Method for determining positioning data of a subject in a radiotherapy system

Abstract

Computer-implemented method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment, comprising the steps of: - obtaining planning image data of at least a region of interest of the subject (step S1); - obtaining treatment planning data of the region of interest (step S2), wherein the treatment planning data of the region of interest comprise at least position data of a target in the planning image data, position data of an organ at risk in the planning image data, and a treatment position; - obtaining current image data of at least the region of interest of the subject at the time of the radiotherapeutic treatment (step S3); - determining positioning data for the region of interest of the subject in the radiotherapy system for the radiotherapeutic treatment based on the obtained planning image data, the obtained treatment planning data and the obtained current image data (step S4).

Description

[0001] METHOD FOR DETERMINING POSITIONING DATA OF A SUBJECT IN A RADIOTHERAPY SYSTEM

[0002] FIELD OF THE INVENTION

[0003] The present disclosure relates to a computer implemented method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment, a data processing device, a computer program, a computer readable medium, and a medical system.

[0004] TECHNICAL BACKGROUND

[0005] Radiotherapeutic treatments are widely used for treatment of tumours. In a preparation phase of the radiotherapeutic treatment a technician (e.g., a physician or physicist) calculates treatment planning data used for a radiotherapy system. The treatment planning data is used for positioning a patient in relation to the radiotherapy system and for controlling the radiotherapy system. The calculation of the treatment planning data relies on the circumstances of the treatment of the tumour. However, these circumstances may vary between the calculation and the carrying out of the treatment. It has been found that a further need exists to provide a method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment.

[0006] EXEMPLARY SHORT DESCRIPTION OF THE INVENTION

[0007] The invention proposes a computer implemented method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment. The method proposes to determine positioning data for the subject based on planning image data, treatment planning data and current image data. The method determines a shift of a region of interest between the planning image data and the current image data. Based on the treatment planning data and the determined shift, new positioning data for the subject are determined such that the treatment planning data can still be used. This is advantageous as the tedious work of preparing updated treatment planning data does not have to be repeated. Instead, only the positioning data of the patient has to be updated.

[0008] GENERAL DESCRIPTION OF THE INVENTION

[0009] It is an objective of the present invention to provide a possibility to determine positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment. This and other objectives, which become apparent upon reading the following description, are solved by the subject matter of the independent claims. The dependent claims refer to preferred embodiments of the invention.

[0010] According to a first aspect of the present invention, a computer implemented method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment is provided, comprising the steps of: obtaining planning image data of at least a region of interest of the subject (step S1 ); obtaining treatment planning data of the region of interest (step S2), wherein the treatment planning data of the region of interest comprise at least position data of a target in the planning image data, position data of an organ at risk in the planning image data, and a treatment position; obtaining current image data of at least the region of interest of the subject at the time of the radiotherapeutic treatment (step S3); determining positioning data for the region of interest of the subject in the radiotherapy system for the radiotherapeutic treatment based on the obtained planning image data, the obtained treatment planning data and the obtained current image data (step S4).

[0011] The term positioning data, as used herein, is to be understood broadly and may relate to spatial coordinates of a region of interest of the subject to be treated in relation to the radiotherapy system. The positioning data may comprise translational coordinates. The positioning data may comprise rotational coordinates. The positioning data may relate to one (e.g., centre) or more (e.g., centre and an end) points of the region of interest. The positioning data may preferably be used to arrange a patient table on which the patient is lying during a treatment such that the region of interest is arranged according to the determined positioning data.

[0012] The term subject, as uses herein, is to be understood broadly and may relate to a human or an animal.

[0013] The term region of interest, as used herein, is to be understood broadly and may relate to any structure in the subject. The region of interest may preferably be a human or animal tissue such as a tumour or a functional location such as a nerve, or an arteriovenous malformation, or another pathological body part

[0014] The term radiotherapy system, as used herein, is to be understood broadly and may relate to any radiotherapy system that is configured to apply a radiotherapeutic treatment to a subject. The radiotherapeutic treatment preferably comprises a controllable patient table to arrange a region of interest of a subject to be treated in relation to a radiation source of the radiotherapy system. The radiotherapy system may comprise a radiation source (i.e. accelerator).

[0015] The term radiotherapeutic treatment, as used herein, is to be understood broadly and may preferably relate to an irradiation of a region of interest with high energy radiation.

[0016] The term planning image data, as used herein, is to be understood broadly and may relate to a medical image. The planning image data may be a CT image. The planning image data may be a MRT image. The planning image data may preferably be a 3D image. The planning image data may be obtained (i.e. acquired) in a preparation phase in order to generate treatment planning data of the region of interest.

[0017] The term treatment planning data of the region of interest, as used herein, is to be understood broadly and may comprise at least position data of a target in planning image data, position data of an organ at risk in planning image data, treatment position data. The term treatment position data, as used herein, is to be understood broadly and may relate to positioning data of the region of interest in relation to a radiation source (i.e. accelerator) of the radiotherapy system. The treatment planning data may preferably comprise one or more radiation parameter (e.g., radiation energy, radiation duration, direction of incidence of the radiation, position of radiation limiting elements). The radio treatment planning data may preferably comprise a reference dose volume histogram. The reference dose volume histogram may relate to a theoretical distribution of radiation in the region of interest when conducting the radiotherapeutic treatment based on the radio treatment plan. A dose volume histogram (DVH) may show a graphical representation of the radiation dose delivered to any defined volume.

[0018] The term current image data, as used herein, is to be understood broadly and may relate to an image that is acquired before the radiotherapeutic treatment is conducted and after the treatment planning data is generated. The current image may be obtained at the time of the radiotherapeutic treatment. The current image data may give an insight how a position of a target and an organ at risk changed due to changes in the body of the subject (e.g., weight loss, weight growth, tumour enlargement etc.). The current image data may be a CT image. The current image data may be acquired by an x-ray stereo imaging system. The current image data may be acquired by a CBCT. The current image data may be an MRT image. The current image data may preferably be 3D image data.

[0019] The invention is based on the finding that the generation of treatment planning data is a tedious work. Such treatment planning data are generated by a medical physicist (i.e. a technician) and have to be approved by a physician (i.e. medical doctor) responsible for the radiotherapeutic treatment. Due the time span between the generation of the treatment planning data and the carrying out of the radiotherapeutic treatment, the region of interest of the subject may change. Such a change may be caused a weight loss or a weight growth or by a growth of the target (i.e. tumour). Instead of repeating the tedious work of generating new treatment planning data from the beginning, the method proposes to adapt the positioning data of the region of interest in relation to the radiation source, such that merely the treatment positioning data are amended and the resulting radiation distribution (i.e. new dose volume histogram) matches the predefined requirements (i.e. reference dose volume histogram). This may allow to reuse most of the planning treatment data and merely to amend the positioning data of the region of interest.

[0020] In an embodiment, the step S4 of determining the positioning data for the region of interest of the subject may comprise determining a shift vector between the target in the planning image data and the target in the current image data. The shift vector may be determined by an image analysis method. The shift vector may be determined by a tensor transformation. The shift vector may be determined by a rigid fusion. The shift vector may be determined by an elastic fusion. The shift vector may allow to quantify a change of a position between a past state in the planning phase and a current state. The shift vector may allow to assess whether original underlying positioning data for the region of interest are still be applicable for the upcoming radiotherapeutic treatment. The shift vector may allow to determine the current position of the target in the current image data. E.g., in case the shift vector revealed that the shift was small or below a predefined threshold, no positioning data would have to be determined. The shift vector may be used for registration of the planning image data and the current image data.

[0021] In an embodiment, the step S4 of determining the positioning data for the region of interest of the subject may comprise determining an organ at risk (OAR) target vector between the organ at risk and the target in the current image data. The OAR target vector may preferably be defined by a first spatial centre of the OAR and a second spatial centre of the target. The OAR target vector may be defined by the first spatial centre of the OAR and a direction. The OAR target vector may preferably be a 3 dimensional vector. The OAR target vector may serve as basis for determining positioning data. The OAR target vector may help advantageously to decrease the solution space for finding positioning data for the region of interest of the subject. ln an embodiment, the step S4 of determining the positioning data for the region of interest of the subject may comprise determining at least one test position for the region of interest of the subject in a radiotherapy system. The term test position, as used herein, is to be understood broadly and may relate to possible positioning data for the region of interest. The term test position may comprise a plurality of test positions. The test position may be found by means of the OAR target vector. The test position may lie on the OAR vector. The test position may be evaluated regarding a resulting DVH. The test position may be used as information for controlling a patient table on which the patient is lying during the radiotherapeutic treatment (i.e. commands for controlling one or more axes of a patient table).

[0022] In an embodiment, the determining of the positioning data for the region of interest of the subject may comprise determining a dose volume histogram (DVH) for the at least one test position. The determining of the DVH may comprise a calculation of the distribution of radiation over the region of interest based on the treatment planning data, wherein the previous positioning data of the region of interest from the treatment planning data are replaced by positioning data of the at least one test position. The so determined DVH reveals a theoretical distribution of the radiation when using the test position. The determined DVH may enable an assessment of the test position regarding the damage of the organ at risk (e.g., liver) and the efficacy of the treatment of the target (e.g., tumour).

[0023] In an embodiment, the method may further comprise comparing the determined DVH for the at least one test position with a reference DVH from the planning data and deriving a quality result, and selecting the positioning data from the at least one test position based on the quality result. The comparing may comprise a calculation of a difference of a dose for the target and / or the organ at risk. The calculated difference may be compared with a predefined threshold. For example, the threshold may allow a deviation 2 per cent in relation to reference DVH. In other words, the calculated difference may not deviate more than two per cent from the reference DVH. The DVH comprises a dose distribution over a plurality of volume elements. The comparing may be executed for at least at part of these plurality of volume elements. The quality result may comprise a value within a range (e.g., 0.8 in a range from 0 to 1 , wherein 0 is bad and 1 is perfect). The quality result may allow to compare different test positions among each other. The test position with the best quality result may be selected. This may increase the quality of the method. The quality result may further be based on prescription parameters (i.e. recommendation of dose distribution of a medical doctor).

[0024] In an embodiment, the determining of the shift vector between the target in the planning image data and the target in the current image data may be based on a fusion of the planning image data and the current image data. The fusion may be a rigid fusion. The rigid fusion may advantageously be used for small shifts. The fusion may be an elastic fusion. The elastic fusion may be advantageously be used for big shifts.

[0025] In an embodiment, the fusion may be an elastic fusion.

[0026] In an embodiment, the determining of the test position may be based on a position of the OAR in the current image data and a position of the target in the current image data. The position of the target may be determined by the shift vector described above. The position of the OAR and the position of target may be used to define a vector or a geometric body. The vector or geometric body may serve as a solution space for possible test positions. For example, the test position may be lie on the vector or within the geometric body. This may advantageously decrease the effort to find a proper test position.

[0027] In other words, the current position positions of the target and the OAR serve as basis for defining a solution space for one or more test positions. This may advantageously decrease the effort for determining a test position.

[0028] In an embodiment, the determining of the test position may be based on a volume defined by a position of the OAR in the current image data and a position of the target in the current image data. The volume may comprise any shape. The volume may comprise a first end and a second end, wherein the position of target defines the first end and the position of the OAR defines the second end. The test position may lie within the defined volume. This may advantageously decrease the effort for determining a test position.

[0029] In an embodiment, the volume may be one of the following: a cone, a cylinder, and a lens.

[0030] In an embodiment, the determining of the test position may be based on a vector, defined by a position of the OAR in the current image data and a position of the target in the current image data, and a predefined distance around the vector. The predefined distance may be a fixed value or a variable value (e.g., increasing with distance from target or vice versa). The test position or test positions may lie on a surface that is defined by the vector and predefined distance. The test position or test positions may lie within the volume defined by the vector and the predefined distance.

[0031] In an embodiment, the determining of the test position may be based on a dose gradient from the treatment planning data. The dose gradient describes a dose intensity change over a distance. The method proposes to determine more test positions in an area where the dose gradient is high (i.e. a high dose intensity change) and to determine less test positions in an area where the dose gradient is low (i.e. a low dose intensity change). Additionally, the method proposes to determine more test positions in an area where the dose is low and starts to change (i.e. increase of dose and increase of dose gradient). This may advantageously decrease the effort of determining proper positioning data.

[0032] In an embodiment, the determining of the test position may be based on one or more of the following: size of target, size of OAR, distance between OAR and target.

[0033] The size of the target and the OAR relates to their respective volumes. For example, the test positions may lie in a volume that relates to a cone. The dimensions of the cone may be defined by means of the distance and the volumes of the target and the OAR. This may advantageously increase the efficacy of the method.

[0034] In an embodiment, the DVH may be based on a dose distribution of a single fraction. The term single fraction, as used herein, is to be understood broadly and may preferably relate to a single radiotherapeutic treatment in the radiotherapy system. A common radiotherapeutic treatment comprises a plurality of single fractions distributed over a time span (e.g., three months). The determined DVH and / or the reference DVH may relate to such a single fraction. In other words, the dose distribution of the planning data of a single fraction is used to determine the DVH.

[0035] In an embodiment, the determined DVH may be based on logged machinery data of the radiotherapy system; wherein the logged machinery data comprises one or more of the following: radiated dose and treatment position. Instead of just using a theoretical dose distribution of a single fraction, the logged machinery data from one or more already conducted treatments may be used for determining the DVH. The determined DVH may be based on logged machinery data (i.e. previous single fractions) of the radiotherapy system and a single fraction of an upcoming radiotherapeutic treatment. This may advantageously increase the reliability of the method.

[0036] In an embodiment, the deriving of the quality result may be based on indication specific parameters.

[0037] The term indication specific parameter, as used herein, is to be understood broadly and may relate to a sensitivity of radiation of an organ at risk. For example, a liver has a low sensitivity to radiation and a spine has a high sensitivity to radiation. Such an information may be advantageously considered by deriving the quality result. The indication specific parameter may be used for weighting a result of the comparing of the determined DVH with the reference DVH. The weighting may advantageously effect the reliability of the method.

[0038] In an embodiment, the method may further comprise determining a dose for the provided determined positioning data for the radiotherapy system for the radiotherapeutic treatment based on logged machinery data from the radiotherapeutic treatment and providing the determined dose, and preferably summing up the determined dose with determined doses from former fractions and providing a total dose for the region of interest. The term total dose, as used herein, may relate to an accumulated dose over a plurality of single fractions.

[0039] This may advantageously allow determining a realistic dose that was applied to a region of interest of a subject.

[0040] In an embodiment, the method may further comprise providing a prediction model configured to predict a shift of the target and the organs at risk in the region of interest; and further determining based on the predicted shift of the target and the organs at risk the at least one test position.

[0041] The prediction model may be any model configured to describe a shift of the target and the organs at risk in the region of interest. The prediction model may be may be based on 4D image data. The 4D image data may for example relate to lung movements. The lung movements may indicate possible positions of the target and the organ at risk in the region of interest with a corresponding probability. Alternatively or additionally, the prediction model may consider one or more anatomical boundary conditions in order to predict the shift. For instance, a prostate in general moves along a preferred direction (i.e. vertical in the body and not sideways).

[0042] The prediction model may be used beforehand the upcoming treatment in order to predict a shift. Instead of carrying out the method described above based on the determined shift vector, the method may be carried based on a predicted shift vector. This may advantageously transfer computationally intensive steps in a preliminary stage. In case a determined shift corresponds to a predicted shift the corresponding determined positioning data for the region of interest of the subject can be reused. This may speed up the method in the current situation when a patient is prepared for the radiotherapeutic treatment.

[0043] In an embodiment, the current image data may be data from an x-ray stereo system or a CBCT system.

[0044] In an embodiment, the determined positioning data may be used to position the subject in the radiotherapy system for the radiotherapeutic treatment, wherein an original treatment plan remains unchanged. The positioning data may be used to derive commands for one more drive axles of a patient table in order to position the region of interest such that the determined positioning data can be achieved. A further aspect of the present disclosure relates to a data processing device, comprising means for carrying out the method described above.

[0045] A further aspect of the present disclosure relates to a computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method described above.

[0046] A further aspect of the present disclosure relates to a computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method described above.

[0047] A further aspect of the present disclosure relates to a medical system comprising: a data processing device as described above, and a medical device, preferably a radiotherapy system for a radiotherapeutic treatment.

[0048] In an embodiment, the medical device may be a radiotherapy system for a radiotherapeutic treatment comprising a patient table.

[0049] In an embodiment, the medical device may be configured to position the subject based on the determined positioning data for a subsequent radiotherapeutic treatment.

[0050] In an embodiment, the medical device may use the determined positioning data to control one or more drive axles of the patient table for positioning a subject.

[0051] DEFINITIONS Computer implemented method

[0052] 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.

[0053] 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 light box. An example of such a digital light box 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.

[0054] 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.

[0055] 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 instruction-executing 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.

[0056] Image registration

[0057] Image registration is the process of transforming different sets of data into one co-ordinate 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.

[0058] Imaging methods

[0059] 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.

[0060] 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.

[0061] Mapping 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 colour 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).

[0062] Elastic fusion, image fusion / morphing, rigid

[0063] 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.

[0064] In this application, the term "image morphing" is also used as an alternative to the term "elastic image fusion", but with the same meaning.

[0065] 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.

[0066] 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.

[0067] 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.

[0068] A measure of similarity can for example be determined on the basis of a determined correlation between the first and second datasets.

[0069] BRIEF DESCRIPTION OF THE DRAWINGS In the following a, the present disclosure is exemplarily with reference to the enclosed figures, in which

[0070] Figure 1 is a schematic view of a method according to the preferred embodiment of the present invention;

[0071] Figure 2 is a schematic view of an arrangement of a target and an organ at risk in a planning image;

[0072] Figure 3 is schematic view of an arrangement of a target and organ at risk in a current image;

[0073] Figure 4 is a schematic view of possible test positions in dependency of an OAR target vector;

[0074] Figure 5 is a schematic view of further possible test positions in dependency of the centre organ at risk and the centre of the target;

[0075] Figure 6 is a schematic view of possible test positions in dependency of a dose gradient; and

[0076] Figure 7 is a schematic view of an exemplary medical system.

[0077] DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a schematic view of a method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment.

[0079] Step S1 comprises obtaining planning image data of at least a region of interest of the subject. The region of interest is in the present example a stomach area of a patient. The radiotherapeutic treatment is in the present example a radiation of a tumour. The radiotherapy system comprises in the present example a positioning system for a patient, a position able radiation source (i.e. an accelerator), and an x-ray device for obtaining current image data. The x-ray device in the present example is an x-ray stereo system. The planning image data are acquired in the present example with a CT.

[0080] Alternatively, the planning image data may be acquired with an MRI.

[0081] Step S2 comprises obtaining treatment planning data of the region of interest. The treatment planning data comprises positioning data of a target in the planning image data, position data of an organ at risk in the planning image data, and a treatment position. The treatment planning data may comprise additionally a reference DVH. The treatment planning data may additionally comprise one or more radiation parameter.

[0082] Step S3 comprises obtaining current image data of at least the region of interest of the subject at the time of the radiotherapeutic treatment. The current image data are obtained in the present example with the x-ray stereo system of the radiotherapy system.

[0083] Step S4 comprises determining positioning data for the region of interest of the subject in the radiotherapy system for the radiotherapy therapeutic treatment based on the obtained planning image data, the obtained treatment planning data and the obtained current image data. Step S4 may comprise determining a shift vector between the target and the planning image data and the target in the current image data. The shift vector may be determined by a fusion of the planning image data and the current image data. The fusion may be an elastic fusion. Step S4 may comprise determining an OAR target vector between the organs at risk and the target in the current image data. Step S4 may comprise determining at least one test position. The at least one test position may lie on the OAR target vector. The determining of the test position may be based on a volume defined by the position of the organ at risk and the target in the current image data. The volume may be one of the following a cone, a server in the, and a lens. The determining of the test position may be based on a dose gradient. The determining of the test position may be based on one or more of the following: size of the target size of the organs at risk, distance between the organs at risk and the target. The step S4 may comprise determining a DVH for the at least one test position. The step S4 may comprise comparing the determined DVH with a reference DVH from the planning data and deriving a quality result. Based on the derived quality result the positioning data (i.e. selected test position) from the at least one test position may be selected. For example, two test positions are analysed as described above and the test position with the best quality result is selected. The so determined positioning data is used in the present example to position the subject in the radiotherapy system for the radiotherapeutic treatment, wherein the radiation parameters of the original treatment plan remain unchanged.

[0084] Figure 2 is a schematic view of an arrangement of an organ at risk 10 with its centre 12 and a planned target volume 11 with its centre 13 in a planning image 15. The planned target volume 11 covers in the present example the tumour 14 (i.e. target). The centre 13 of the planned target volume 11 coincides with the centre of the tumour 14.

[0085] Figure 3 is a schematic view of a corresponding arrangement of an organ at risk 20 with its centre 22 and a planned target volume 21 with its centre 23 in a current image 27. The tumour 24 corresponds to the tumour 14 in Figure 2. The arrangement of the organ at risk 20 and the planned target volume 21 and tumour 24 changed in relation to Figure 2. The position of the centre 23 of the tumour 24 changed in relation to the centre 22 of the organ at risk 20. The centre 23 of the tumour 24 is presently also the centre of the planned target volume 21. The planned target volume 21 also covers a part of the organ at risk 20. This is depicted as an overlap 25. Furthermore, the organ at risk target vector 26 is depicted in the current image 27. The OAR target vector 26 is defined by the centre 22 of the organ at risk 20 and the centre 23 of the tumour Figure 4 shows possible test positions in dependency of the OAR target vector 35 in a current image 36. Figure 4 shows an organ at risk 30 and corresponding centre 32 and a target 34 (i.e. tumour) and a corresponding centre 33. Furthermore, the original planned target volume 31 and its corresponding centre 33, which is the same as the centre of the target 34, are depicted. Furthermore, the OAR target vector 35 is depicted. Figure 4 shows additional test positions 40 to 42, wherein each of the test position lies on the OAR target vector 35. Each test position corresponds to a shift of the centre of the planned target volume. The shifted target volumes 43 to 45 correspond to the test positions 40 to 42. In the present example, the test position 41 enables a radiation of the target 34 without radiating the organ risk 30. Test position 40 would still radiate at least partially the organ at risk 30 and test position 42 would only radiate the target 34 partially. The test position relates to a positioning of the region interest of the patient in the radiotherapy system.

[0086] Figure 5 shows further test positions in dependency of the centre of the organ a risk 50 and the centre 51 of the target in the current image. Therein a cylinder 52 is depicted. The cylinder 52 may be defined by central axis through centres 50 and 51 and a constant radius. The radius e.g. is defined by the radius of the OAR or the target or an empiric value. The cylinder 52 starts at the centre 50. In the cylinder 52 a plurality of possible test positions 59 and 60 is depicted. Furthermore, a lens 54 is depicted. The lens 52 may be defined by a central axis through centres 50 and 51 and a variable radius. The lens 52 may start at the centre 50. In the lens 52 a plurality of possible test positions 57 and 58 is depicted. Furthermore, a cone 53 is depicted. The cone 53 may be defined by a central axis through centres 50 and 51 and an angle. The cone 53 may start at the centre 50. In the cone 53 a plurality of possible test positions 55 and 56 is depicted. Figure 6 shows possible test positions in dependency of a dose gradient 70. The dose gradient 70 is depicted over a horizontal axis 71 and a vertical axis 72. The horizontal axis 71 relates to a spatial extension within the region of interest. The vertical axis 72 relates to a dose. The possible test positions are distributed over the horizontal axis 71. The test positions 76, 77, 78 are distributed unequally. In area 72 the dose gradient changes significantly. The method proposes to distribute more test positions 76 in this area. In the areas 74 and 75 the dose gradient does not change significantly. The method proposes to determine more test positions in an area where the dose is low and starts to change (i.e. increase of dose and increase of dose gradient). This applies to the area 74 and 73. The method proposes to distribute only a few test positions 78 in area 75 as the dose is already very high in this area. In sum, this may advantageously decrease the calculation effort.

[0087] Figure 7 shows an exemplary view of a medical system 100. The medical system 100 comprises a data processing device 101 configured to carry out the method described above. The medical system 100 comprises a medical device. The medical device comprises radiotherapy system 102 configured to provide a radiotherapeutic treatment. The medical device comprises a patient table 104 configured to position a region of interest 103 of a subject based on the determined positioning data for a subsequent radiotherapeutic treatment. The medical device uses the determined positioning data to control one or more axes of the patient table for positioning the subject. The medical system comprises a stereoscopic x-ray imaging system 105 for obtaining current image data. REFERENCE SIGNS

[0088] 51 obtaining planning image data

[0089] 52 obtaining treatment planning data

[0090] 53 obtaining current image data

[0091] 54 determining positioning data

[0092] 10 organ at risk

[0093] 11 planned target volume

[0094] 12 centre organ at risk

[0095] 13 centre planned target volume, centre target

[0096] 14 tumour (i.e. target)

[0097] 15 planning image

[0098] 20 organ at risk

[0099] 21 planned target volume

[0100] 22 centre organ at risk

[0101] 23 centre target

[0102] 24 tumour

[0103] 25 overlap

[0104] 26 organ at risk target vector

[0105] 27 current image

[0106] 30 organ at risk

[0107] 31 planned target volume

[0108] 32 centre organ at risk

[0109] 33 centre target

[0110] 34 target

[0111] 35 organ at risk target vector

[0112] 36 current image

[0113] 40, 41 , 42 test position

[0114] 43, 44, 45 shifted target volumes 50 centre organ at risk

[0115] 51 centre target

[0116] 52 cylinder

[0117] 53 cone

[0118] 54 lens

[0119] 55, 56 test positions cone

[0120] 57, 58 test positions lens

[0121] 59, 60 cylinder

[0122] 70 dose gradient

[0123] 71 horizontal axis

[0124] 72 vertical axis

[0125] 73 area with high dose gradient

[0126] 74, 75 areas with low dose gradient

[0127] 76, 77, 78 test positions

[0128] 100 system

[0129] 101 data processing device

[0130] 102 radiotherapy system

[0131] 103 subject

[0132] 104 moveable patient table

[0133] 105 x-ray stereo system

Claims

CLAIMS1. Computer-implemented method for determining positioning data of a subject in a radiotherapy system for a radiotherapeutic treatment, comprising the steps of:- obtaining planning image data of at least a region of interest of the subject (step S1 );- obtaining treatment planning data of the region of interest (step S2), wherein the treatment planning data of the region of interest comprise at least position data of a target in the planning image data, position data of an organ at risk in the planning image data, and a treatment position;- obtaining current image data of at least the region of interest of the subject at the time of the radiotherapeutic treatment (step S3);- determining positioning data for the region of interest of the subject in the radiotherapy system for the radiotherapeutic treatment based on the obtained planning image data, the obtained treatment planning data and the obtained current image data (step S4).

2. The method according to claim 1 , wherein the step S4 of determining the positioning data for the region of interest of the subject comprises determining a shift vector between the target in the planning image data and the target in the current image data.

3. The method according to claim 1 or 2, wherein the step S4 of determining the positioning data for the region of interest of the subject comprises determining an organ at risk (OAR)-target vector between the organ at risk and the target in the current image data.

4. The method according to any one of the preceding claims, wherein the step S4 of the determining the positioning data for the region of interest of the subject comprises determining at least one test position for the region of interest of the subject in a radiotherapy system.

5. The method according to claim 4, wherein the determining the positioning data for the region of interest of the subject comprises determining a dose volume histogram (DVH) for the at least one test position.

6. The method according to claim 5, further comprising comparing the determined DVH for the at least one test position with a reference DVH from the planning data and deriving a quality result, and selecting the positioning data from the at least one test position based on the quality result.

7. The method according to claim 2, wherein the determining the shift vector between the target in the planning image data and the target in the current image data is based on a fusion of the planning image data and the current image data.

8. The method according to claim 7, wherein the fusion is an elastic fusion.

9. The method according to claim 3 and 4, wherein the determining the test position is based on a position of the OAR in the current image data and a position of the target in the current image data.

10. The method according to claim 3 and 4, wherein the determining the test position is based on a volume defined by a position of the OAR in the current image data and a position of the target in the current image data.

11. The method according to claim 10, wherein the volume is one of the following: a cone, a cylinder, and a lens.

12. The method according to claim 3 and 4, wherein the determining the test position is based on a vector, defined by a position of the OAR in the current image data and a position of the target in the current image data, and a predefined distance around the vector.

13. The method according to claim 4, wherein the determining the test position is based on a dose gradient from the treatment planning data.

14. The method according to any one of the claims 4 to 13, wherein the determining the test position is based on one or more of the following: size of PTV, size of OAR, distance between OAR and PTV.

15. The method according to claims 5 or 6, wherein the DVH is based on a dose distribution of a single fraction.

16. The method according to any one of the claims 5, 6 or 15, wherein the DVH is based on logged machinery data of the radiotherapy system; wherein the logged machinery data comprises one or more of the following: radiated dose and treatment position.

17. The method according to any one of the claims claim 5, 6, 15 or 16, wherein the deriving the quality result is based on indication specific parameters.

18. The method according to any one of the preceding claims, further comprising determining a dose for the provided determined positioning data for the radiotherapy system for the radiotherapeutic treatment based on logged machinery data from the radiotherapeutic treatmentand providing the determined dose, and preferably summing up the determined dose with determined doses from former fractions and providing a total dose for the region of interest.

19. The method according to any one of the preceding claims, further comprising providing a prediction model configured to predict a shift of the target and the organs at risk in the region of interest; and further determining based on the predicted shift of the target and the organs at risk the at least one test position.

20. The method according to any one of the preceding claims, wherein the current image data is data from an x-ray stereo system or a CBCT system.21 . The method according to any one of the preceding claims, wherein the determined positioning data are used to position the subject in the radiotherapy system for the radiotherapeutic treatment, wherein an original treatment plan remains unchanged.

22. A data processing device, comprising means for carrying out the method according to any one of the claims 1 to 21 .

23. A computer program comprising instructions which, when the program is executed by a computer, cause the computer to carry out the method according to any one of the claims 1 to 21 .

24. A computer-readable medium comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of the claims 1 to 21 .

25. A medical system comprising:a data processing device according to claim 22, and a medical device, preferably a radiotherapy system for a radiotherapeutic treatment.

26. The medical system according to claim 25, wherein the medical device is a radiotherapy system for a radiotherapeutic treatment comprising a patient table.

27. The medical system according to claims 25 or 26, wherein the medical device is configured to position the subject based on the determined positioning data for a subsequent radiotherapeutic treatment.

28. The medical system according to claim 27, wherein the medical device uses the determined positioning data to control one or more axes of the patient table for positioning a subject.

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