Patent Description:
In medical applications, a target region of a patient is often imaged with two or more different modalities to obtain additional information which one single modality would not be able to depict. Spatially aligning complementary information from two or more image modalities has a wide range of applications including diagnostics, planning, simulation and in particular guidance. The registration of image data sets from different modalities has been extensively studied and many solutions have been proposed. Many of these approaches require at least some of the same anatomy or landmarks to be visible in both modalities. This cross-modality information is used to perform the registration. However, in many cases, there is little or no cross modality information such that a registration cannot be based on it. Thus, known methods such as using landmarks, image intensity, mutual information, gradient based approaches and learning similarity functions, cannot be applied.

In image guided interventions, such as cardiac resynchronization therapy ("CRT"), pre-operative MR or SPECT images are used to analyse tissue characteristics or function and intraoperative X-ray fluoroscopy is used to guide the procedure. The pre- and intra-operative modalities are fundamentally different and do not share significant cross-modality information. In such cases, alternative registration strategies are required.

In a paper by <NPL>), it has been proposed to use fiducial markers and optical tracking devices for registration in cardiac resynchronization therapy. However, this approach requires pre-operative and MR imaging immediately before the procedure and additional hardware in the operating room. Additionally, anatomical registration has been proposed where the position of the vessels is inferred from catheters and aligned to vessels segmented from pre-operative images (see for example <NPL>) and <NPL>)). However, catheters can deform the vessels and the resolution of magnetic resonance may be too low to accurately segment the vessels.

In the scope of planning and visualizing, <NPL>, have proposed to register a pre-operative SPECT image data set to fluoroscopy image data sets by manually matching landmarks, namely interventricu-lar grooves to coronary artery vessels, performing an iterative closest point (ICP) refinement and finally do a nonlinear warping to gain an image with fused information to visualize the left ventricle and its surroundings, in particular for planning purposes. This method is dependent on accurately identifying landmarks in pre-operative data which is challenging as variations of the anatomy may result in inaccuracies. Additionally, an accurate registration between a pre-operative first image data set and a second image data set is not achieved because of these inaccuracies and nonlinear warping steps to improve the visualization. The method cannot be used for image guidance during an intervention anyway, since manual placement of blood vessels in the respective grooves has to be performed to enable ICP finding a suitable starting point.

<CIT> discloses a method for aligning sets of medical image data. First and second sets of image data are obtained respectively, using first and second different medical imaging modalities. For each set, an axis of an anatomical feature and a landmark point for the anatomical feature is determined, and the first and second sets are aligned by comparing the respective axes and landmark points.

<CIT> discloses a method for synthesizing 3D multimodality image sets into a single composite image. Surfaces are initially extracted from two or more different images to be matched. The matching process involves efficiently adjusting the surfaces to find the best fit among them.

<CIT> discloses a method for combining human facial information with 3D magnetic resonance brain images. An artificial face surface scan is created from 3D magnetic resonance images. The artificial face surface scan is then aligned to an actual face surface scan generated by a laser scanner.

It is an object of the present invention to provide a novel approach for registering multi-modal images sharing not enough common information to base a registration thereon, which can be performed fully automatic and results in an accurate registration information which can also be used for image-based guidance during interventions.

This object is achieved by a method according to claim <NUM>.

The inventive method for registration of a first image data set and a second image data set of a target region of a patient, wherein the first and the second image data sets have been acquired using different imaging modalities, comprises the following steps:.

wherein a boundary condition describing possible motion of the patient between acquisitions of the first and the second image data set is used to reduce the parameter space of the optimization of the transformation parameters.

In many cases, the first anatomical structure will only be visible in the first image data set, while the second anatomical structure is only visible in the second image data set. This invention, however, also encompasses the case that anatomical structures may be at least partially visible in both image data sets, but the in particular automatically deducible information is not sufficient to facilitate the registration. In other words, neither information concerning the first anatomical structure nor information concerning the second anatomical structure determined by evaluating the image data sets is sufficient to enable registration of the image data sets on its own.

The invention exploits the fact that even if there are no anatomical structures visible in both image data sets to facilitate a registration, there may be anatomical structures whose geometrical relationship may be at least partly, that is, for extended segments, known. To facilitate a registration process, it is not sufficient to have a point correspondence or a known geometrical relationship for a single point, but the segments have to be extended, wherein preferably areas, in particular surfaces, are used as segments. If at least geometrical information describing the segments of the first and second anatomical structures, for which the geometrical relationship is known, can be calculated accurately enough from the two image data sets, the segments can be aligned according to the geometrical relationship in a suitable optimization process to yield transformation parameters describing the transformation between the two image data sets and thus, enabling the calculation of registration information to register the image data sets, and thus, also facilitate registration between images subsequently acquired using the image device of the second image data set as long as the patient does not move in a relevant manner.

The invention is, of course, also applicable to the registration of more than two image data sets and/or more than two modalities, for example having multiple second image data sets or even a third image data sets, wherein geometrical relationships encompassing three anatomical structures are used. An application of the method of the invention also includes image acquired over time, in particular a 4D data set like CINE MRI, where a single first image data set and its first anatomical structure can be used for registration to all partial image data sets of a temporal sequence as second image data sets.

In this and the following, image data set and imaging modality should be broadly interpreted as also encompassing other methods to gather multidimensional information on the geometry of anatomical structures and the resulting multidimensional anatomical datasets describing these geometries. For example, these modalities can include electro-anatomical mapping methods and related methods. In these cases it is possible that the image data sets already only contain the anatomical structures, simplifying segmentation, and/or that they already include point cloud representations and/or surface representations, in particular meshes. The image data set may, of course, already have been derived from at least one original image data set, for example by using CAD (Computer Aided Diagnostics) and/or as a parametrized model.

Additionally, the described registration process is not only applicable to different modalities, but also to different imaging techniques using the same modality. For example, the first image data set may be a contrast-enhanced x-ray image data set, in particular a digital subtraction angiography (DSA) image data set, and the second image data set may be a non-contrast-enhanced image data set.

As the registration process is fully automatic and can preferably be realized in real-time, no manual interaction is required and the registration information can be used advantageously for image guidance in medical interventions in the target region of the patient. As only the geometry of the segments and the known geometrical relationship has to be known, the method has a broad spectrum of applications and can for example also be used for magnetic resonance image data sets which may have a lower spatial resolution such that for example the geometry of extended areas or surfaces can be deduced, but finer structures, such as grooves, wherein other anatomical structures lie, cannot be seen.

In an advantageous embodiment, the known geometrical relationship describes at least partly parallel or touching or intersecting surfaces of the anatomical structures. In particular, adjacent anatomical structures are used, which share at least partly parallel, intersecting or touching surfaces as extended segments, for which the geometrical relationship is known. The core of the proposed registration approach in this embodiment is the use of anatomical structures that are adjacent or share a common surface.

Preferably, the first anatomical structure is a tissue layer delimiting an organ and/or organ part, or adjacent to a secondary tissue layer delimiting an organ and/or organ part, in particular the myocardium or the epicardium, and the second anatomical structure is a blood vessel structure and/or parallel to the surface of the organ. Parallel in this case can be understood as at least essentially following the curvature of the surface. In particular, the first anatomical structure may, in cardiac anatomy, be the epicardial surface of the left ventricle (LV), which is adjacent to the coronary sinus (CS) blood vessel tree as a second anatomical structure. The left ventricle is visible in the operative magnetic resonance image data sets, but the coronary sinus blood vessel tree is not. The blood vessel tree is visible during contrast enhanced x-ray fluoroscopy, however, the left ventricle is not. Since it is known that the coronary sinus blood vessel tree extends parallel or on the epicardial surface of the left ventricle, this prior anatomical knowledge, the known geometrical relationship, can be exploited to register multi-modal images without cross-modality image information.

While the cardiac anatomy, in particular the left ventricle, will in the following often be used to explain concepts of the current invention, the method described is also applicable in other areas of the human body, in particular for different anatomical structures. For example, for other organs like the liver or the kidneys, it is also known that blood vessel structures are found on the surface of these organs. This also holds to for certain kinds of tumors. Another area of application for the described method is the brain anatomy, where for example gray matter is adjacent to the meninx (cerebral membrane) and its blood vessels. Other examples include registering the spine (holes in vertebrae) to the spinal cord or to nerves extending through the spinal cord, and registration of the liver to the diaphragm.

In preferred embodiments, the first image data set can be a magnetic resonance image data set and the second image data set can be an x-ray image data set. The x-ray image data set can preferably be a fluoroscopy image data set, in particular a biplane fluoroscopy image data set or a rotational angiographic x-ray image data set recorded during or at the onset of an intervention, as will be further discussed below in detail. For example, a biplane x-ray device having c-arms could be used.

The first geometry information may be determined using tissue segmentation. For example, for magnetic resonance image data sets, multiple algorithms have been proposed to segment certain types of tissue, for example myocardial tissue. In an embodiment, the epicardial contour can be detected in a magnetic resonance data set using a combination of machine learning landmark detection and gray level analysis, as for example described in an article by <NPL>). A mesh can then be fit to contours to generate a surface representation of the left ventricle epicardium at end diastole. For other areas of application, in particular brain anatomy, similar algorithms are known and have been proposed.

The second geometry information may be determined using vessel segmentation on the second image data set, which in particular has been acquired using a contrast agent. The imaging of blood vessels is commonly performed using x-ray techniques. While in rotational angiography x-ray image data sets corresponding pixels showing the same blood vessel are easier to detect, it is also possible to achieve automatic determination of the geometry information for two x-ray projections imaged in two different planes, i.e. biplane fluoroscopy image data sets.

Thus, in an embodiment of the invention, when reconstructing a blood vessel structure as second anatomical structure from multiple two dimensional x-ray images of the second image data set, at least one specific point identifiable in each of the x-ray images, in particular at least one specific point of a medical instrument used to temporarily block a blood vessel and/or a specific point of a vessel bifurcation, is detected in all x-ray images and, using the specific point as a starting point, pixels corresponding to the same blood vessel are detected using the constraint that the blood vessel tree is interconnected. While in the past, it has been difficult to reconstruct a blood vessel tree automatically from a few two dimensional fluoroscopy images, such that in many cases manual interaction was required to find corresponding blood vessels in all projections, recently techniques have been proposed which allow automatic segmentation of blood vessel trees also in sparse sets of projections, using specific points which can be detected in all two dimensional images of the image data set as a starting point and exploiting the fact that the blood vessels are all interconnected. In this manner, even if, in particular in image guidance situations during interventions, only a few different projections are available as two dimensional x-ray images, in particular two two-dimensional images in biplane fluoroscopy, a completely automatic registration process including the segmentation of the anatomical structures can be achieved in a reliable way.

The specific point can be a feature point of a medical instrument, for example a catheter. In CRT, balloon catheters are often used to supply contrast agent and/or prevent reflux of the contrast agent from the vessels to be contrasted. Such a medical instrument can be automatically detected in all projections of a fluoroscopy image data set and thus supply a specific point from where to start the reconstruction of the vessel tree. For example, the detection of catheters in biplane fluoroscopy has been described in an article by <NPL>. It has also been proposed to localize a balloon catheter based on a support-vector-machine approach by <NPL>.

Methods as these can also be applied to localize specific points for the reconstruction of a blood vessel tree according to the current invention. Alternatively or additionally, specific points of automatically detectable and identifiable bifurcations can be used. Regarding the reconstruction of the blood vessel tree starting at a specific point in the blood vessel tree, reconstruction methods for curvilinear structures from two views have also been discussed, see, for example, <NPL>), and can also be applied to the reconstruction of blood vessel trees, taking into account the boundary condition that the blood vessels are interconnected.

Generally, in a preferred embodiment of the current invention, the first and/or second geometry information comprises a point cloud representation and/or a surface representation, in particular a mesh. Such representations of the segments, for which a known geometrical relationship exists, can with special advantage be used when adjacent parallel surfaces are evaluated. In particular, in these cases, the method of the invention preferably performs point-to-point-registration or point-to-plane registration, registering point clouds with each other or point clouds with a surface.

Many algorithms registering point cloud representations or surface representations as such require carefully chosen initial sets of parameters since they usually only detect local minima of the error functions or deviation functions. The current invention exploits the fact that that recently so-called globally optimized (Go) algorithms have been proposed that allow finding global minima of such registration problems by combining these algorithms with global search algorithms.

Thus, in an especially preferred embodiment of the current invention, a globally optimized registration algorithm is used for optimizing the transformation parameters, in particular a globally optimized iterative closest point algorithm (Go-ICP). While using an ICP-algorithm is preferred, other algorithms can also be used, for example Coherent Point Drift algorithms (CPD), as described for example by <NPL>. In an article by <NPL>), a branch and bound (BnB) algorithm is combined with an ICP algorithm to overcome their individual weaknesses, thus to provide a fast globally optimal solution. This Go-ICP algorithm guarantees convergence to the globally optimal solution. It is additionally much more efficient than the standard BnB algorithm, since even if it explores the whole possible solution space, it refines the intermediate results with the ICP method, thus benefiting from the good attributes of both algorithms.

For example, if a reconstructed blood vessel structure, is used wherein the geometry information is a point cloud representation, the registration problem can be described as a partial point cloud matching problem with unknown point correspondences. The rigid transformation can be described as a rotation and a translation, however, also the correspondences of features, for example points to vertices has to be taken into account. If the optimal rotation and translation was known, the correspondences could be found easily and if the correspondences were known, the optimal rotation and transformation would be easy to calculate. This problem of unknowns is solved by the ICP algorithm, which always finds the nearest local minimum, however, additionally using the BnB algorithm as proposed by J. Yang et al in their Go-ICP algorithm yields the globally optimal solution in a fast and reliable manner.

It is noted that, if one of the anatomical structures is a blood vessel structure extending on a surface of the other anatomical structure, the blood vessel structure can of course be evaluated regarding the surfaces of the blood vessels oriented in the direction of the surface such that a point cloud can be reduced for registration purposes.

According to the invention, a boundary condition describing possible motion of the patient between acquisitions of the first and the second image data set is used to reduce the parameter space of the optimization of the transformation parameters. The boundary condition can be determined considering known positioning information of the patient during acquisition of the first and second image data set. In this manner, information about the positioning of the patient can be used to reduce the parameter space to be searched in the course of the registration. For example, if the patient has been imaged in both cases in the supine position, transformations assuming a turning of the patient to a prone position can be excluded.

Preferably, at least one correlation information regarding the first and the second image data set, which is relevant regarding the registration, but not sufficient for registering the image data sets, is used as a part of an optimization target function and/or as a boundary condition while optimizing the transformation parameters and/or for refining the registration information. In particular, the correlation information can comprise a point correspondence or a line correspondence. Even in cases in which extended anatomical structures cannot be fully visible in the image data sets of both modalities, there may be some mutual information which alone is not sufficient to register the image data sets, but can of course be used to increase the accuracy of the registration, check the plausibility of the registration and/or enable a faster computing process. The method is improved by involving information that is common in the two modalities. For example, in cardiac registration, some parts of the vascular tree can even be identified in a magnetic resonance image data set. In another example, the shadow of the heart border, in particular the border of the left ventricle, can be identifiable in interventional fluoroscopy images of an X-ray image data set. Thus, in an concrete embodiment, the registration information can be refined by an adaptation to such features identifiable in an interventional image, for example the shadow of the left ventricle.

Some target regions of patients may also be subjected to motion during acquisition of at least one of the image data sets. Preferably, when parts of at least one of the image data sets are acquired in different motion states of a patient motion, in particular an at least partly periodical motion, a motion state of at least one part of the image data set is selected and parts acquired during other motion states are transformed to the selected motion state. While, in a cardiac application, the images are commonly acquired during a breath hold state of the patient, the periodical heart motion still has an influence on parts of the image data set. If an image data set comprises parts acquired in different motion states, the motion is preferably extracted such that all parts of the image data set can be converted to a certain, predetermined, selected motion state. In particular, a motion model, in particular based on a primary component analysis of the motion, can be used to describe the motion states and transformations between motion states. An approach using masked principle component analysis motion gating is described by <NPL>).

As already mentioned, an advantageous area of application of the fully automatic registration of multi-modal image data sets is during image-guided interventions. Thus, the registration information is preferably used in calculating a fusion image for image guidance during a medical interventional procedure, in particular in a method for image guidance during an interventional medical procedure in the target region, wherein the registration information is used in generating a displayed fusion image incorporating information from both image data sets, or, preferably, if the modality of the second image data set is the interventional modality, incorporating information from or associated with the first image data set and information from subsequent images of the imaging device of the second image data set, at least as long as the patient did not or only irrelevantly move. Commonly, the first image data set, for example a magnetic resonance image data set, will be a pre-operative image data set which already been used for planning of the interventional procedure. The second image data set in such a case comprises interventional images, for example fluoroscopy images, which can depict the used medical instruments, for example in a blood vessel structure in the target region of the patient. It is thus advantageous to calculate a fusion image by using at least a part of each image data set and/or subsequent images of the imaging device of the second image data set using the registration information. In particular, parts of a pre-operative image data set can be overlaid onto interventional images of the interventional modality. Fusion images can also comprise information deducted from the image data sets, for example results of a planning stage preceding the interventional procedure. Markers and/or additional information relating to the first image data set can be overlaid onto image data from the second image data set or subsequent images, which for example are no longer contrast enhanced. In this manner, accurate guidance during a medical interventional procedure is achieved.

In this context, it is also preferred when during an interventional procedure in the target region the second image data set is reacquired at least once and with each reacquisition of the second image data set the registration information is updated, in particular based on the previously determined registration information and/or in real time. As the registration process proposed by the current invention is running fully automatically, it is possible to update the registration information in real time, for example by rerunning the registration process using the most recently acquired interventional image data also showing the second anatomical structure. In case the second image data set comprises contrast enhanced image date, a contrast enhanced reacquisition can be initiated if the registration information is to be updated, while the image guidance used subsequent non-contrast-enhanced images of the second modality. For example, if a patient motion is observed, a new contrast-enhanced acquisition can be triggered automatically or initiated by a user. However, alternatively, the above-mentioned sparse common information between the modalities can also be used to update the registration information by refining using this cross-modality information, which may not suffice for registering image data sets, but for updating an already known registration information, in particular, from non-contrast-enhanced images of the second modality. It is noted that when using algorithms like Go-ICP, using the previously calculated registration information as a starting point is not necessarily necessary since the whole parameter space (if not reduced as in embodiments described above) is searched.

A registration device can perfom registration using the inventive method of a first image data set and a second image data set of a target region of a patient, wherein the first and the second image data sets have been acquired using different imaging modalities or using different imaging techniques on the same imaging modality, the registration device comprising.

In other words, the registration device is adapted to perform the inventive method. Consequently, all remarks and features concerning the inventive method are also applicable to the registration device. The registration device can comprise a computer and/or at least one processor, wherein the units described can be implemented as hardware components and/or software components.

A computer program can execute the steps of the inventive method when the computer program is executed on a computer. The computer program may be stored on an electronically readable storage medium according, on which the computer program is stored. The electronically readable storage medium may be a non-transitory storage medium, for example a CD.

In summary, the described invention reduces the difficulty of the registration process significantly, especially by omitting manual steps. The registration process becomes a "one click" process. The registration time is reduced with a potential of increased accuracy. The proposed registration process is able to register multi-modal images that do not share cross modality information/landmarks and is able to automatically update the registration in case of patient movement. The registration process is fully automated and finds the optimal solution of the registration problem. It is particularly advantageous for CRT interventions and can in general open up a new way of intermodality registration for multiple other fields of application.

Further advantages and details of the current invention are apparent from the following description of the preferred embodiments in conjunction with the drawings, in which.

<FIG> is a flow chart of an embodiment of a method according to the current invention, which is used for image guidance during a medical interventional procedure, in this case a CRT procedure. Pre-operatively, a magnetic resonance image data set has been acquired as a first image data set. Additionally to the mentioned anatomical magnetic resonance image data set, a functional magnetic resonance image data set has been acquired to locate lesions in the left ventricle. The anatomical image data set has been used along with the functional magnetic resonance image data set to plan the intervention, in particular to locate lesions and intervention locations on the epicardial surface of the myocardium.

During the actual intervention, a minimally invasive medical instrument, in particular a catheter, is used. To be able to guide the intervention, a biplane angiographic imaging device is used to acquire fluoroscopic images in different angulations showing the target region of the patient, especially the coronary sinus blood vessel tree due to application of a contrast agent. Two dimensional fluoroscopic images are also used to show the medical instrument. All these images form a second image data set.

The pre- and intra-operative modalities are fundamentally different such that the first image data set and at the second image data set do not share significant cross-modality information. However, for image guidance during the interventional procedure, a registration is needed to be able to accurately overlay information from the pre-operative first magnetic resonance image data set and/or planning information derived from the pre-operative imaging onto the fluoroscopic images of the second x-ray image data set showing the medical instrument and the blood vessel tree. To calculate registration information according to the invention, the first image data set <NUM> and the second image data <NUM> are used as input data. To facilitate registration, two anatomical structures have already been selected, wherein the first anatomical structure is only visible in the first image data set and the second anatomical structure is only visible in the second image data set. However, at least segments of these anatomical structures have a known geometrical relationship. In the cardiac application discussed here, the magnetic resonance image data set clearly shows the myocardium of the left ventricle as a first anatomical structure, so that the epicardial surface of the myocardium can be derived on which usually lies the coronary sinus blood vessel tree as the second anatomical structure clearly visible in the second image data set <NUM>. In other words, the first and the second anatomical structures are adjacent in the sense that they share a common surface or at least parallel surfaces.

In a step S1, the myocardium is segmented in the first image data set <NUM> by using a known tissue segmentation algorithm not discussed here in detail. From the segmented myocardium, the epicardial surface can be derived and is described in step S1 as a surface representation, namely a mesh. The mesh can also be understood as a point cloud representation if the nodes are taken as single points; however, other possibilities exist to describe the epicardial surface as a point cloud representation alternatively or additionally. The surface representation and/or the point cloud representation constitutes a first geometry information.

In a step S2, which is independent from step S1, the second image data set <NUM> is evaluated to derive a second geometry information describing the coronary sinus blood vessel tree as a point cloud representation. To achieve this, the two dimensional fluoroscopy images of the second image data set are first analysed to detect specific points visible in all two-dimensional fluoroscopy images. In this embodiment, the specific point is a feature point of the medical instrument; however, alternatively or additionally a specific point of an identifiable bifurcation in the blood vessel tree can be used. Knowing the location of the specific point in all two-dimensional projections of the second image data set <NUM>, the blood vessel tree can be reconstructed, wherein the fact that the blood vessels are interconnected is used to assign pixels from the fluoroscopy images to certain blood vessels and find pixels showing the same blood vessel. To create a point cloud representation of the blood vessel tree, points along the centre lines of the blood vessels can be used, but it is preferred to use points on the circumference of the vessels, in particular those on the surface oriented to the epicardial surface.

Once the first and the second geometry information are known, in a step S3 registration is performed. In this embodiment, a Go-ICP algorithm is used to find the global optimum regarding the known relationship, i.e. minimizing the deviations from the known geometrical deviationship, for transformation parameters comprising those of a rigid transformation (rotation and translation) and feature correspondences, in the case of two point cloud representations point correspondences. Other algorithms, for example a Go-CPD algorithm, can also be used. By using a globally optimized registration algorithm, a manual step wherein a first rough positioning is chosen as a starting point for finding a local minimum, can be omitted.

The Go-ICP algorithm can use boundary conditions reducing the parameter space to be searched. For example, positioning information from the acquisition of the first image data set <NUM> and the second image data set <NUM> can be used to exclude certain motions of the patient between the acquisitions. Additionally, sparse mutual information from the image data sets <NUM>, <NUM>, which is not sufficient to register them on its own, can be used to formulate boundary conditions.

It is, however, as indicated in <FIG>, preferred, to use such information that is common in the two modalities in a step S4 to refine the registration information calculated in step S3. For example, some parts of the vascular tree in the first magnetic resonance image data set <NUM> can be identified and/or the shadow of the border of the left ventricle or other components of the human heart can be extracted from fluoroscopy images of the second x-ray image data set <NUM>.

The result of the method according to the invention is accurate registration information <NUM> which can be used to create fusion images for image guidance, as will be detailed below.

<FIG> shows geometrically how the registration process works. The blood vessel tree <NUM> extracted from the second image data set <NUM> and the epicardial surface <NUM> of the myocardium as segmented from the first image data set <NUM> are positioned such that the blood vessel tree <NUM> optimally lies on the epicardial surface <NUM> of the myocardium.

It is noted that the registration information <NUM> can be updated automatically every time a new second image data set <NUM> is acquired during the interventional procedure, for example, if patient motion occurred and/or if requested by a user, who, for example, observed a patient motion, and/or periodically. In both cases, the registration process is preferably performed in real-time.

The registration information <NUM> is used to create fusion images for image guidance during the interventional procedure. An example for such a fusion image is shown in <FIG>. The fusion image <NUM> consists of a fluoroscopy image <NUM> of the second image data set <NUM> or a fluoroscopy image acquired using the same imaging device as used for the second image data set <NUM> with no relevant patient motion occurring since acquisition of the second image data set <NUM>, overlaid with information from the first image data set <NUM> and/or planning information from planning performed on the first image data set <NUM>. As can be seen, the fluoroscopy image <NUM> clearly shows a medical instrument <NUM>, here a multipolar lead with electrodes <NUM>. As an overlay, the epicardial surface <NUM> is included as well as areas containing lesions (in this case scarred tissue), an information taken from the above-mentioned functional magnetic resonance image data set, which, of course, is registered with the anatomical magnetic resonance image data set forming the first image data set <NUM>. Lesions, in particular scarred tissue, are shown as coloured areas <NUM>. As can be seen, the electrodes <NUM> are placed to avoid scarred tissue.

Finally, <FIG> shows an angiographic imaging device <NUM> having two C-arms <NUM> such that fluoroscopic images using two different angulations can be acquired simultaneously. The angiographic imaging device <NUM> has a control device <NUM>, which, in this embodiment, also includes a registration device <NUM> adapted to perform the method according to the invention.

The registration device <NUM> comprises a selection unit <NUM> for selecting the anatomical structures. For example, for multiple possible interventional procedures and combinations of modalities, suitable anatomical structures and respective algorithms can be stored in a data base of the control unit <NUM>.

The registration device <NUM> further comprises an evaluation unit <NUM> for automatically determining the first and second geometry information and an optimization unit <NUM> for registering a geometry information. Finally, a registration unit <NUM> for determining the registration information from the optimized transformation parameters determined by the optimization unit <NUM> is provided.

Additionally, the control device <NUM> can also comprise a fusion unit using the registration information to generate fusion images such as a fusion image <NUM> shown in <FIG>.

Claim 1:
Method for registration of a first image data set (<NUM>) and a second image data set (<NUM>) of a target region of a patient, wherein the first and the second image data sets (<NUM>, <NUM>) have been acquired using different imaging modalities or using different imaging techniques on the same imaging modality, the method comprising:
- selecting a first anatomical structure which is visible in the first image data set (<NUM>), and a second anatomical structure which is visible in the second image data set (<NUM>), such that there is a known geometrical relationship between at least extended segments of the anatomical structures,
- by evaluating the first and second image data sets (<NUM>, <NUM>), automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure comprising the respective segment and a second geometry information describing the geometry of at least a part of the second anatomical structure comprising the respective segment,
- automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences between features in the first and second geometry information by minimizing deviations from the known geometrical relationship, and
- determining registration information (<NUM>) from the optimized transformation parameters,
characterized in that a boundary condition describing possible motion of the patient between acquisition of the first and the second image data set (<NUM>, <NUM>) is used to reduce the parameter space of the optimization of the transformation parameters.