The provision of the resulting image data

Abstract

The invention relates to a method of providing result image data, comprising: - receiving pre-acquired first image data of an object comprising an examination region, wherein the first image data maps the examination region, - generating a model data set based on the first image data, - receiving pre-acquired second image data of the object, - pre-aligning the model data set and the second image data on a first level of detail which is lower than a second level of detail, - the model data set and the second image data are registered on the second level of detail based on a second feature of a second class of features of the examination region, which is mapped into the model data set and the second image data on the second level of detail, wherein the second class of features is mappable on the second level of detail or higher, - providing the registered second image data and / or the registered model data set as result image data. The invention also relates to a providing unit, a system and a computer program product.

Description

Technical Field

[0001] The present invention relates to a method, providing unit, system, and computer program product for providing resulting image data. Background Technology

[0002] By displaying a graphical representation of a subject's preoperative image data, medical personnel, such as interventional physicians and / or surgeons, can be supported during the subject's examination and / or treatment, particularly intraoperatively. Here, preprocessed image data can advantageously include preprocessing information, such as planning information. Intraoperative image data of the subject is typically acquired during examination and / or treatment for supervision and / or guidance. To combine preoperative and intraoperative image data into a common graphical display, the preoperative and intraoperative image data can be registered. For example, registration can be based on the subject's anatomical and / or geometric features, which are typically mapped in the preoperative and intraoperative image data. These features are often limited by the level of detail in which they are mapped in the preoperative and / or intraoperative image data.

[0003] However, this registration between preoperative and intraoperative imaging data can become challenging when they contain mappings of soft tissue organs, such as the liver. Significant deformation of these soft tissue organs can lead to large changes in the spatial location of their geometric and / or anatomical features between preoperative and intraoperative imaging. Here, the registration problem is often underestimated and can only be addressed by making assumptions that may lead to undesirable deviations from reality. Summary of the Invention

[0004] Therefore, the goal of the present invention is to provide a method and means to achieve more robust and accurate image registration.

[0005] In a first aspect, the present invention relates to a method for providing resulting image data. In a first step, pre-obtained first image data of an object including an inspection region is received. Furthermore, the first image data is mapped to the inspection region. In a next step, a model dataset is generated based on the first image data. In a further step, pre-obtained second image data of the object is received. The model dataset and the second image data map at least one common portion of the inspection region at a second level of detail. In a further step, the model dataset and the second image data are pre-aligned at a first level of detail, below the second level of detail, based on a first feature of a first class of features of the inspection region, which is mapped to the model dataset and the second image data at the first level of detail. Alternatively or additionally, the model dataset and the second image data are pre-aligned at the first level of detail based on geometric quantities of the object, particularly the inspection region, obtained from the second image data. In a further step, the model dataset and the second image data are registered at the second level of detail based on a second feature of a second class of features of the inspection region, which is mapped to the model dataset and the second image data at the second level of detail, wherein the second class of features is capable of mapping at the second level of detail or higher. In a further step, the registered second image data and / or the registered model dataset are provided as resulting image data.

[0006] The above steps of the proposed method can be performed at least partially sequentially and / or simultaneously.

[0007] Furthermore, the steps of the proposed method can be implemented at least partially, and in particular entirely, by a computer.

[0008] Receiving pre-acquired first image data and / or pre-acquired second image data may include collecting and / or reading data from an electronically readable storage medium and / or receiving data from a memory unit, particularly a database. Furthermore, the pre-acquired first and second image data may be received from the same medical imaging device or different medical imaging devices. At least one medical imaging device for acquiring the first and / or second image data may include a magnetic resonance imaging system (MRI) and / or a positron emission tomography (PET) system and / or an ultrasound system and / or a medical X-ray system and / or a computational tomography (CT) system and / or an optical imaging system, such as an endoscope and / or a laparoscope.

[0009] Advantageously, the first and second image data are obtained before the proposed method begins, in particular, the first and second image data are obtained in advance.

[0010] The first image data can map an examination region of an object in two-dimensional (2D) or three-dimensional (3D) spatial resolution. Furthermore, the second image data can map at least a common portion of the spatially resolved 2D or 3D examination region. Additionally, the first and / or second image data can be temporally resolved. The object (or subject) can be a human and / or animal patient and / or examination phantom. Furthermore, the examination region can include spatial regions of the object, such as anatomical regions, particularly organs and / or tissues. The common portion of the examination region can include the examination region or a portion of the examination region, particularly a spatial segment. The first image data can include multiple image points, particularly pixels or voxels, with assigned image values ​​mapping the examination region. Similarly, the second image data can each include multiple image points, in which image values ​​are assigned to map at least a common portion of the examination region within a specific pixel or voxel. If the first and / or second image data are temporally resolved, the image points can each include a time-intensity curve. Therefore, the first and / or second image data can respectively map variations in the examination region of the object, such as contrast agent flow and / or motion, particularly physiological motion and / or motion of the medical object. Furthermore, the first image data may include first metadata, which may include information about the acquisition geometry and / or acquisition parameters and / or operating parameters of the medical imaging device, for acquiring the first image data during acquisition. Similarly, the second image data may include second metadata, which may include information about the acquisition geometry and / or acquisition parameters and / or operating parameters of the medical imaging device, for acquiring the second image data during acquisition.

[0011] Furthermore, the first image data may include planning information, such as a surgical resection plan. The planning information may include graphical workflow prompts that are registered in the first image data by mapping the examination regions (particularly features of the first and / or second class of features).

[0012] Advantageously, the first image data can be mapped to the inspection area at the first acquisition time, particularly before the program. Therefore, the first image data can map the inspection area in a first deformed state. The first deformed state can be characterized, for example, by the spatial arrangement of the first and / or second features mapped in the first image data relative to each other. Furthermore, the second image data can be mapped to at least a common portion of the inspection area at a second acquisition time after the first acquisition time, particularly within or after the program. Therefore, the second image data can map the inspection area in a second deformed state. The second deformed state can be characterized, for example, by the spatial arrangement of the first and / or second features mapped in the second image data relative to each other.

[0013] The first and / or second levels of detail can be characterized by mapping and / or acquiring first image data, second image data, and / or parameters of at least one medical imaging device. In particular, the first and / or second levels of detail can be characterized by spatial resolution and / or temporal resolution. The first and / or second levels of detail can be characterized by further image quality measures of the first and / or second image data, such as the dynamic range of image values, signal-to-noise ratio (SNR), and / or contrast-to-noise ratio (CNR). Furthermore, the first and / or second levels of detail can be characterized by different types of anatomy, such as organ and / or tissue types, which exhibit different image contrasts, particularly for different imaging modalities and / or image acquisition parameters. The second level of detail can be determined and / or limited by the highest common spatial resolution and / or temporal resolution and / or image quality measures of the first and second image data. Additionally, the first level of detail can be determined by the level of accuracy of the generated model dataset, such as reconstruction parameters and / or model parameters. Alternatively or additionally, the first level of detail can be determined by the level of accuracy of the second image data with respect to the acquisition form of the object.

[0014] The features of the first type of feature can display a size higher than the minimum size of the first type of feature determined by the first level of detail. Similarly, the features of the second type of feature can display a size higher than the minimum size of the second type of feature determined by the second level of detail and lower than the minimum size of the first type of feature.

[0015] Advantageously, the model dataset may include a 2D or 3D spatial representation of the area to be examined. Furthermore, the model dataset may be temporally resolved. Advantageously, the model dataset may represent the area to be examined prior to the acquisition of the second image data, particularly before the procedure. The model dataset may include the first image data. Alternatively, the model dataset may be generated based on the first image data, for example, reconstructed and / or segmented from the first image data. The model dataset can display all the functions described above for the first image data. Furthermore, the model dataset may include a virtual representation of the area to be examined, particularly anatomical targets and / or tissues within the area to be examined, such as a mesh model and / or a centerline model.

[0016] The first type of features can include all features of the examined region, particularly all geometric and / or anatomical features, which are mappable and distinguishable, and particularly identifiable, at a first level of detail and above. The first type of features can include, for example, large-scale anatomical landmarks of the examined region, such as tissue boundaries and / or organ surface shapes and / or tumors and / or large blood vessels and / or large blood vessel bifurcations. Since the features of the first type of features are mappable and identifiable at a first level of detail and above, particularly at a second level of detail, the second image data can include first features of the first type of features commonly mapped in the model dataset (particularly in the first image data), as well as second image data at the first level of detail. The first features of the first type of features can include geometric and / or anatomical features of the examined region that are mapped at a first level of detail to the model dataset, particularly in the first image data, and in the second image data. Furthermore, at least some, particularly all, of the first features mapped to the model dataset can be generated as part of the generated model dataset. For example, at least some, particularly all, of the first features mapped in the model dataset are not mapped to the first image data. Here, the first image data can map features of the second type of features, especially the second features, but the first features are advantageously generated, especially reconstructed, as part of the generative model dataset.

[0017] The pre-alignment of the model dataset and the second image data may include determining a first transformation rule for rigid and / or deformable transformations (particularly translation and / or rotation and / or deformation and / or scaling) of the model dataset and / or the second image data. Advantageously, the minimum level of precision for the pre-alignment between the model dataset and the second image data, particularly the level of precision of the first transformation rule, may be equivalent to a first level of detail. The pre-alignment of the model dataset and the second image data may include minimizing deviations, particularly mismatches, between first features. Therefore, the pre-alignment of the model dataset and the second image data may include alignment of the first features at a first level of detail. Furthermore, the pre-alignment of the model dataset and the second image data may include applying the first transformation rule to the model dataset and / or the second image data.

[0018] Alternatively or additionally, the pre-alignment of the model dataset and the second image data can be based on the acquisition form of the second image data relative to the object, particularly in the object's coordinate frame. Specifically, the pre-alignment of the model dataset and the second image data can be based on the acquisition form of the second image data about the examination area. The acquisition form of the second image data can be provided by a medical imaging device used to acquire the second image data. Alternatively, the acquisition form of the second image data can be detected by a tracking system (e.g., optical and / or electromagnetic and / or ultrasonic and / or mechanical sensors). The tracking system can be configured to detect the spatial position and / or orientation and / or pose on the medical imaging device for acquiring the second image data when acquiring the second image data about the object (particularly the examination area).

[0019] The acquisition of the second image data may include positioning information of the medical imaging device, particularly its spatial location and / or orientation (especially angle) and / or posture, which the medical imaging device uses to acquire second image data (regarding the object, particularly the examination area) at the time of acquisition (especially at a first level of detail). Specifically, the pre-alignment of the model dataset and the second image data may be based on the relative positioning between the medical imaging device and the object (especially the examination area), which the medical imaging device uses to acquire the second image data at the time of acquisition.

[0020] The model dataset and the second image data map a common portion of the examined region at least at or above the second level of detail. Registration of the model dataset and the second image data can be based on second features of a second class of features of the examined region, particularly geometric and / or anatomical features, which are mapped to the model dataset, particularly the first and second image data, at the second level of detail. The second class of features can include all features of the examined region, particularly all geometric and / or anatomical features, which are mappable and distinguishable, and particularly identifiable, at and above the second level of detail. Specifically, features of the second class of features below the second level of detail, particularly at the first level of detail, are not mappable or distinguishable. Furthermore, features of the second class of features can include small-scale, particularly micro-scale or low-millimeter-scale, anatomical landmarks of the examined region, such as microvessels, particularly fine vascular networks, and / or fibrosis of fibrotic liver tissue.

[0021] Furthermore, the registration of the model dataset and the second image data may include determining a second transformation rule for rigid and / or deformation transformations (particularly translation and / or rotation and / or deformation and / or scaling) of the model dataset and / or the second image data. Determining the second transformation rule may include determining measurements of the local stress and / or deformation and / or torsional states of the second features. Advantageously, the minimum accuracy of the registration between the model dataset and the second image data, particularly the minimum accuracy of the second transformation rule, may be equivalent to the second level of detail. The registration of the model dataset and the second image data may include minimizing deviations, particularly mismatches, between the second features. Therefore, the registration of the model dataset and the second image data may include aligning the second features at the second level of detail. Furthermore, the registration of the model dataset and the second image data may include applying the second transformation rule, particularly to pre-aligned model datasets and / or particularly pre-aligned second image data.

[0022] The provision of the resulting image data may include a graphical representation of the resulting image data stored on an electronically readable storage medium and / or transmitted to the providing unit and / or displayed by the display unit. Advantageously, the provision of the resulting image data includes providing a registration model dataset and / or second image data of the registration.

[0023] The proposed method can achieve accurate and robust registration between the model dataset and the second image data.

[0024] In a particularly preferred embodiment of the proposed method, a geometric and / or anatomical model and / or initial image data of the area to be examined may be received. Furthermore, the generation of the model dataset may include registering the first image data to the geometric and / or anatomical model and / or initial image data at a first level of detail based on other features or further features (mapped in the first image data and represented in the geometric and / or anatomical model and / or initial image data) of the first class of features.

[0025] Receiving initial image data of a geometric and / or anatomical model and / or examination area (particularly the object) may include collecting and / or retrieving data from an electronically readable storage medium and / or re-receiving data from a memory unit (particularly a database). Advantageously, the geometric and / or anatomical model of the examination area may include a general and / or specific representation of the object (particularly the examination area) at a first level of detail or above. Furthermore, the geometric and / or anatomical model may include a 2D or 3D representation of the geometric and / or anatomical object of the examination area, such as a centerline and / or vascular segments and / or vascular trees and / or a mesh model. Additionally, the initial image data may include a 2D or 3D mapping of the examination area. Initial image data may be received from at least one medical imaging device to obtain first and / or second image data, or from different medical imaging devices. The medical imaging devices used to acquire the initial image data may include magnetic resonance imaging (MRI) systems and / or positron emission tomography (PET) systems and / or ultrasound systems and / or medical X-ray systems and / or computed tomography (CT) systems and / or optical imaging systems, such as endoscopes and / or laparoscopes.

[0026] Registration of the first image data with the geometric and / or anatomical model and / or initial image data can be based on other features of the first type of features of the examined region, particularly geometric and / or anatomical features, which are mapped in the first image data and represented, particularly mapped or modeled, in the geometric and / or anatomical model and / or initial image data at a first level of detail. Furthermore, registration of the first image data with the geometric and / or anatomical model and / or initial image data can include determining further transformation rules for rigid and / or deformable transformations, particularly translation and / or rotation and / or deformation and / or scaling of the geometric and / or anatomical model and / or initial image data and / or the first image data. Advantageously, the minimum level of precision for registration of the first image data with the geometric and / or anatomical model and / or initial image data, particularly the minimum level of precision for further transformation rules, can be equivalent to the first level of detail. Registration of the first image data with the geometric and / or anatomical model and / or initial image data can include minimizing deviations between other features, particularly mismatches. Therefore, registration of the first image data with the geometric and / or anatomical model and / or initial image data can include the alignment of other features at the first level of detail. Furthermore, the registration of the first image data with the geometric and / or anatomical model and / or initial image data may include applying further transformation rules to the first image data. Advantageously, the model dataset may include the registered first image data and at least one of the geometric model, anatomical model, or initial image data.

[0027] Preferred embodiments may advantageously utilize geometric and / or anatomical models, particularly other features represented in the geometric and / or anatomical models, and / or as initial image data for 2D or 3D structures, which are used to reconstruct model datasets based on the spatial arrangement of the first image data, particularly the second features.

[0028] In a particularly preferred embodiment of the proposed method, the second type of features is unique above the first level of detail. Furthermore, pre-alignment of the model dataset and the second image data can provide a pre-alignment of the second features of the second type of features at the first level of detail, for registering the model dataset and the second image data at the second level of detail.

[0029] Advantageously, the second type of features can be unique, particularly explicit, at a level higher than the first level of detail, especially within the spatial range and / or temporal span corresponding to the spatial and / or temporal resolution characterizing the first level of detail. Conversely, the second type of features may be ambiguous at the first level of detail. Therefore, in order to explicitly align the model dataset and the second image data at the second level of detail, the second features of the second type of features need to be pre-aligned at the first level of detail.

[0030] The pre-alignment of the model dataset and the second image data can be based on the first-level features mapped to the first-level features in the model dataset and the second image data at the first level of detail. As a result of this pre-alignment, the second features of the second class of features (which are mapped to the model dataset and the second image data at the second level of detail) can also be pre-aligned at the first level of detail.

[0031] Advantageously, pre-alignment between the model dataset and the second image data at the first level of detail can facilitate the explicit identification of the corresponding mapping of the second feature at the second level of detail for registration of the model dataset and the second image data at the second level of detail. Here, the second feature can act as an explicit fingerprint, in particular an identifier, between its corresponding mapping in the model dataset and the second image data at the second level of detail.

[0032] In a particularly preferred embodiment of the proposed method, the first image data may include a plurality of first maps of the examined region, wherein each of the first maps maps at least one of the first features. Generation of the model dataset may include reconstructing the model dataset from the first maps.

[0033] Advantageously, the first mapping can be at least partially, and particularly completely, in different acquisition forms, such as different mapping directions and / or mapping locations, and / or mapping the inspection area at different first acquisition times. Furthermore, the first mapping can map at least partially different or at least partially overlapping portions of the inspection areas.

[0034] Furthermore, the generation of the model dataset can include 2D or 3D reconstruction based on an initial mapping of the examined regions. Specifically, the model dataset can be reconstructed from a first mapping using features of first and / or second types of features as a scaffold, said features being mapped in at least two first mappings, such as blood vessels. Reconstructing the model dataset from the first mapping can be based on the acquisition form of the first mapping, particularly relative to each other and / or relative to the object. Alternatively or additionally, the model dataset can be reconstructed from the first mapping based on common features of the examined regions mapped in at least two first mappings. Therefore, a better, particularly more accurate and / or more comprehensive mapping of the examined regions can be achieved in the model dataset.

[0035] In a particularly preferred embodiment of the proposed method, the first image data maps several at least partially distinct segments of the inspected region of the object. Furthermore, the registration of the model dataset and the second image data may include identifying at least one first mapping in the model dataset based on pre-alignment of the model dataset and the second image data, the at least one first mapping corresponding to a mapping of a common portion of the inspected region in the second image data.

[0036] Advantageously, the first image data can map several at least partially, and particularly completely, segments of the object's inspection area, especially spatial segments. Furthermore, each of the at least partially distinct segments can include a 2D portion of the inspection area, such as a slice, or a 3D portion, such as space. Advantageously, a combination of several at least partially distinct segments covers the inspection area. The at least partially distinct segments can have different or equal geometric quantities and / or dimensions.

[0037] Pre-alignment of the model dataset and the second image data at a first level of detail may include determining the spatial and / or temporal correspondence between the mappings of common parts of the inspection regions in the model dataset and the second image data. The spatial and / or temporal correspondence between the mappings of common parts of the inspection regions in the model dataset and the second image data includes the spatial and / or temporal correspondence between image points in the model dataset and the second image dataset that map the common parts of the inspection regions.

[0038] Since the model dataset is generated based on, and particularly reconstructed from, the first image data, at least one first mapping can be identified based on the pre-alignment of the model dataset and the second image data. Specifically, this involves examining the spatial and / or temporal correspondence between the common portions of regions in the mappings of the model dataset and the second image data. Advantageously, the mapping of features of the second class of features in the first image data (which is non-unique, and particularly ambiguous, below the first level of detail) can be narrowed down to the mapping of features of the second class of features in the at least one identified first mapping. Furthermore, these mappings of features of the second class of features in the at least one identified first mapping can include second features for registering the model dataset and the second image data. Therefore, more robust and efficient registration can be achieved between the model dataset and the second image data.

[0039] In a particularly preferred embodiment of the proposed method, the first mapping may 2D map the common portion of the inspected region using at least partially different acquisition methods. Furthermore, the generation of the model dataset may include reconstructing mappings of at least a portion of the first and / or second features in the model dataset from at least two of the first 2D mappings.

[0040] The first image data may include multiple first two-dimensional (2D) maps, particularly projection maps and / or slice maps, of a common portion of the inspection area. Furthermore, the multiple initial 2D maps may at least partially, and particularly completely, have different acquisition forms, such as different mapping orientations, specific angles, and / or different mapping positions and / or different field-of-view sizes and / or shapes. Advantageously, the first 2D maps may map the common portion of the inspection area from at least partially different mapping orientations and / or mapping positions. Furthermore, the first 2D maps may at least partially, and particularly completely, map different projections and / or slices of the common portion of the inspection area.

[0041] Furthermore, at least one of the first and / or second features in the first image data, particularly the mapping of all the first and / or second features, can be reconstructed from at least two of the first 2D mappings. If the second image data maps the common portion of the inspection area in three dimensions (3D), the reconstruction of the mapping of at least one of the first and / or second features can include a 3D reconstruction based on the back projections of at least two of the first two 2D mappings, particularly at least two of the two first 2D mappings. Alternatively, if the second image data can include at least one second 2D mapping, particularly several second 2D mappings, of the common portion of the inspection area, wherein the acquisition form of the first 2D mappings and at least one second 2D mapping of the common portion of the inspection area are at least partially different, the reconstruction can include a 3D reconstruction based on a temporary dataset of back projections of at least two of the first 2D mappings, particularly at least two of the first 2D mappings, and a subsequent 2D reconstruction based on the acquisition form of the second image data and the temporary dataset, particularly the forward projection.

[0042] Therefore, more robust registration can be achieved between the model dataset and the second image data.

[0043] In a particularly preferred embodiment of the proposed method, the second image data may include several second 2D maps of common portions of the areas examined under at least partially different acquisition methods. Furthermore, at least a portion of the first and / or second features in the second image data is reconstructed from at least two of the second 2D maps.

[0044] The second image data may include several second 2D maps of the common portion of the examined area, particularly projection maps and / or slice maps. Furthermore, the second 2D maps may at least partially, and especially completely, have different acquisition forms, such as different mapping directions (especially angles), and / or different mapping positions and / or different field-of-view sizes and / or shapes. Advantageously, the second 2D maps may map the common portion of the examined area from at least partially different mapping directions and / or mapping positions. Furthermore, the second 2D maps may at least partially, and especially completely, map different projections and / or slices of the common portion of the examined area.

[0045] Furthermore, at least one feature, particularly all of the first and / or second features, of the first and / or second features in the second image data can be reconstructed from at least two of the second 2D maps. If the first image data maps a common portion of the inspection area in 3D, the reconstruction of the mapping of at least one feature, the first and / or second features, can include 3D reconstruction based on at least two of the second 2D maps, particularly back projections of at least two of the second 2D maps. Alternatively, if the first image data includes at least one first 2D map, particularly multiple first 2D maps, of a common portion of the inspection area, wherein the acquisition forms of at least one first 2D map and the second 2D map of the common portion of the inspection area are at least partially different, the reconstruction structure can include 3D reconstruction based on a further provisional dataset of at least two of the second 2D maps, particularly back projections of at least two of the second 2D maps, and subsequent 2D reconstruction based on the acquisition form of the first image data and the further provisional dataset, particularly forward projection.

[0046] Therefore, more robust registration can be achieved between the model dataset and the second image data.

[0047] In a particularly preferred embodiment of the proposed method, the common portion of the examined region may include a first portion of the anatomical target. Furthermore, the second image data may, particularly exclusively, map a second portion of the anatomical target that is not mapped in the model dataset. Additionally, the registration of the model dataset and the second image data may include determining a first deformation rule for mapping the first portion of the anatomical target in the second image data based on a second feature. Furthermore, the registration of the model dataset and the second image data may include determining a second deformation rule for mapping the second portion of the anatomical target in the second image data by extrapolating the first deformation rule. Furthermore, the registration of the model dataset and the second image data may include applying the first and second deformation rules to the second image data.

[0048] The anatomical target may include organs, such as the liver and / or kidneys and / or hollow organs and / or tissues, particularly tumors. Advantageously, a first portion of the anatomical target may generally be mapped to both the model dataset and the second image data. The first portion may include a spatial segment of the anatomical target. Furthermore, a second portion of the anatomical target may be mapped to the second image data but not to the model dataset. Similarly, the second portion of the anatomical target may include another spatial segment of the anatomical target. Advantageously, the combined first and second portions of the anatomical target may cover the anatomical target. Furthermore, the first and second portions of the anatomical target may be spatially adjacent to each other.

[0049] Determining a first deformation rule for mapping a first portion of an anatomical target in second image data may include aligning second features mapped in the model dataset and the second image data at a second level of detail. Advantageously, at least one, particularly several or all, of the second features are geometric and / or anatomical features of the first portion of the anatomical target. The first deformation rule may include rules, particularly instructions, for performing rigid and / or deformable transformations, particularly translation and / or rotation and / or deformation and / or scaling, on the mapping of the first portion of the anatomical target and / or the second image data in the model dataset to align the second features of the first portion of the anatomical target.

[0050] The determination of a second deformation rule for mapping a second portion of an anatomical target in the second image data may include extrapolating a first deformation rule. This extrapolation may be based on the geometric relationship between the first and second portions of the anatomical target, particularly location and / or orientation, and / or anatomical relationships, particularly tissue and / or organ parameters, such as elasticity and / or tissue composition and / or vascularization. Alternatively or additionally, the extrapolation of the first deformation rule may be based on a biomechanical model of the anatomical target, which can be inferred from a model dataset and / or the second image data. Therefore, rules, particularly instructions, for rigid and / or deformable transformations of the first and / or second image data can be extrapolated to the mapping of the second portion of the anatomical target in the second image data.

[0051] Advantageous embodiments of the proposed method can advantageously allow the registration between the mapping of common parts of the examined area (in particular the first part of the anatomical target) in the model dataset and the second image data to extend to the second part of the anatomical object, which is mapped only in the second image data.

[0052] In a particularly preferred embodiment of the proposed method, providing the resulting image data may include mixing and / or overlaying and / or superimposing the registered second image data with the model dataset. Alternatively, providing the resulting image data may include mixing and / or overlaying and / or superimposing the registered model dataset with the second image data.

[0053] Providing the resulting image may include, in particular, weighted and / or regional blending (e.g., addition and / or multiplication), and / or, in particular, partially transparent overlay and / or overlay registered second image data with the model dataset. Alternatively, providing the resulting image may include, in particular, weighted and / or regional and / or global and / or pointwise blending (e.g., addition and / or multiplication) and / or, in particular, partially transparent overlay and / or overlay registered model dataset with the second image data.

[0054] Therefore, the resulting image can advantageously include features aligned with the model dataset and the second image data.

[0055] In a second aspect, the present invention relates to a providing unit configured to perform a method for providing resulting image data according to the invention.

[0056] The providing unit may include a computing unit, a storage unit, and / or an interface. Furthermore, the providing unit may be configured to perform embodiments of the proposed method for providing resulting image data according to the present invention, wherein the computing unit, storage unit, and / or interface are configured to perform their respective steps. In particular, the interface may be configured to receive first and second image data. Furthermore, the interface may be configured to provide resulting image data. Additionally, the computing and / or storage unit may be configured to generate a model dataset, a pre-aligned and registered model dataset, and second image data.

[0057] According to the present invention, all the statements and advantages of the above-described method for providing result image data also apply to the providing unit, and vice versa. According to the present invention, the additional features, advantages, and / or alternative embodiments of the above-described method for providing result image data may also be transferred to advantageous embodiments of the providing unit, and vice versa.

[0058] In a third aspect, the present invention relates to a system comprising a providing unit according to the invention, at least one medical imaging device, and a display unit. The at least one medical imaging device is configured to acquire first image data of an object including an examination region, wherein the first image data maps the examination region. Furthermore, the at least one medical imaging device is configured to acquire second image data of the object, wherein the first and second image data map at least one common portion of the examination region at a second level of detail. The providing unit is configured to generate a model dataset based on the first image data. Furthermore, the providing unit is configured to pre-align the model dataset and the second image data at a first level of detail, below a second level of detail, based on a first feature of a first type of feature of the examination region, wherein the first feature is mapped into the model dataset and the second image data at the first level of detail. Alternatively or additionally, the providing unit is configured to pre-align the model dataset and the second image data at a first level of detail based on a geometrical amount of the second image data relative to the acquisition of the object, particularly the examination region. Furthermore, the providing unit is configured to register the model dataset and the second image data at a second level of detail based on a second feature of a second type of feature of the examination region, wherein the second feature is mapped into the model dataset and the second image data at the second level of detail, wherein the second type of feature is capable of mapping at a second level of detail or higher. Furthermore, the providing unit is configured to provide registered second image data and / or registered model datasets as result image data. Additionally, the display unit is configured to display a graphical representation of the result image data.

[0059] According to the present invention, all the statements and advantages of the above-described method for providing resulting image data also apply to the system, and vice versa. According to the present invention, additional features, advantages, and / or alternative embodiments of the above-described method for providing resulting image data may also be transferred to advantageous embodiments of the system, and vice versa.

[0060] At least one medical imaging device may include a magnetic resonance imaging system (MRI) and / or a positron emission tomography (PET) system and / or an ultrasound system and / or a medical X-ray system and / or a computed tomography (CT) system and / or an optical imaging system as an imaging modality.

[0061] The display unit may include a display and / or a monitor and / or a screen and / or a projector, which is configured to visually display a graphical representation of the resulting image data.

[0062] In a particularly preferred embodiment of the proposed system, the system may include first and second medical imaging devices, wherein the first and second medical imaging devices are in different imaging modes. Furthermore, the first medical imaging device may be configured to acquire first image data. Additionally, the second medical imaging device may be configured to acquire second image data.

[0063] Advantageously, the first and second image data can each be acquired by a dedicated medical imaging device, particularly first and second medical imaging devices. As a result of different imaging modes, the first and / or second features can be mapped differently in the first and second image data, particularly features with different intensities and / or contrasts. Advantageously, the providing unit can be configured to identify the first and / or second features, such as shape and / or contour and / or pattern, based on geometric features typically mapped in the first and second image data.

[0064] In a particularly preferred embodiment of the proposed system, the first medical imaging device can be in an external imaging mode. Furthermore, the second medical imaging device can be in an intracavitary imaging mode.

[0065] The first medical imaging device can be configured to acquire first image data from outside the object, for example by detecting the transmitted and / or reflected portions of acoustic and / or electromagnetic waves after interaction with the examination area. The second medical imaging device can be a diagnostic and / or surgical instrument, such as an endoscope and / or laparoscope and / or bronchoscope and / or catheter, configured to be at least partially inserted into the object, particularly a cavity and / or hollow organ of the object. Advantageously, the second medical imaging device can be configured to acquire second image data from within the object, particularly from within the examination area.

[0066] In a fourth aspect, the present invention relates to a computer program product. The computer program product may include a computer program. For example, a computer program according to the invention may be directly loaded into the memory of a providing unit and includes a program scheme for performing steps of a method for providing resulting image data according to the invention if the computer program is executed in the providing unit. The computer program may be stored on an electronically readable storage medium, thereby including electronically readable control information stored therein. The control information includes at least one computer program according to the invention and is configured such that when the storage medium is used in the providing unit, the control information performs a method for providing resulting image data according to the invention. The electronically readable storage medium according to the invention may preferably be a non-transient medium, particularly a CD-ROM. The computer program product may include additional elements, such as documentation and / or add-ons, particularly a hardware dongle for using software.

[0067] Furthermore, the present invention can also be based on an electronically readable storage medium that stores electronically readable control information such that when the storage medium is used in a providing unit, the control information executes a method for providing resulting image data according to the present invention.

[0068] The software-based implementation has the following advantages: the previously used providing unit can be easily upgraded through software updates to perform the method for providing the resulting image data according to the present invention. Attached Figure Description

[0069] Further details and advantages of the present invention will become apparent from the following detailed description of preferred embodiments in conjunction with the accompanying drawings, in which:

[0070] Figures 1 to 5 Schematic diagrams are shown of different advantageous embodiments of the proposed method for providing resulting image data.

[0071] Figure 6 A schematic diagram of an embodiment of the proposed high-level labeling of the providing unit is shown.

[0072] Figures 7 to 8 Schematic diagrams of different advantageous embodiments of the proposed system are shown. Detailed Implementation

[0073] Figure 1A schematic diagram of an advantageous embodiment of the proposed method for providing Prov-RD result image data is shown. In a first step, pre-acquired first image data D1 of an object comprising an inspection region (REC-D1) can be received. Furthermore, the first image data D1 can be mapped to the inspection region. In a further step, a Gen-MD model dataset MD can be generated based on the first image data D1. In a further step, pre-acquired second image data D2 of an object (REC-D2) can be received. Furthermore, the model dataset MD and the second image data D2 can map at least one common portion of the inspection region at a second level of detail. In a further step, the model dataset MD and the second image data D2 are pre-aligned at a first level of detail (PREG-MD-D2) based on a first feature of a first class of features of the inspection region, which is mapped to the model dataset MD and the second image data D2 at the first level of detail. Alternatively or additionally, the model dataset MD and the second image data D2 are pre-aligned at the first level of detail based on the geometric quantities of the second image data D2 relative to the object, particularly the inspection region. In a further step, the model dataset MD and the second image data D2 are registered REG-MD-D2 at a second level of detail based on the second feature of the second type of features of the examined region. The second feature is mapped to the model dataset MD and the second image data D2 at the second level of detail. Advantageously, the second type of features can be mapped at the second level of detail or higher. In a further step, the registered second image data D2-REG and / or the registered model dataset MD-REG are provided as the resulting image data PROV-RD.

[0074] Advantageously, the second type of features can be unique above the first level of detail. Further, the pre-alignment PREG-MD-D2 of the model dataset MD and the second image data D2 can provide a pre-alignment of the second features of the second type of features at the first level of detail, for the registration Reg-MD-D2 of the model dataset MD and the second image data D2 at the second level of detail. Here, the second features can act as explicit fingerprints, specifically identifiers, between their corresponding mappings in the model dataset MD and the second image data D2 (at the second level of detail). The spatial extent of the uniqueness of the second type of features can depend on the precision level of the pre-alignment PREG-MD-D2 between the model dataset MD and the second image data D2, particularly the first level of detail. For example, if the pre-alignment PREG-MD-D2 is spatially accurate to 2 cm, then the second type of features need to be unique within a 2 cm spatial range, specifically the search space. Alternatively, multiple non-unique features of the second type of features can be combined based on a combination method to achieve uniqueness within the spatial range. The spatial extent of the uniqueness of the second type of features can be determined by matching each feature of the second type of features with all other features of the second type of features in the inspection region and measuring the spatial distance between the matching features. Alternatively, the spatial extent of the uniqueness of the second type of feature can be determined through statistical analysis of its complexity. Furthermore, the spatial extent of the uniqueness of the second type of feature can become larger with more complex geometric quantities and / or patterns of the features.

[0075] Furthermore, providing the resulting image data (prov-rd) may include mixing and / or overlaying and / or superimposing the registered second image data (D2-reg) with the model dataset (MD). Alternatively, providing the resulting image data (Prov-RD) may include mixing and / or overlaying and / or superimposing the registered model dataset (MD-REG) with the second image data (D2).

[0076] Figure 2 A schematic diagram of a further advantageous embodiment of the proposed method for providing Prov-RD result image data is shown, wherein the geometric and / or anatomical model MER and / or initial image data DP of the examined region can be received as REC-MER, REC-DP. Furthermore, the generation of the model dataset MD (Gen-MD) may include additional features based on a first class of features (which are mapped in the first image data D1 and represented at a first level of detail in the geometric and / or anatomical model MER and / or initial image data DP), registering the first image data D1 to the geometric and / or anatomical model MER and / or initial image data DP.

[0077] Figure 3A schematic diagram of a further advantageous embodiment of the proposed method for providing image data of Prov-RD results is shown, wherein the first image data D1 may include a plurality of first maps D1.M1 to D1.Mn of the examined region. Furthermore, the generation of the model dataset MD (Gen-MD) may include reconstructing the model dataset MD from the first maps D1.M1 to D1.MN.

[0078] Advantageously, the first mappings D1.M1 to D1.MN can map several at least partially different segments of the inspection region of the object. Further, the registration REG-MD-D2 of the model dataset MD and the second image data D2 can include: identifying at least one first mapping in the model dataset MD based on the pre-aligned PREG-MD-D2 of the model dataset MD and the second image data D2, the at least one first mapping corresponding to a mapping of a common portion of the inspection region in the second image data D2.

[0079] Figure 4 A schematic diagram of a further advantageous embodiment of the proposed method for providing Prov-RD result image data is shown, wherein each of the first maps D1.M1 to D1.MN two-dimensionally maps a common portion of the examined region having at least partially different acquired 2D geometric quantities. Furthermore, the generation of the model dataset MD (Gen-MD) may include a mapping in the model dataset MD of at least a portion of at least two reconstructed RECO-D1-F first and / or second features from the first maps D1.M1 to D1.MN.

[0080] Furthermore, the second image data D2 may include several second 2D maps D2.M1 to D2.Mk of a common portion of the inspection area, the common portion having at least partially different acquisition formats. Additionally, the mapping of at least a portion of the first and / or second features in the second image data D2 can be reconstructed from at least two of at least two of the second 2D maps D2.M1 to D2.Mk to reconstruct RECO-D2-F.

[0081] Figure 5A schematic representation of another advantageous embodiment of the proposed method for providing Prov-RD result image data is shown, wherein the examination region may include anatomical targets. Furthermore, a common portion of the examination region may include a first portion of the anatomical targets. Therefore, first image data D1 and second image data D2 may each map to the first portions of anatomical targets D1.AO1 and D2.AO1. Furthermore, second image data D2 may map to a second portion of anatomical target D2.AO2, which is not mapped in the model dataset MD. In particular, the second portion of anatomical target D2.AO2 is not included in the common portion of the examination region. Advantageously, the registration REG-MD-D2 of the model dataset MD and the second image data D2 may include a first deformation rule based on a second feature to determine that DET-DF1 maps to the first portion of the anatomical target in the second image data D2.AO1. Furthermore, the registration REG-MD-D2 of the model dataset and the second image data D2 may include a second deformation rule by extrapolating the first deformation rule to determine that DET-DF2 maps to the second portion of the anatomical target in the second image data D2.AO2. In addition, the registration REG-MD-D2 of the model dataset and the second image data D2 may include applying the first and second deformation rules to the second image data D2.

[0082] Figure 6 A schematic diagram of an advantageous embodiment of the proposed providing unit PRVS is shown. The providing unit PRVS may include a computing unit CU, a storage unit MU, and / or an interface. Furthermore, the providing unit PRVS can be configured to perform embodiments of the proposed method for providing PROV-RD result image data according to the invention, wherein the computing unit CU, the storage unit MU, and / or the interface IF are configured to perform their respective steps. In particular, the interface can be configured to receive first and second image data REC-D1 and REC-D2. Furthermore, the interface can be configured to provide PROV-RD result image data. Additionally, the computing unit CU and / or the storage unit MU can be configured to generate a GEN-MD model dataset MD, a pre-aligned PREG-MD-D2 and a registered REG-MD-D2 model dataset MD, and second image data D2.

[0083] Figure 7A schematic diagram of an advantageous embodiment of the proposed system is shown, wherein the system preferably includes a providing unit PRVS according to the invention, a first medical imaging device, a second medical imaging device, and a display unit 41. The first and second medical imaging devices can be different imaging methods. Specifically, the first medical imaging device can be implemented as a medical C-arm X-ray system 37, which can be configured to acquire first image data D1 of an object 31 including an examination region ER. Furthermore, the second medical imaging device can be implemented as a medical ultrasound device IU1, which is configured to acquire second image data D2 of the object 31. The first image data D1 and the second image data D2 can advantageously map at least a common portion of the examination region ER at a second level of detail.

[0084] The medical C-arm X-ray system 37 may include an X-ray detector 34 and an X-ray source 33, which may be mounted on the C-arm 38 of the C-arm X-ray system 37 such that they are movable, particularly rotatable, about at least one axis. Furthermore, the medical C-arm X-ray system 37 may include a motion unit 39, particularly including at least one wheel and / or track and / or robotic system, which allows spatial movement of the medical C-arm X-ray system 37. To acquire first image data D1 of the object 31, particularly including at least one projected image of the object 31, a providing unit PRVS may send a signal 24 to the X-ray source 33. Thus, the X-ray source 33 may emit an X-ray beam, particularly a cone beam and / or fan beam and / or parallel beam. When the X-ray beam is inserted into the surface of the X-ray detector 34 after interaction between the X-ray beam and the examined region ER of the object 31, the X-ray detector 34 may send a signal 21 to the providing unit PRVS, which depends on the detected X-ray. Based on signal 21, the providing unit PRVS may be configured to receive the first image data D1.

[0085] The medical ultrasound device UI1 may include at least one ultrasound transducer. Specifically, the medical ultrasound device UI1 may include multiple ultrasound transducers, which may be spatially arranged in a ring, particularly an ellipse or circle, a row, an array, and / or a matrix. At least one ultrasound transducer may be configured to transmit an ultrasound field to the object 31, particularly the examination region ER, via a coupling medium (e.g., gel). Furthermore, at least one ultrasound transducer may be configured to detect reflected and / or transmitted portions of the ultrasound field after interaction between the ultrasound field and the object 31, particularly the examination region ER. Advantageously, the medical ultrasound device UI1 may be configured to provide a signal 36 based on the receiving portion of the ultrasound field. Based on signal 36, the providing unit PRVS may be configured to receive second image data D2.

[0086] The providing unit PRVS can be configured to generate a model dataset MD based on first image data D1. The providing unit PRVS is configured to pre-align the model dataset MD and the second image data D2 at a first level of detail (below a second level of detail) based on a first feature of a first class of features of the inspection region ER mapped at a first level of detail in the model dataset MD and the second image data D2. Alternatively or additionally, the providing unit PRVS is configured to pre-align the model dataset MD and the second image data D2 at a first level of detail based on geometric quantities of the second image data D2 relative to the object, particularly the inspection region. In a further step, the providing unit PRVS is configured to register the model dataset MD and the second image data D2 at a second level of detail based on a second feature of a second class of features of the inspection region, the second feature being mapped to the model dataset MD and the second image data D2 at the second level of detail. Advantageously, the second class of features can be mapped at a second level of detail or higher. In particular, the PRVS unit is configured to provide the registered second image data D2-REG and / or the registered model dataset MD-REG as the result image data to the display unit 41 via signal 25.

[0087] Display unit 41 may include a display and / or monitor configured to display a graphical representation of the resulting image data. The system may further include an input unit 42, particularly a keyboard. Input unit 42 may advantageously be integrated into display unit 41, for example as a capacitive and / or resistive touch display. Input unit 42 may be configured to acquire user input, such as from medical personnel. Furthermore, providing unit PRVS may be configured to receive user input from input unit 42 via signal 26. Providing unit PRVS may be configured to control the acquisition of first image data D1 and further image data D2 via medical C-arm X-ray system 37 based on user input, particularly based on signal 26.

[0088] Figure 8 A schematic diagram of a further advantageous embodiment of the proposed system is shown, wherein the first medical imaging device can be in an in vitro imaging mode. (As shown in...) Figure 7As shown, the first medical imaging device can be embodied as a medical C-arm X-ray system 37, which can be configured to acquire first image data D1 from outside the object. Furthermore, the second medical imaging device IU2 can be embodied as an intracavitary imaging modality, particularly diagnostic and / or surgical instruments, such as endoscopes and / or laparoscopes and / or bronchoscopes and / or catheters, configured to be at least partially inserted into the object 31, particularly into the cavity and / or hollow organ of the object 31. The second medical imaging device IU2 can be configured to acquire second image data D2 from within the object 31, particularly from within and / or adjacent to the examination area ER. The second imaging device IU2 can be configured to provide the second image data D2 to the providing unit PRVS via signal 36.

[0089] While the invention has been described in detail with reference to preferred embodiments, it is not limited to the disclosed examples, and those skilled in the art can derive other variations from the disclosed examples without departing from the scope of the invention. Furthermore, the use of indefinite articles such as “a” and / or “an” does not exclude multiples of the respective features. Moreover, terms such as “unit” and “element” do not exclude the possibility that individual components may comprise multiple interacting sub-components, wherein the sub-components may be further spatially unrelated.

Claims

1. A method for providing result image data (PROV-RD), the method comprising: - Receive (REC-D2) pre-acquired first image data (D1) of an object (31) including an inspection region (ER), wherein the first image data (D1) maps the inspection region (ER). - Generate a model dataset (MD) based on the first image data (D1). - Receive (REC-D2) pre-acquired second image data (D2) of the object (31), wherein the model dataset (MD) and the second image data (D2) map at least one common portion of the inspection region (ER) at a second level of detail. - based on: - The first feature of the first type of feature of the inspection region (ER), the first feature being mapped at a first level of detail to the model dataset (MD) and the second image data (D2), and / or - Regarding the acquisition format of the second image data (D2) of the object, The model dataset (MD) and the second image data (D2) are pre-aligned at a first level of detail below the second level of detail (PREG-MD-D2). - Based on the second type of features of the inspection region (ER), the model dataset (MD) and the second image data (D2) are registered at a second level of detail (REG-MD-D2). The second feature is mapped to the model dataset (MD) and the second image data (D2) at the second level of detail, wherein the second type of feature can be mapped at the second level of detail or higher. - Provide (PROV-RD) registered second image data (D2-REG) and / or registered model dataset (MD-REG) as the result image data.

2. The method according to claim 1, characterized in that, Receive (REC-MER, REC-DP) the geometric and / or anatomical model (MER) and / or initial image data (DP) of the examined region (ER). The generation of the model dataset (MD) (GEN-MD) includes registering the first image data (D1) with respect to the geometric and / or anatomical model (MER) and / or the initial image data (DP) based on other features of the first class of features, which are mapped in the first image data (D1) and represented at a first level of detail in the geometric and / or anatomical model (MER) and / or the initial image data (DP).

3. The method according to claim 1 or 2, characterized in that, The second type of feature is unique above the first level of detail. The pre-alignment (PREG-MD-D2) of the model dataset (MD) and the second image data (D2) provides a pre-alignment of the second features of the second class at the first level of detail, which is used for registration (REG-MD-D2) of the model dataset (MD) and the second image data (D2) at the second level of detail.

4. The method according to any one of the preceding claims, characterized in that, The first image data (D1) includes multiple first maps (D1.M1, D1.MN) of the inspection region (ER). The generation of the model dataset (MD) (GEN-MD) involves reconstructing the model dataset (MD) from the first mapping (D1.M1, D1.MN).

5. The method according to claim 4, characterized in that, The first mapping (D1.M1, D1.MN) maps to several at least partially different segments of the inspection region (ER) of the mapping object (31). The registration (REG-MD-D2) of the model dataset (MD) and the second image data (D2) includes recognizing at least one first mapping in the model dataset (MD) based on the pre-alignment (PREG-MD-D2) of the model dataset (MD) and the second image data (D2), the at least one first mapping corresponding to the mapping of the common part of the inspection region (ER) in the second image data (D2).

6. The method according to claim 4 or 5, characterized in that, The first mapping (D1.M1, D1.MN) respectively uses at least partially different acquisition methods to two-dimensionally map the common part of the inspection region (ER). The generation of the model dataset (MD) (GEN-MD) includes the mapping of at least a portion of the first and / or second features in the mapping model dataset (MD) by reconstructing (RECO-D1-F) the first and / or second features from at least two first mappings (D1.M1, D1.MN).

7. The method according to any one of the preceding claims, characterized in that, The second image data (D2) includes several second two-dimensional maps (D2.M1, D2.MK) of the common portion of the inspection region (ER) under at least partially different acquisition formats. The mapping of at least two of the first and / or second features in the second two-dimensional mapping (D2.M1, D2.MK) is performed in the second image data (D2).

8. The method according to any one of the preceding claims, characterized in that, The common portion of the examination area (ER) includes the first part of the anatomical target. The second image data (D2) maps to the second part of the anatomical target, which is not mapped to the model dataset (MD). The registration (REG-MD-D2) of the model dataset (MD) and the second image data (D2) includes: - Based on the second feature, determine (DET-DF1) a first deformation rule for mapping the first part of the anatomical target to the second image data (D2). - A second deformation rule (DET-DF2) for mapping the second part of the anatomical target to the second image data (D2) is determined by extrapolating the first deformation rule. - Apply the first and second deformation rules to the second image data (D2).

9. The method according to any one of the preceding claims, characterized in that, The image data provided for the (PROV-RD) results include: - Second image data (D2-REG) with mixed and / or overlay and / or superimposed registration and model dataset (MD) or - Model dataset (MD-REG) with mixed and / or overlay and / or superimposed registration and second image data (D2).

10. A provisioning unit (PRVS) comprising a computing unit, a storage unit, and an interface, characterized in that, The providing unit can be configured to perform the method according to any one of claims 1 to 9, wherein the computing unit and / or storage unit is configured to generate a model dataset, a pre-aligned and registered model dataset, and a second image data, and the interface is configured to receive the first image data and the second image data and / or provide the resulting image data.

11. A system comprising a providing unit (PRVS), at least one medical imaging device, and a display unit (41), At least one of the medical imaging devices is configured as follows: - Obtain first image data (D1) of the object (31) including the inspection region (ER), wherein the first image data (D1) maps to the inspection region (ER). - Obtain second image data (D2) of the object (31), wherein the first image data (D1) and the second image data (D2) map at least one common portion of the inspection region (ER) at a second level of detail. The provided unit (PRVS) configuration is as follows: - Generate a model dataset (MD) based on the first image data (D1). - based on: - The first feature of the first type of feature of the inspection region (ER), the first feature being mapped at a first level of detail to the model dataset (MD) and the second image data (D2), and / or - Regarding the acquisition format of the second image data (D2) of the object, The model dataset (MD) and the second image data (D2) are pre-aligned at a first level of detail below the second level of detail (PREG-MD-D2). - Based on the second type of features of the inspection region (ER), the model dataset (MD) and the second image data (D2) are registered at a second level of detail (REG-MD-D2). The second feature is mapped to the model dataset (MD) and the second image data (D2) at the second level of detail, wherein the second type of feature can be mapped at the second level of detail or higher. - Provide the registered second image data (D2-REG) and / or the registered model dataset (MD-REG) as the result image data. The display unit (41) is configured to display a graphical representation of the resulting image data.

12. The system according to claim 11, The system includes first and second medical imaging devices. The first and second medical imaging devices use different imaging modes. The first medical imaging device is configured to acquire first image data (D1). The second medical imaging device is configured to acquire second image data (D2).

13. The system according to claim 12, The first medical imaging device is an external imaging mode. The second medical imaging device is an intracavitary imaging mode.

14. A computer program product comprising a program that can be directly loaded into the memory of a programmable controller of a providing unit (PRVS), having a program code portion for performing all steps of the method according to any one of claims 1 to 9 when the program is executed in the programmable controller of the providing unit (PRVS).

Images