METHODS AND DEVICES FOR REGISTRATION OF IMAGE DATA SETS OF A TARGET REGION OF A PATIENT

Abstract

A method for registering image data sets of a target region of a patient includes selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between extended segments of the anatomical structures; automatically determining a first geometry information describing the geometry of at least a part of the first anatomical structure and a second geometry information describing the geometry of at least a part of the second anatomical structure, neither information being sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the anatomical structures with respect to the other and geometrical correspondences; and determining registration information from the optimized transformation parameters.

Claims

1. A method for registering 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 a same imaging modality, the method comprising: selecting a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between at least extended segments of the first and second anatomical structures; automatically determining, by evaluating the first and second image data sets, a first geometry information describing a geometry of at least a part of the first anatomical structure comprising the respective segment and a second geometry information describing a geometry of at least a part of the second anatomical structure comprising the respective segment, wherein neither the first geometry information concerning the first anatomical structure nor the second geometry information concerning the second anatomical structure is sufficient to enable registration of the image data sets on its own; automatically optimizing transformation parameters describing a rigid transformation of one of the first or second anatomical structures with respect to the other anatomical structure and geometrical correspondences between features in the first and second geometry information by minimizing deviations from the known geometrical relationship; and determining registration information from the optimized transformation parameters.

2. The method of claim 1, wherein the first anatomical structure is different from the second anatomical structure.

3. The method of claim 2, wherein the known geometrical relationship describes as least partly parallel or touching or intersecting surfaces of the first and second anatomical structures.

4. The method of claim 1, wherein the first anatomical structure is a tissue layer delimiting an organ or organ part, or adjacent to a secondary tissue layer delimiting an organ or organ part, and wherein the second anatomical structure is a blood vessel structure on or parallel to a surface of the organ.

5. The method of claim 4, wherein the first anatomical structure is an epicardium or a myocardium.

6. The method of claim 4, wherein the first geometry information is determined using tissue segmentation, or wherein the second geometry information is determined using vessel segmentation on the second image data set, or wherein the first geometry information is determined using the tissue segmentation and the second geometry information is determined using the vessel segmentation on the second image data set.

7. The method of claim 6, wherein the second image data set has been acquired using a contrast agent.

8. The method of claim 1, wherein the first image data set is a magnetic resonance image data set and the second image data set is an x-ray image data set.

9. The method of claim 8, wherein, 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 is detected in all x-ray images and, using the specific point as a starting point, pixels corresponding to a same blood vessel are detected using a constraint that a blood vessel tree is interconnected.

10. The method of claim 9, wherein the at least one specific point of a medical instrument is used to temporally block a blood vessel, a specific point of a bifurcation, or both the blood vessel and the specific point of the bifurcation.

11. The method of claim 1, wherein the first geometry information, the second geometry information, or both the first and second geometry information comprise a point cloud representation, a surface representation, or both the point cloud representation and the surface representation.

12. The method of claim 11, wherein the surface representation is a mesh.

13. The method of claim 11, wherein a globally optimized iterative closest point algorithm, a globally optimized coherent point drift algorithm, or both the globally optimized iterative closest point algorithm and the globally optimized coherent point drift algorithm are used for optimizing the transformation parameters.

14. The method of claim 1, wherein a boundary condition describing possible motion of the patient between acquisition of the first image data set and the second image data set is used to reduce a parameter space of the optimization of the transformation parameters.

15. The method of claim 1, wherein at least one correlation information regarding the first image data set and the second image data set, which is relevant regarding the registration, but not sufficient for registering the first and second 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.

16. The method of claim 1, wherein the registration information is used in calculating a fusion image for image guidance during a medical interventional procedure.

17. The method of claim 1, wherein, during an intervention in the target region, the second image data set is reacquired at least once and with at least one reacquisition of the second image data set the registration information is updated

18. The method of claim 17, wherein the registration information is updated in real time.

19. A registration device 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 or using different imaging techniques on a same imaging modality, the registration device comprising: a selection unit configured to select a first anatomical structure only or at least partially only visible in the first image data set, and a second anatomical structure only or at least partially only visible in the second image data set, such that there is a known geometrical relationship between at least extended segments of the first and second anatomical structures; an evaluation unit configured to automatically determine, by evaluating the first and second image data sets, a first geometry information describing a geometry of at least a part of the first anatomical structure comprising the respective segment and a second geometry information describing a geometry of at least a part of the second anatomical structure comprising the respective segment, wherein neither the first geometry information concerning the first anatomical structure nor the second geometry information concerning the second anatomical structure is sufficient to enable registration of the image data sets on its own; an optimization unit configured to automatically optimize transformation parameters describing a rigid transformation of one of the first or second anatomical structures with respect to the other anatomical structure and geometrical correspondences between features in the first and second geometry information by minimizing deviations from the known geometrical relationship; and a registration unit configured to determine registration information from the optimized transformation parameters.

20. The registration device of claim 19, wherein the first anatomical structure is different from the second anatomical structure.

21. A computer comprising: a non-transitory electronically readable storage medium including a computer program that when executed, the non-transitory electronically readable storage medium and computer program configured to cause a computer to perform: select a first anatomical structure only or at least partially only visible in a first image data set, and a second anatomical structure only or at least partially only visible in a second image data set, such that there is a known geometrical relationship between at least extended segments of the first and second anatomical structures; automatically determine, by evaluating the first and second image data sets, a first geometry information describing a geometry of at least a part of the first anatomical structure comprising the respective segment and a second geometry information describing a geometry of at least a part of the second anatomical structure comprising the respective segment, wherein neither the first geometry information concerning the first anatomical structure nor the second geometry information concerning the second anatomical structure is sufficient to enable registration of the image data sets on its own; automatically optimize transformation parameters describing a rigid transformation of one of the first or second anatomical structures with respect to the other anatomical structure and geometrical correspondences between features in the first and second geometry information by minimizing deviations from the known geometrical relationship; and determine registration information from the optimized transformation parameters.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Further advantages and details of the current disclosure are apparent from the following description of the embodiments in conjunction with the drawings, in which:

[0041] FIG. 1 depicts a flow chart of an embodiment of the method.

[0042] FIG. 2 depicts an example of a diagram visualizing a registration process.

[0043] FIG. 3 depicts an example of a fusion image for image guidance.

[0044] FIG. 4 depicts an example of an angiographic imaging device having an integrated registration device.

DETAILED DESCRIPTION

[0045] FIG. 1 is a flow chart of an embodiment of a method, which is used for image guidance during a medical interventional procedure, e.g., 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, e.g., to locate lesions and intervention locations on the epicardial surface of the myocardium.

[0046] During the actual intervention, a minimally invasive medical instrument, (e.g., 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, such as 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.

[0047] 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, the first image data set 1 and the second image data 2 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 shows the myocardium of the left ventricle as a first anatomical structure, so that the epicardial surface of the myocardium may be derived on which may lie the coronary sinus blood vessel tree as the second anatomical structure visible in the second image data set 2. 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.

[0048] In act S1, the myocardium is segmented in the first image data set 1 by using a known tissue segmentation algorithm not discussed here in detail. From the segmented myocardium, the epicardial surface may be derived and is described in act S1 as a surface representation, namely a mesh. The mesh may 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.

[0049] In act S2, which is independent from act S1, the second image data set 2 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 analyzed 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 may be used. Knowing the location of the specific point in all two-dimensional projections of the second image data set 2, the blood vessel tree may 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 center lines of the blood vessels may be used, and it is possible to use points on the circumference of the vessels, such as those on the surface oriented to the epicardial surface.

[0050] Once the first and the second geometry information are known, in act S3 registration is performed. In this embodiment, a Go-ICP algorithm is used to find the global optimum regarding the known relationship, e.g., minimizing the deviations from the known geometrical deviationship, for transformation parameters including those of a rigid transformation (e.g., rotation and translation) and feature correspondences, in the case of two point cloud representations point correspondences. Other algorithms, (e.g., a Go-CPD algorithm), may also be used. By using a globally optimized registration algorithm, a manual act wherein a first rough positioning is chosen as a starting point for finding a local minimum, may be omitted.

[0051] The Go-ICP algorithm may use boundary conditions reducing the parameter space to be searched. For example, positioning information from the acquisition of the first image data set 1 and the second image data set 2 may be used to exclude certain motions of the patient between the acquisitions. Additionally, sparse mutual information from the image data sets 1, 2, which is not sufficient to register them on its own, may be used to formulate boundary conditions.

[0052] It is, however, as indicated in FIG. 1, possible to use such information that is common in the two modalities in act S4 to refine the registration information calculated in act S3. For example, some parts of the vascular tree in the first magnetic resonance image data set 1 may be identified and/or the shadow of the border of the left ventricle or other components of the human heart may be extracted from fluoroscopy images of the second x-ray image data set 2.

[0053] The result of the method is accurate registration information 3 that may be used to create fusion images for image guidance, as will be detailed below.

[0054] FIG. 2 shows geometrically how the registration process works. The blood vessel tree 4 extracted from the second image data set 2 and the epicardial surface 5 of the myocardium as segmented from the first image data set 1 are positioned such that the blood vessel tree 4 optimally lies on the epicardial surface 5 of the myocardium.

[0055] It is noted that the registration information 3 may be updated automatically every time a new second image data set 2 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 may be performed in real-time.

[0056] The registration information 3 is used to create fusion images for image guidance during the interventional procedure. An example for such a fusion image is shown in FIG. 3. The fusion image 6 includes a fluoroscopy image 7 of the second image data set 2 or a fluoroscopy image acquired using the same imaging device as used for the second image data set 2 with no relevant patient motion occurring because acquisition of the second image data set 2, overlaid with information from the first image data set 1 and/or planning information from planning performed on the first image data set 1. The fluoroscopy image 7 shows a medical instrument 8, here a multipolar lead with electrodes 9. As an overlay, the epicardial surface 10 is included as well as areas containing lesions (e.g., scarred tissue), an information taken from the above-mentioned functional magnetic resonance image data set, which is registered with the anatomical magnetic resonance image data set forming the first image data set 1. Lesions, (e.g., scarred tissue), are shown as colored areas 11. As depicted, the electrodes 9 are placed to avoid scarred tissue.

[0057] Finally, FIG. 4 shows an angiographic imaging device 12 having two C-arms 13 such that fluoroscopic images using two different angulations may be acquired simultaneously. The angiographic imaging device 12 has a control device 14, which, in this embodiment, also includes a registration device 15 configured to perform the method.

[0058] The registration device 15 includes a selection unit 16 for selecting the anatomical structures. For example, for multiple possible interventional procedures and combinations of modalities, suitable anatomical structures, and respective algorithms may be stored in a data base of the control unit 14.

[0059] The registration device 15 further includes an evaluation unit 17 for automatically determining the first and second geometry information and an optimization unit 18 for registering a geometry information. Finally, a registration unit 19 for determining the registration information from the optimized transformation parameters determined by the optimization unit 18 is provided.

[0060] Additionally, the control device 14 may also include a fusion unit using the registration information to generate fusion images such as a fusion image 6 shown in FIG. 3.

[0061] It is to be understood that the elements and features recited in the appended claims may be combined in different ways to produce new claims that likewise fall within the scope of the present disclosure. Thus, whereas the dependent claims appended below depend from only a single independent or dependent claim, it is to be understood that these dependent claims may, alternatively, be made to depend in the alternative from any preceding or following claim, whether independent or dependent, and that such new combinations are to be understood as forming a part of the present specification.

[0062] While the present disclosure has been described above by reference to various embodiments, it may be understood that many changes and modifications may be made to the described embodiments. It is therefore intended that the foregoing description be regarded as illustrative rather than limiting, and that it be understood that all equivalents and/or combinations of embodiments are intended to be included in this description.