the individual method acts according to an exemplary embodiment are described below with reference to fig1 . in a first act s 1 , a 3d dataset of the vascular system is acquired by an angiography system , for example a c - arm device . the dataset may be acquired on the basis of a difference calculation between a fill image in which the vascular system is filled with contrast agent statically , e . g ., uniformly or in a “ steady state ”, and a reference image that is free of contrast agent . the result of the 3d acquisition is a three - dimensional , static visualization of the vascular system . the acquisition contains not only the large afferent and efferent vessels ( e . g ., arteries , veins ) but also the vessels of the parenchyma ( in particular , the so - called microcirculation ). all of the vessels may be segmented if necessary . in a second act s 2 , those vessels whose size exceeds a predetermined limit value are segmented . this may be carried out on the basis of known segmentation methods , for example , by threshold - value - based segmentation . alternatively , however , a manual segmentation on the basis of a 3d visualization of the dataset is also deserving of consideration , for example . the result of the segmentation is a first modified 3d dataset ( e . g ., segmented dataset ) containing only the smaller vessels of interest ( in particular , the vessels of the parenchyma ). in addition , a second modified 3d dataset is generated that exclusively contains the large vessels ( e . g ., arteries , veins ) that have been segmented out . acts s 1 and s 2 may be performed at an arbitrary point in time , independently of the following acts and / or independently of a perfusion measurement . in a further act s 3 , at least one 2d projection image of the body region of interest is acquired , in particular , by digital subtraction angiography . the acquisition may be performed dynamically by obtaining a plurality of projection images sequentially as a time series shortly after injection of a contrast agent bolus in order to visualize the perfusion of the vascular system dynamically ( e . g ., with time information ). the subsequent acts may be performed on the basis of the individual images of the acquisition series or on the basis of a parametric projection image which combines the individual images of the acquisition series and visualizes them in a common image . in act s 4 , the 2d projection image is normalized on the basis of the 3d dataset of the parenchymal volume ( e . g ., first modified 3d dataset , pbv dataset ). in this process , the 2d projection image is divided pixel by pixel by a forward projection of the 3d dataset of the parenchymal volume . the forward projection of the 3d dataset is performed in this case along the same projection direction as the 2d projection image . toward that end a registration of the forward projection with the 2d projection image may be carried out first . in act s 5 , those vessels in the 2d projection image whose size exceeds a predetermined limit value are marked or eliminated . this is accomplished , e . g ., on the basis of the segmented large vessels of the 3d dataset ( e . g ., second modified 3d dataset ). by a forward projection of the second modified 3d dataset , which exclusively contains the segmented large vessels , the corresponding large vessels are identified in the 2d projection image and may be classified as such . in act s 6 , the regions of the large vessels in the 2d projection image are filled with artificial data , in particular by interpolation , e . g ., non - linear interpolation . as a result of the interpolation the anatomical information of the areas hidden by the large vessels is initially lost . in order to reintroduce at least some of said information into the image , the 2d projection image is denormalized on the basis of the 3d dataset of the parenchymal volume ( e . g ., first modified 3d dataset ) in act s 7 . toward that end , the 2d projection image is multiplied pixel by pixel by a forward projection of the 3d dataset of the parenchymal volume . act s 3 may include the acquisition of a time series of 2d projection images in order to visualize the perfusion of the vascular system . acts s 4 to s 7 are performed for each point in time of the time series , only the regions of the vascular system filled at the respective point in time being taken into account in each case in the 3d dataset . in a further act , the post - processed individual projections may then be assembled to provide a new time - to - peak ( ttp ) calculation and visualization . the new visualization suppresses the large vessels . by a time - dependent denormalization of projection values , the embodiments therefore achieve an improved representation of ttp maps , which no longer contain the large and interfering vessels . a three - dimensional dataset of the parenchymal blood volume ( 3d pbv dataset ) may be used for this purpose . this may be acquired independently in time of the acquisition of the 2d projection ( dsa acquisition or dsa series ) and used on the basis of the 3d information for denormalizing a 2d dsa acquired from an arbitrary angle . a 3d dataset , once present , may therefore be used subsequently for denormalizing a plurality of 2d dsa acquisitions in order to improve their visualization . fig2 depicts a device by which the method may be performed and which itself may be embodied or configured . the device includes an acquisition apparatus 30 , specifically an x - ray machine , in particular , a c - arm system . the acquisition apparatus 30 includes an x - ray source 32 and an x - ray detector 33 , which are attached to the arms of a c - arm 31 that may be rotated around a patient couch 38 . the x - ray detector 33 may be a digital detector that is capable of generating digital x - ray images of a patient 40 positioned on the patient couch 38 . the c - arm is movably mounted on a pedestal 34 . the movements of the c - arm and the acquisition of x - ray images are controlled by a control and computing unit 35 . furthermore , the digital x - ray images acquired by the x - ray detector 33 may be transmitted to the control and computing unit 35 and processed there . the control and computing unit 35 includes a data medium or program memory 36 , for example , an internal hard disk on which is stored a program code of a computer program for controlling the acquisition apparatus 30 and for processing the data obtained . the control and computing unit 35 and the program memory 36 may be part of a computer 37 that is , for example , a pc , a workstation , or a console for the acquisition apparatus 30 . in addition , a screen for displaying x - ray images , as well as input devices such as keyboard and / or mouse , may also be present . the acquisition apparatus 30 is configured for the purpose of acquiring a 3d dataset of the body region of interest , in particular , of the brain of a patient , based on acquisition of a plurality of two - dimensional projection images from different directions . in addition , the acquisition apparatus 30 is provided for the purpose of acquiring a time series of 2d projection images from a predetermined direction . the acquired series images ( dsa series ) may be assembled into a parametric visualization ( iflow image ), which operation may be performed in the control and computing unit 35 . alternatively , however , different and / or other acquisition devices may also be provided for the acquisition of the static 3d dataset and of the 2d projection image containing the time information . although the invention has been illustrated and described in greater detail on the basis of an exemplary embodiment , the invention is not limited by the disclosed examples and other variations may be derived herefrom by the person skilled in the art without leaving the scope of protection of the invention . the above - described method may be implemented via a computer program product including one or more readable storage media having stored thereon instructions executable by one or more processors of the computing system . execution of the instructions causes the computing system to perform operations corresponding with the acts of the method described above . the instructions for implementing processes or methods described herein may be provided on non - transitory computer - readable storage media or memories , such as a cache , buffer , ram , flash , removable media , hard drive , or other computer readable storage media . a processor performs or executes the instructions to train and / or apply a trained model for controlling a system . computer readable storage media include various types of volatile and non - volatile storage media . the functions , acts , or tasks illustrated in the figures or described herein may be executed in response to one or more sets of instructions stored in or on computer readable storage media . the functions , acts or tasks may be independent of the particular type of instruction set , storage media , processor or processing strategy and may be performed by software , hardware , integrated circuits , firmware , micro code and the like , operating alone or in combination . likewise , processing strategies may include multiprocessing , multitasking , parallel processing and the like . 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 invention . 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 . while the present invention 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 .
the embodiments relate to generating a 2d projection image of a vascular system of a body region of interest , including : acquiring a 3d dataset of the body region of interest , acquiring at least one 2d projection image of the body region of interest , generating a modified 3d dataset by eliminating vessels whose size exceeds a predetermined limit value , normalizing the 2d projection image using projection data of the modified 3d dataset , eliminating vessel projections in the normalized 2d projection image whose size exceeds a predetermined limit value , interpolating the areas of the 2d projection image in which the vessel projections have been eliminated , and denormalizing the normalized and interpolated 2d projection image using projection data of the modified 3d dataset .