In the context of a CT-controlled or MRT-controlled interventional access, for the purpose of supporting and monitoring the navigation of interventional tools that are required for this, 2D projection images, which are usually generated using fluoroscopic imaging in real time, of the anatomical tissue regions to be treated, the medical instruments that are used and the surrounding regions within the body of a patient who is to be treated, are represented on a display screen. A conventional multidirectional C-arm radioscopy system can be used, for example, for monitoring such an interventional access by means of x-ray imaging. Modern C-arm devices allow a rotatory acquisition of 2D projection recordings of tissue regions that are to be depicted, with subsequent 3D reconstruction of the acquired image data which is then visualized in a three-dimensional format. For this purpose, a 3D data record is calculated from the 2D projection recordings of the relevant tissue regions using suitable digital image processing functions. With regard to the image quality and the visualization possibilities, the resulting 3D depictions have CT-like properties.
In the field of interventional rotation angiography, it is appropriate to cite in particular a technology which has been developed by the firm Siemens and has become known under the product designation syngo DynaCT. syngo DynaCT utilizes the recordings of a rotation angiography and generates an angiography CT image (ACT) therefrom. In the case of a C-arm rotation of 220°, the image acquisition typically takes place within approximately 5 to 10 seconds. The recorded volume can then be three-dimensionally reconstructed directly in the angiography room. The application areas of syngo DynaCT range from the representation of hemorrhages or pathologies of the cranial system to the guidance and inspection of punctures and drainage systems. When visualizing tumors and metastases, e.g. in the liver, syngo DynaCT is also used for supporting therapeutic measures relating to e.g. embolization or RF ablation. Since syngo DynaCT already supplies CT-like images during the performance of an interventional access, a patient who is to be treated need no longer be additionally moved into a computer tomograph for the purpose of generating the image data for two-dimensional projection recordings which must then be converted into a volume data record and presented in graphical form for display.
As an alternative to syngo DynaCT, it is also possible to use CT, PET-CT or MRT-based radiological imaging methods, by means of which image data of two-dimensional projection recordings of interesting tissue regions, organs, lesions, anatomical or pathological structures within the body of a patient who is to be treated can be obtained prior to intervention. This image data must then first be converted into a volume data record of a reconstructed 3D view M of the relevant image objects, matched and merged with the image data of a 2D fluoroscopy image F which shows the surrounding tissue regions of these image objects, and graphically visualized together with this, before an interventional access that must be carried out under image-oriented monitoring can take place.
For the purpose of matching the two images M and F, for the data records of an image object BO which is identically represented in the two depictions, wherein said data records might relate to different location-coordinates systems, it is necessary to determine the position offset and angle offset parameters of a two or three-dimensional coordinate transformation which transfers the coordinates of the one data record to the coordinates of the other data record in each case, thereby bringing the two representations of the relevant image object BO into line with each other. In order to optimize the quality of this transfer (i.e. the quality of the image superimposition when merging both data records), an evaluation function (metric) which is defined over a parameter environment is formed and submitted to an optimization criterion that can be expressed by an extremal condition, wherein said optimization criterion can then be used to find the position offset and angle offset parameters for which the two representations of the image object BO are best superimposed. In other words, the evaluation function therefore assumes its optimum for the case that the two data records are correctly registered.
For the purpose of determining the position offset and angle offset parameters, provision is made for specific image features (also referred to as “anatomical landmarks” below) which are contained in the two data records, that must be matched, of the relevant image object BO and can spatially associated with each other. If these landmarks are artificially applied marking objects, the term “extrinsic” registration is used. As part of this activity, an easily detectable system of orientation points is applied to the patient before the image recording. These fixed points can be mathematically brought into a shared context with relative ease subsequently. In most cases, however, the image data is available without artificially added landmarks. In this instance, the case is one of an “intrinsic” registration. The required image features must often be obtained by means of image analysis methods in this case. This takes place either by detecting anatomical landmarks, wherein this can involve e.g. edges or surfaces of bones, internal organs or clearly delimitable tissue regions within the body of the patient, or a segmentation of specific image features which are contained in both data records takes place before the matching. Intrinsic image features are used e.g. in the context of voxel-based matching methods, which have gained considerable significance in the last decade in the course of the research into intensity-based algorithms that are based on an analysis of “mutual information” I(G1, G2) as an evaluation criterion for quantifying the quality of a registration. The registration measure given by the following expression
                              I          ⁡                      (                                          G                1                            ,                              G                2                                      )                          =                              ∑                                          G                1                            ,                              G                2                                              ⁢                                                    p                ⁡                                  (                                                            g                      1                                        ,                                          g                      2                                                        )                                            ·                              log                10                                      ⁢                                          p                ⁡                                  (                                                            g                      1                                        ,                                          g                      2                                                        )                                                                              p                  ⁡                                      (                                          g                      1                                        )                                                  ·                                  p                  ⁡                                      (                                          g                      2                                        )                                                                                                          (        1        )            is based on the Kullback-Leiber divergence (KLD) between the associated probability density functions pG1(g1) and pG2(g2) of the gray value distributions in the data records G1 and G2 of two images which must be registered together, wherein g1 and g2 designate two discrete random variables for the gray values contained in these two images. In this case, the Kullback-Leiber divergence on the right-hand side of the formula indicates the extent to which the one distribution varies from the other distribution, since it represents a measure for the reciprocal dependency of the two random variables g1 and g2, which measure is maximal in the case of maximal statistical dependency (i.e. in the case of an optimal registration) and minimal in the case of total statistical independence (i.e. in the case of totally incorrect registration). Since only the gray value information of acquired image data is used for determining I(G1, G2), no a priori knowledge or image analysis is required for this in principle.
The significant advantage of syngo DynaCT over conventional image acquisition and image registering systems is that visualized syngo DynaCT image data records reproduce current 3D views of image objects to be represented within the body of a patient, wherein these are already related to the location-coordinates system of a 2D fluoroscopy image of the surrounding tissue regions of this image object and are already optimally registered with the 2D fluoroscopy image, such that it is possible to forgo the use of anatomical landmarks for matching three-dimensionally reconstructed views of pre-interventionally acquired image data of the image object with the image data of the 2D fluoroscopy image, and to forgo the use of an intensity-based registration measure for quantifying the quality of this registration.