example by explicitly storing correspondencies or implicitly via the imaging settings.

In a (for example third) exemplary step, image matching of the 2D image subset onto the second 2D image set is performed, thereby obtaining the transformation in the external reference system.

Image matching compares two sets of 2D images to calculate a 3D relationship between the image sets. This is well-known in the field of medical image processing, for example from EP2593922 A1 (“METHOD AND SYSTEM FOR DETERMINING AN IMAGING DIRECTION AND CALIBRATION OF AN IMAGING APPARATUS”) or W012120405 A1 (“2D/3D IMAGE REGISTRATION ”).

The transformation is given in up to three translational dimensions and/or up to three rotational dimensions. It can also be understood as a relative position. The transformation can describe, for example, the position of the 3D image dataset at the second point in time relative to the initial position of the 3D image dataset at the first point in time, the position of the 3D image dataset at the second point in time relative to a previous position of the 3D image dataset, for example at an intermediate point in time between the first point in time and the second point in time, or the position of the 3D image dataset relative to a target position of the 3D image dataset.

It shall be noted that the imaging settings associated with a 2D projection can include one or more additional parameters, such as dose information when capturing the 2D projection or settings of a collimator of the medical imaging device used for capturing the 2D projection. The imaging position comprised in the imaging settings can be defined by one or more of the position of an x-ray source of the imaging device and the position of an x-ray detector of the imaging device. In one embodiment, such positions are defined in the external reference system.

An imaging position is for example defined by the position of a radiation source, such as an x-ray source, and the position of a radiation detector, such as an x-ray detector, in the external reference system. The positions of the source and the detector can be measured directly, for example using marker devices attached to the source and the detector. The positions are for example given in the reference system of a medical tracking device.

If the source and/or the detector is movable relative to the reference system of the medical imaging device, then the imaging position can be defined by the positions in the reference system of the medical imaging device. If the position of the reference system of the medical imaging device in the reference system of a medical tracking system is known, the position of the source and/or the position of the marker can be transformed into the reference system of the medical tracking system.

If the medical imaging device comprises a robotic arm which carries the source and the detector in a fixed position relative to the free end of the robotic arm, the imaging position can be defined by the states of the joints of the robotic arm.

According to the present invention, the step of image matching does not match the DRR images calculated from the 3D image dataset with the second 2D image set, but rather initial 2D projections which were used for reconstructing the medical 3D image dataset. This avoids the computational efforts required for calculating the DRR images. In addition, the anatomical structure shown in the subsequent 2D projections typically fits the initial 2D projections better than DRR projections generated from the 3D image dataset, in particular if the initial 2D projections are eccentric scans captured using a highly versatile imaging device such as Loop-X® in which the positions of the x-ray source and the x-ray detector can be adapted relative to each other and the x-ray beam emitted by the x-ray source can be shaped using a collimator.

In addition, loss of information is prevented which occurs in the reconstruction of the 3D image dataset from the initial 2D projections and again in the creation of DRR images from the 3D image dataset.

In an embodiment of the first aspect, the step of acquiring the second 2D image set involves selecting indication specific imaging settings. The indication can for example indicate a particular region of the anatomical structure. The imaging settings are selected such that this region is visible in the subsequent 2D projections, for example by selecting imaging positions in which the propagation path of the x-ray radiation passes through bony structures, in particular those bony structures corresponding to the indication. In one implementation, the selection involves using a look-up table in which imaging settings are associated with indications, such that imaging settings can be found by searching the look-up table for a particular indication.

Once the imaging settings are selected, subsequent 2D projections can be acquired, using the selected imaging settings, as the second 2D image set and corresponding initial 2D projections can be selected for the 2D image subset.

In one embodiment, the step of acquiring the second 2D image set involves selecting imaging settings based on a radiation dose applicable to the anatomical structure. In one implementation, imaging settings for which the overall radiation dose for acquiring the subsequent 2D projections is minimized are selected. In another implementation, image settings are selected which use as much of an allowed maximum radiation dose without exceeding it. This can be determined by the type of anatomical structure and the position of the anatomical structure relative to the imaging device. Based on the aforementioned information, the dose may be increased if a lot of surrounding tissue is in the path of the x-ray beam requiring a higher dose.

In one embodiment, the plurality of initial 2D projections of the first 2D image set are taken using a medical imaging device having predefined presets for the imaging settings of the initial 2D projections and the imaging settings for the second 2D image set are determined based on the predefined presets. The predefined presets can for example be indication specific. For a particular indication, a corresponding preset is determined, for example using a look-up table. The preset comprises the imaging settings to be used for imaging the anatomical structure according to the indication. The indication can for example imply a particular area of the anatomical structure to be imaged. The imaging settings of a particular preset can be adapted depending on the position of the anatomical structure relative to the external reference system, wherein this position can be measured or assumed.

In one embodiment, the step of performing image matching further provides a reliability measure representative of the reliability of the result of the image matching. In many image matching modules, such a reliability measure is used internally anyway and is provided as an output parameter.

In this embodiment, the method of the first aspect further comprises, if the reliability measure is below a predetermined threshold, the additional steps of supplementing the second 2D image set with at least one additional subsequent 2D projection, thus supplementing the image pairs with at least one additional image pair comprising one initial 2D projection and one additional subsequent 2D projection taken with the same imaging settings, supplementing the 2D subset with those initial 2D projections of the first 2D image set which are comprised in the at least one additional image pair and performing imaging matching of the supplemented 2D image subset onto the supplemented second 2D image set, thereby obtaining the transformation.

In this embodiment, the reliability measure indicates that the image matching was not successful or less accurate than desired. The image matching is therefore repeated with more image pairs by adding one or more new subsequent 2D projections to the second 2D image sets and adding a corresponding number of initial 2D projections from the first 2D image set to the 2D image subset.

In one embodiment, the step of performing image matching further provides a reliability measure representative of the reliability of the result of the image matching and the method according to the first aspect further involves, if the reliability measure is below a predetermined threshold, the additional steps of calculating a DRR image set comprising at least one DRR image from the 3D image dataset with particular image settings, supplementing the 2D image subset with the DRR image set, acquiring at least one additional subsequent 2D projection comprising at least one additional subsequent 2D projection of the structure, wherein each one of the additional subsequent 2D projections was taken using the particular imaging settings of one of the DRR images in the DRR image set, thus resulting in a plurality of additional image pairs each comprising one DRR image and one subsequent 2D projection taken with the same imaging settings; supplementing the second 2D image set with the at least one additional subsequent 2D projection; and performing image matching of the supplemented 2D image subset onto the supplemented second 2D image set, thereby obtaining the transformation.

This embodiment means a hybrid approach in which DRR images are derived from the 3D image dataset to form additional image pairs to be used for the image matching. This approach is particularly useful if the initial 2D projections alone do not allow successful matching with the subsequent 2D projections, for example due to adverse imaging directions when capturing the initial 2D projections. In this case, DRR images with suitable imaging settings can be calculated and corresponding subsequent 2D projections can be captured.

In contrast to previous matching approaches using DRR images, in which subsequent 2D projections are captured first and then DRRs are calculated for matching, the present embodiment calculates suitable DRR images and then captures corresponding subsequent 2D projections. This means that advantageous image settings can be used to obtain subsequent 2D projections which are suitable for the image matching. In one embodiment, in a first iteration of the method, only a single first subsequent 2D projection is acquired, only a single initial 2D projection having the same imaging settings as the single first subsequent 2D projection is acquired and preliminary image matching of the single initial 2D projection onto the first subsequent 2D projection is performed, thus obtaining a preliminary transformation. Then, in a second iteration, the imaging settings for the other subsequent 2D projections are selected based on the preliminary transformation. In this embodiment, the preliminary transformation is an approximation of the transformation and capturing the other subsequent 2D projections is adapted to the preliminary transformation.

In one example, only a single new subsequent 2D projection is acquired in the second iteration, and the preliminary transformation is updated based on image matching of the single new subsequent 2D projection and the corresponding initial 2D projection. The updated preliminary transformation is then used as the preliminary transformation in a new instance of the second iteration, and so on until the transformation becomes stable. This means that the transformation does no longer change between two subsequent instances of the second iteration. The transformation can be understood as not changing if it stays exactly equal, changes less than a predetermined absolute threshold or less than a predetermined relative threshold relative to the transformation.

In one implementation, in the second iteration, an offset depending on the preliminary transformation is added to the imaging positions in the imaging settings of the initial 2D projections when used for acquiring the other subsequent 2D projections. This means that the other subsequent 2D projections are not taken using the same imaging settings as those of the initial 2D projections, but with an offset which is adapted to the preliminary transformation. If the preliminary transformation for example indicates that the anatomical structure has performed a translational movement in the external reference system between the first and second point in time, this movement is added to the offset to the imaging settings to compensate for this movement. The same applies for a rotational rather than a translational movement or a combination thereof.

As an option, the single first subsequent 2D projection is re-captured using imaging settings in which the offset is added to the imaging position. The offset can be understood as a virtual movement of the 3D image dataset in the external reference system compared to its initial position. In this case, the offset should be added to the result of the imaging matching in order to obtain the transformation.

In one implementation, an offset is calculated from the preliminary transformation and, in the second iteration, each of the plurality of image pairs comprises one subsequent 2D projection taken with particular imaging settings and one initial 2D projection having imaging settings with an imaging position differing from the imaging position in the particular imaging settings of the corresponding subsequent 2D projection by the offset. In this implementation, the subsequent 2D projections are captured and acquired and the corresponding initial 2D projections are searched based on the offset added to or subtracted from the imaging positions of the subsequent 2D projections. In other words, initial 2D projections which are assumed to be most similar to the subsequent 2D projections are determined.

In most cases, there will be no initial 2D projections having imaging settings with an imaging position differing from the imaging position in the imaging settings of a subsequent 2D projection by exactly the offset. In this case, an initial 2D projection having the smallest difference in its imaging position from the imaging position of a subsequent 2D projection amended by the offset is selected for the image pair, and the difference is considered in the image matching. It shall be noted that the differences can be different for each image pair.

In one embodiment, the external reference system is a reference system of a medical tracking system or of a medical imaging device used for taking the plurality of subsequent 2D projections. However, it could be any other external reference system, such as a global reference system, the reference system of an operating theater or the reference system of a marker device, in particular a marker device attached to the anatomical structure.

In a second aspect, the invention is directed to a computer-implemented medical method of determining a registration of a 3D image dataset with an external reference system, comprising the step of applying the transformation obtained according to the method of the first aspect to an initial registration of the 3D image dataset with the external reference system. The initial registration can be the initial position of the 3D image dataset in the external reference system.

In the second aspect, the transformation defines the position of the 3D image dataset relative to the initial position in the external reference system. If this transformation is applied to the initial registration or position of the 3D image dataset, the current position of the 3D image dataset in the external reference system is obtained.

In a third aspect, the invention is directed to a computer-implemented medical method of registering a 3D image dataset of an anatomical structure with a marker device attached to the anatomical structure. The method comprises the steps of determining a registration of the 3D image dataset in an external reference system as in the second aspect, measuring the position of the marker device in the external reference system and calculating the position of the 3D image dataset relative to the marker device from the registration of the 3D image dataset in the external reference system and the position of the marker device in the external reference system, thus obtaining the registration of the 3D image dataset with the marker device.

In this embodiment, the spatial relationship between a marker device attached to an anatomical structure and the 3D image dataset of the anatomical structure is established. It is then possible to track the position of the marker device in the external reference system, for example using a medical tracking or navigation system, and therefore tracking the position of the 3D image dataset.

In a fourth aspect, the invention is directed to a computer program comprising instructions which, when the program is executed by at least one computer, causes the at least one computer to carry out the method according to the any one or more of the first, second, third or fourth 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, such as an electromagnetic carrier 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 any one or more of the first, second, third or fourth aspect. The signal wave is in one example a data carrier signal carrying the aforementioned computer program. 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, for example as the electromagnetic carrier 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, mobile network, for example the internet. For example, the signal, for example the signal wave, is constituted to be transmitted by optic or acoustic data transmission. The invention according to the fifth aspect therefore may alternatively or additionally relate to a data stream representative of the aforementioned program, i.e. comprising the program.

In a fifth aspect, the invention is directed to a computer-readable storage medium on which the program according to the fifth aspect is stored. The program storage medium is for example non-transitory.

In a sixth aspect, the invention is directed to at least one computer (for example, a computer), comprising at least one processor (for example, a processor), wherein the program according to the fifth aspect is executed by the processor, or wherein the at least one computer comprises the computer-readable storage medium according to the sixth aspect.

In an seventh aspect, the invention is directed to a medical system, comprising: a) the at least one computer according to the seventh aspect; b) a medical imaging device for capturing 2D projections of an anatomical structure; and c) at least one electronic data storage device storing at least the first 2D image set and the second 2D image set, wherein the at least one computer is operably coupled to

- the at least one electronic data storage device for acquiring, from the at least one data storage device, at least the first 2D image set and the second 2D image setdata, and

- an output device to output the transformation or an indication based on the transformation, for example as an optical, acoustical or tactile event. 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 attaching a marker device to an anatomical structure. 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 processing 2D projections depicting an anatomical structure. 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.

DEFINITIONS

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

Computer implemented method

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.

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-semiconductor 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 computer includes a server resource. 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 computer program comprising instructions which, when on the program is executed by a computer, cause the computer to carry out the method or methods, for example, the steps of the method or methods, described herein and/or to a computer-readable storage medium (for example, a non-transitory computer- readable storage medium) on which the program is stored 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, such as an electromagnetic carrier 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. The signal wave is in one example a data carrier signal carrying the aforementioned computer program. The invention also relates to a computer comprising at least one processor and/or the aforementioned computer-readable storage medium and for example a memory, wherein the program is executed by the processor.

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.

Acquiring data

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. A step of “determining” as described herein for example comprises or consists of issuing a command to perform the determination described herein. For example, the step comprises or consists of issuing a command to cause a computer, for example a remote computer, for example a remote server, for example in the cloud, to perform the determination. Alternatively or additionally, a step of “determination” as described herein for example comprises or consists of receiving the data resulting from the determination described herein, for example receiving the resulting data from the remote computer, for example from that remote computer which has been caused to perform the determination. 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.

Marker

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.

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.

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.

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.

Reference star

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.

Surgical navigation system

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.

Imaging methods

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.

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.

Elastic fusion, image fusion/morphing, rigid

Image fusion can be elastic image fusion or rigid image fusion. In the case of rigid image fusion, the relative position between the pixels of a 2D image and/or voxels of a 3D image is fixed, while in the case of elastic image fusion, the relative positions are allowed to change.

In this application, the term "image morphing" is also used as an alternative to the term "elastic image fusion", but with the same meaning.

Elastic fusion transformations (for example, elastic image fusion transformations) are for example designed to enable a seamless transition from one dataset (for example a first dataset such as for example a first image) to another dataset (for example a second dataset such as for example a second image). The transformation is for example designed such that one of the first and second datasets (images) is deformed, for example in such a way that corresponding structures (for example, corresponding image elements) are arranged at the same position as in the other of the first and second images. The deformed (transformed) image which is transformed from one of the first and second images is for example as similar as possible to the other of the first and second images. Preferably, (numerical) optimisation algorithms are applied in order to find the transformation which results in an optimum degree of similarity. The degree of similarity is preferably measured by way of a measure of similarity (also referred to in the following as a "similarity measure"). The parameters of the optimisation algorithm are for example vectors of a deformation field. These vectors are determined by the optimisation algorithm in such a way as to result in an optimum degree of similarity. Thus, the optimum degree of similarity represents a condition, for example a constraint, for the optimisation algorithm. The bases of the vectors lie for example at voxel positions of one of the first and second images which is to be transformed, and the tips of the vectors lie at the corresponding voxel positions in the transformed image. A plurality of these vectors is preferably provided, for instance more than twenty or a hundred or a thousand or ten thousand, etc. Preferably, there are (other) constraints on the transformation (deformation), for example in order to avoid pathological deformations (for instance, all the voxels being shifted to the same position by the transformation). These constraints include for example the constraint that the transformation is regular, which for example means that a Jacobian determinant calculated from a matrix of the deformation field (for example, the vector field) is larger than zero, and also the constraint that the transformed (deformed) image is not self-intersecting and for example that the transformed (deformed) image does not comprise faults and/or ruptures. The constraints include for example the constraint that if a regular grid is transformed simultaneously with the image and in a corresponding manner, the grid is not allowed to interfold at any of its locations. The optimising problem is for example solved iteratively, for example by means of an optimisation algorithm which is for example a first-order optimisation algorithm, such as a gradient descent algorithm. Other examples of optimisation algorithms include optimisation algorithms which do not use derivations, such as the downhill simplex algorithm, or algorithms which use higher-order derivatives such as Newton-like algorithms. The optimisation algorithm preferably performs a local optimisation. If there is a plurality of local optima, global algorithms such as simulated annealing or generic algorithms can be used. In the case of linear optimisation problems, the simplex method can for instance be used. In the steps of the optimisation algorithms, the voxels are for example shifted by a magnitude in a direction such that the degree of similarity is increased. This magnitude is preferably less than a predefined limit, for instance less than one tenth or one hundredth or one thousandth of the diameter of the image, and for example about equal to or less than the distance between neighbouring voxels. Large deformations can be implemented, for example due to a high number of (iteration) steps.

The determined elastic fusion transformation can for example be used to determine a degree of similarity (or similarity measure, see above) between the first and second datasets (first and second images). To this end, the deviation between the elastic fusion transformation and an identity transformation is determined. The degree of deviation can for instance be calculated by determining the difference between the determinant of the elastic fusion transformation and the identity transformation. The higher the deviation, the lower the similarity, hence the degree of deviation can be used to determine a measure of similarity.

A measure of similarity can for example be determined on the basis of a determined correlation between the first and second datasets.

Fixed (relative) position

A fixed position, which is also referred to as fixed relative position, in this document means that two objects which are in a fixed position have a relative position which does not change unless this change is explicitly and intentionally initiated. A fixed position is in particular given if a force or torque above a predetermined threshold has to be applied in order to change the position. This threshold might be 10 N or 10 Nm. In particular, the position of a sensor device remains fixed relative to a target while the target is registered or two targets are moved relative to each other. A fixed position can for example be achieved by rigidly attaching one object to another. The spatial location, which is a part of the position, can in particular be described just by a distance (between two objects) or just by the direction of a vector (which links two objects). The alignment, which is another part of the position, can in particular be described by just the relative angle of orientation (between the two objects). BRIEF DESCRIPTION OF THE DRAWINGS

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

Fig. 1 illustrates a medical system for implementing the invention;

Fig. 2 is a schematic illustration of the system according to the fifth aspect;

Fig. 3 shows an embodiment of the present invention, specifically the method according to the first aspect; and

Fig. 4 shows correspondences in the first and second 2D image sets.

DESCRIPTION OF EMBODIMENTS

Figure 1 shows an exemplary embodiment of a medical system for implementing the present invention. Some elements shown in Figure 1 are not essential for the invention as defined by the claims and are therefore optional.

The system of Figure 1 comprises a medical imaging device 1 , a patient table 7, a medical tracking system 9, two monitors 6 and a computer 11 (not shown in Figure 1 ).

The medical imaging device 1 is an x-ray imaging system, such as the Loop-X® marketed by Brainlab AG. It comprises a ring-shaped gantry 2 attached to a base B. The gantry 2 is tiltable relative to the base B about an axis parallel to the floor.

The gantry 2 carries an x-ray source 3 and an x-ray detector 4 which are independently movable along the gantry 2. The x-ray source 3 can comprise a collimator for shaping the x-ray beam emitted by the x-ray source 3. The positions of the x-ray source 3 and of the x-ray detector relative to the gantry 2 are measured, for example using encoders, or determined from a control signal for moving the the x-ray source 3 and/or the x-ray detector 4 along the gantry 2. A marker device in terms of reflective patches 5 is attached to the gantry 2 such that they have a fixed position relative to the gantry 2. This relative position, and thus the registration between the marker device and the gantry 2, is known. The tilting angle of the gantry 2 relative to the base B can be measured, for example using encoders, or determined from a control signal for tilting the gantry 2.

The system further comprises a patient couch 7 for accommodating a medical structure S, which can be a human patient. Attached to the medical structure S is a marker device in terms of a reference star 8. The patient couch 7 can partially protrude into the opening of the gantry 2, such that x-ray radiation emitted by the x-ray source 3 propagates through the anatomical structure S and hits the x-ray detector 4, which generates a 2D projection.

The monitors 6 are connected to the computer 11 described later.

The medical tracking system also referred to as medical navigation system 9, is configured to track marker devices, such as the set of marker patches 5 and the reference star 8, respectively. In the present example, the medical tracking system 9 is a stereoscopic optical tracking system which emits infrared light and captures reflected infrared light using a stereoscopic camera. The position of a marker device like the set of marker patches 5 and the reference star 8 can thus be determined relative to the medical tracking system 9, and therefore in a reference system of the medical tracking system 9.

In this document, a reference system is a Cartesian co-ordinate system with mutually orthogonal axes. The reference system has a location of its origin and an orientation.

Figure 2 is a schematic illustration of the medical system 10 according to the eighth aspect. The system is in its entirety identified by reference sign 10 and comprises the computer 11 , an electronic data storage device (such as a hard disc) 14 for storing at least the first and second 2D image sets and the imaging device 1. The components of the medical system 10 have the functionalities and properties explained above with regard to the fifth aspect of this disclosure. The computer 11 comprises central processing unit 12, the electronic data storage device 14 and the interface 13 for connecting the computer to external devices, such as the medical imaging device 1 , the tracking system 9, the monitors 6 and an input device 15.

A monitor 6 is an example of any suitable display device. A monitor 6 and the input device 15 can be combined into a single unit, such as a touchscreen. The input device 15 can be any suitable device such as a keyboard, a mouse, a touch sensitive surface or the like.

The electronic data storage device 14 stores instructions which implement the method of the present invention, data to be processed by the central processing unit 12 according to the present invention and data processed by the central processing unit 12 according to the present invention.

Figure 3 shows an exemplary flow diagram of a method according to the present invention.

At step S01 , a first 2D image set is acquired. The first 2D image set comprises a plurality of initial 2D projections captured, at a first point in time, using the medical imaging device 1 or another medical imaging device using the same imaging modality. Associated with each of the initial 2D projections are imaging settings used when capturing the respective initial 2D projection. In the present example, imaging settings comprise the tilt angle of the gantry 2 relative to the base B, the position of the x-ray source 3 relative to the gantry 2, the position of the x-ray detector 4 relative to the gantry 2 and settings of the collimator of the x-ray source 3. The imaging settings can optionally comprise additional parameters, such as x-ray acquisition parameters like tube current, voltage, pulse width and pulse length. Other optional parameters of the imaging settings are the tilt angle of the gantry 2 relative to the base B and the position of the base B of the medical imaging device 1 in the reference system of the medical tracking system 9. The first 2D image set can be acquired from the electronic data storage device 14, the medical imaging device 1 or any other device storing the first 2D image set.

The initial 2D projections of the first 2D image set are used to reconstruct a medical 3D image dataset of the anatomical structure S, but this reconstruction is not an essential aspect of the present invention.

The imaging position is defined in the reference system of the medical imaging device 1 by the tilt angle of the gantry 2 relative to the base B, the positions of the x-ray source 3 and the x-ray detector 4 relative to the gantry 2 and optionally the settings of the collimator in the x-ray source 3. The imaging position is defined in the reference system of the medical tracking system 9 by the position of the base B in the reference system of the medical tracking system 9, the tilt angle of the gantry 2 relative to the base B, the positions of the x-ray source 3 and the x-ray detector 4 relative to the gantry 2 and optionally the settings of the collimator in the x-ray source 3.

If the imaging positions when capturing the initial 2D projections in the reference system of the medical imaging device 1 or of the medical tracking system 9, which are examples of an external reference system, are known, the reconstructed medical 3D image dataset has an initial position in the external reference system.

Figure 4 shows an example of a first 2D image set comprising five initial 2D projections IP1 , IP2, IP3, IP4 and IP5. The initial 2D projection IP1 has associated imaging settings IS1 , the initial 2D projection IP2 has associated imaging settings IS2, the initial 2D projection IP3 has associated imaging settings IS3, the initial 2D projection IP4 has associated imaging settings IS4, and the initial 2D projection IP5 has associated imaging settings IS5.

Returning to Figure 3, step S02 involves determining imaging settings for subsequent 2D projections. In the example of Figure 4, the imaging settings IS1 , IS3 and IS4 are selected.

The imaging settings are for example selected based on a table which associates a medical indication with imaging settings for the subsequent 2D projections. This table can further associate the medical indication with imaging settings for capturing the initial 2D projections

Step S03 involves acquiring a second 2D image set comprising a plurality of subsequent 2D projections captured at a second point in time later than the first point in time.

In the example of Figure 4, the second 2D image set comprises subsequent 2D projection SP1 captured using the imaging settings IS1 , subsequent 2D projection SP2 captured using the imaging settings IS3 and subsequent 2D projection SP3 captured using imaging settings IS4. In the present example, acquiring the second 2D image set involves instructing the medical imaging device 1 to capture the subsequent 2D projections using the imaging settings selected in step S02.

Step S04 involves measuring the position of the reference star 8 in the reference system of the medical tracking system 9. This step might involve instructing the medical tracking systems 9 to measure the position of the reference star 8 and returning the measured position. Step 04 can be combined with step 03 into a single step.

Step S05 involves calculating a 2D image subset from the first 2D image set. The 2D image subset comprises those initial 2D projections of the first 2D image set which have corresponding subsequent 2D projections in the second 2D image set. A corresponding subsequent 2D projection is one captured using the same imaging settings as the initial 2D projection.

In the example shown in Figure 4, the 2D image subset comprises the initial 2D projections IP1 , IP3 and IP4.

Calculating the 2D image subset from the first 2D image set means reducing the first 2D image set, such that the arrow pointing from the first 2D image set to the 2D image subset in Figure 4 is marked with "reduction".

Step S06 involves image matching of the initial 2D projections in the 2D image subset onto the subsequent 2D projections in the second 2D image set. Image matching is based on the pairs of an initial 2D projection and a corresponding subsequent 2D projection. In the present example, the correspondence is established via the imaging settings. The initial 2D projection IP1 corresponds to the subsequent 2D projection SP1 , the initial 2D projection IP3 corresponds to the subsequent 2D projection SP2 and the initial 2D projection IP4 corresponds to the subsequent 2D projection SP3.

The image matching of step S06 establishes a spatial relationship in 3D between the 2D image subset and the second 2D image set, and thus between the first 2D image set and the second 2D image set. The result is a transformation given in up to three spatial dimensions and/or up to three rotational dimensions. In the present example, the transformation represents the (virtual) position of the medical 3D image dataset in the external reference system relative to the initial position of the medical 3D image dataset in the external reference system.

In the present example, the image matching of step S06 returns a numerical reliability measure representative of the reliability of the result of the image matching. Step S07 involves comparing the reliability measure with a predetermined threshold. If the reliability measure is equal to or larger than the threshold, the method proceeds to step S08.

Step S08 involves the calculation of the position of the medical 3D image dataset in the external reference system. This calculation can involve applying the transformation calculated in step S06 to the initial position of the medical 3D image dataset in the external reference system. However, this calculation can be a part of step S04.

In one example, step S08 calculates the position of the medical 3D image dataset in the reference system of the medical tracking system 9. This might involve a transformation of the position of the medical 3D image dataset in the reference system of the medical imaging device 1 into the position of the medical 3D image dataset in the reference system of the medical tracking systems 9, for example using the position of the reference system of the medical imaging device 1 in the reference system of the medical tracking system 9. The position of the medical imaging device 1 in the reference system of the medical tracking system 9 can for example be measured using the marker patches 5 attached to the medical imaging device 1 . After step S08, both the position of the medical 3D image dataset and of the reference star 8 in the reference system of the medical tracking system 9 are known.

Step S09 involves associating the position of the medical 3D image dataset and the position of the reference star 8 with each other, thus establishing a registration of the reference star 8 with the medical 3D image dataset of the anatomical structure S, and therefore with the anatomical structure S.

If, in step S07, the reliability measure is below the determined threshold, the method branches to step S10 in which at least one additional image pair is added to the second 2D image dataset and the 2D image subset.

In one implementation of step S10, an initial 2D projection of the first 2D image set, which is not yet comprised in the 2D image subset, is added to the 2D image subset and a new subsequent 2D projection is captured using the corresponding imaging settings. The thus supplemented 2D image set and 2D image subset are used when repeating step S06.

Instead of adding an initial 2D projection to the 2D image subset in step S10, suitable imaging settings can be determined, for example from a look-up table, and a new subsequent 2D projection is captured using these imaging settings. Then a DRR image is calculated from the medical 3D image dataset using the same imaging settings and the thus calculated DRR image is added to the 2D image subset. The thus supplemented second 2D image set and 2D image subset are used when repeating step S06.

Claims

32 CLAIMS

1 . A data processing method of calculating a transformation of a medical 3D image dataset of an anatomical structure in an external reference system which is external to the 3D image dataset, the 3D image dataset being reconstructed from a first 2D image set, comprising a plurality of initial 2D projections taken at a first point in time from different imaging positions relative to the anatomical structure and imaging settings for each of the initial 2D projections, wherein the imaging settings include the corresponding imaging position in the external reference system and the 3D image dataset thus has an initial position in the external reference system, the method comprising the steps of:

- acquiring a second 2D image set comprising a plurality of subsequent 2D projections of the anatomical structure taken at a second point in time later than the first point in time, wherein each one of the subsequent 2D projections was taken using the imaging settings of one of the initial 2D projections, thus resulting in a plurality of image pairs each comprising one initial 2D projection and one subsequent 2D projection taken with the same imaging settings,

- calculating a 2D image subset comprising those initial 2D projections of the first 2D image set which are comprised in the image pairs, and

- performing image matching of the 2D image subset onto the second 2D image set, thereby obtaining the transformation.

2. The method of claim 1 , wherein the step of acquiring the second 2D image set involves selecting indication specific imaging settings.

3. The method of claim 1 or 2, wherein the step of acquiring the second 2D image set involves selecting imaging settings based on a radiation dose applicable to the anatomical structure.

4. The method of any one of claims 1 to 3, wherein the plurality of initial 2D projections of the first 2D image set are taken using a medical imaging device having 33 predefined presets for the imaging settings of the initial 2D projections and the imaging settings for the second 2D image set are determined based on the predefined presets.

5. The method of any one of claims 1 to 4, wherein the step of performing image matching further provides a reliability measure representative of the reliability of the result of the image matching and the method involves, if the reliability measure is below a predetermined threshold, the additional steps of:

- supplementing the second 2D image set with at least one additional subsequent 2D projection, thus supplementing the image pairs with at least one additional image pair comprising one initial 2D projection and one additional subsequent 2D projection taken with the same imaging settings,

- supplementing the 2D image subset with those initial 2D projections of the first 2D image set which are comprised in the at least one additional image pair, and

- performing image matching of the supplemented 2D image subset onto the supplemented second 2D image set, thereby obtaining the transformation.

6. The method of any one of claims 1 to 4, wherein the step of performing image matching further provides a reliability measure representative of the reliability of the result of the image matching and the method involves, if the reliability measure is below a predetermined threshold, the additional steps of:

- calculating a DRR image set comprising at least one DRR image from the 3D image dataset with particular image settings,

- supplementing the 2D image subset with the DRR image set,

- acquiring at least one additional subsequent 2D projection comprising at least one additional subsequent 2D projection of the anatomical structure, wherein each one of the subsequent additional 2D projections was taken using the particular imaging settings of one of the DRR images in the DRR image set, thus resulting in a plurality of additional image pairs each comprising one DRR image and one subsequent 2D projection taken with the same imaging settings,

- supplementing the second 2D image set with the at least one additional subsequent 2D projection, and

7. The method of any one of claims 1 to 6, wherein

- in a first iteration, only a single first subsequent 2D projection is acquired, only a single initial 2D projection having the same imaging settings as the single first subsequent 2D projection is acquired and preliminary image matching of the single initial 2D projection onto the first subsequent 2D projection is performed, thus obtaining a preliminary transformation, and

- in a second iteration, the imaging settings for the other subsequent 2D projections are selected based on the preliminary transformation.

8. The method of claim 7, wherein, in the second iteration, an offset depending on the preliminary transformation is added to the imaging positions in the imaging settings of the initial 2D projections when used for acquiring the other subsequent 2D projections.

9. The method of claim 7, wherein an offset is calculated from the preliminary transformation and, in the second iteration, each of the plurality of image pairs comprises one subsequent 2D projection taken with particular imaging settings and one initial 2D projection having imaging settings with an imaging position differing from the imaging position in the particular imaging settings of the corresponding subsequent 2D projection by the offset.

10. The method of any one of claims 1 to 9, wherein the external reference system is a reference system of a medical tracking system or of a medical imaging device used for taking the plurality of subsequent 2D projections.

11. A data processing method of determining a registration of a 3D image dataset with an external reference system, comprising the step of applying the transformation obtained according to any one of claims 1 to 10 to an initial registration of the 3D image dataset with the external reference system.

12. A data processing method of registering a 3D image dataset of an anatomical structure with a marker device attached to the anatomical structure, comprising the steps of: - determining a registration of the 3D image dataset in an external reference system as claimed in claim 11 ,

- measuring the position of the marker device in the external reference system, and - calculating the position of the 3D image dataset relative to the marker device from the registration of the 3D image dataset in the external reference system and the position of the marker device in the external reference system, thus obtaining the registration of the 3D image dataset with the marker device.

13. A computer program which, when running on a computer, causes the computer to perform the steps of any one of claims 1 to 11 .

14. A computer on which the computer program according to claim 13 is stored and/or run.

15. A non-transitory computer readable storage medium on which the program according to claim 13 is stored.