Abstract:
In the English translation document, please replace the abstract with the following: 2-D projection images show the temporal profile of the distribution of a contrast medium in an examination object, which contains a vascular system and its surroundings. Each projection image comprises pixels with pixel values. The pixel values of pixels corresponding to one another in the projection images are defined by at least essentially locationally identical areas of the examination object. A computer assigns a uniform 2-D evaluation core that is uniform for all corresponding pixels at least in a sub-area of pixels corresponding to one another in the projection images that is uniform for the projection images. The computer defines at least one characteristic value for each pixel within each projection image based on the evaluation core assigned to the pixel and assigns it to the relevant pixel. Based on the temporal profile of the characteristic values, the computer defines parameters of at least one function of time, so that any deviation between the function parameterized with the parameters and the temporal profile of the characteristic values is minimized. Based on the parameters the computer defines a type and/or an extent and assigns them to a pixel of a 2-D evaluation image corresponding to the pixels of the projection images. The type indicates whether the respective pixel of the evaluation image corresponds to a vessel of the vascular system, a perfused part or a non-perfused part of the surroundings of a vessel of the vascular system. The extent is characteristic of perfusion. The computer outputs at least the sub-area of the evaluation image to a user via a display device.

Description:
CROSS REFERENCE TO RELATED APPLICATIONS  
       [0001]     This application claims priority of German application No. 10 2006 025 422.8 DE filed May 31, 2006, which is incorporated by reference herein in its entirety.  
       FIELD OF INVENTION  
       [0002]     The present invention relates to an image evaluation method for two-dimensional projection images, which show the temporal profile of the distribution of a contrast medium in an examination object, the examination object containing a vascular system and its surroundings, each projection image comprising a plurality of pixels having pixel values, the pixel values of pixels corresponding to one another in the projection images being defined by at least essentially locationally identical areas of the examination object.  
         [0003]     The present invention relates furthermore to a data medium with a computer program stored on the data medium for implementing an image evaluation method of this type and to a computer with a mass memory in which a computer program is filed, such that the computer executes an image evaluation method of this type after the computer program has been called.  
       BACKGROUND OF INVENTION  
       [0004]     Image evaluation methods of this type and the corresponding objects (data medium with computer program, programmed computer) are known.  
         [0005]     Thus, for example, an image evaluation method of this type is known from the technical article “Quantitative Analyse von koronarangiographic Bildfolgen zur Bestimmung der Myokardperfusion” [Quantitative analysis of coronary angiographic image sequences for determining myocardial perfusion] by Urban Malsch et al., which appeared in “Bildverarbeitung fur die Medizin 2003—Algorithmen—Systeme—Anwendungen” [Image processing for medicine 2003—algorithms—systems—applications], Springer Verlag, pages 81 to 85. With this image evaluation method, a computer uses the projection images to determine a two-dimensional evaluation image, which comprises a plurality of pixels, and outputs the evaluation image to a user via a display device. The pixels of the evaluation image correspond to those of the projection images. The computer uses the temporal profile of the pixel values of the projection images to assign a pixel value to the pixels of the evaluation image, the pixel value being characteristic of the time of maximum contrast change.  
         [0006]     The doctrine of the above-mentioned technical article is described in the context of angiographic examinations of the coronary arteries of the human heart. This type of examination is one of the most important diagnostic tools in cardiology today. Additional information such as the determination of flow velocity or myocardial perfusion is further information which can in principle be obtained by means of angiography. The essential diagnostic evidence here is the perfusion of the cardiac muscle.  
         [0007]     Today, a number of other non-invasive methods of examination such as PET, SPECT, MR or contrast-medium-aided ultrasound have also become established. These methods of examination offer the facility for quantifying, in addition to other parameters, the perfusion status of the myocardium. These methods are generally applied in stable angina pectoris cases or in order to assess the risk after a myocardial infarction.  
         [0008]     For an assessment of the therapeutic outcome of an intervention, it would therefore be advantageous to be able to monitor the improvement in perfusion and/or the occurrence of microembolization and microinfarctions during the actual intervention. It would consequently be advantageous if quantification of perfusion were added to the other diagnostic parameters in the catheter laboratory, as this would make it possible to obtain all relevant information in one examination and thus to achieve an improvement in the monitoring of treatment.  
         [0009]     Quantification of the supply of blood to the myocardium by means of angiographic methods is however problematic, since the angiographically observable cardiac vessels have a diameter of just under a millimeter or more. These observable vessels terminate in millions of tiny capillary vessels, which have diameters of only a few micrometers. The flow dynamics and distribution in the capillary vessels are ultimately determined by the blood supply to the cardiac muscle. Drawing conclusions from the macroscopic supply of blood as to the dynamics of the supply of blood in the capillary vessels is, strictly speaking, inadmissible, even though it is often done.  
         [0010]     In order to capture the supply of blood to the myocardium, various methods are known, in particular contrast echocardiography, magnetic resonance tomographic diagnostics and SPECT.  
         [0011]     In order to make the blood flow dynamics in large vessels and in the capillary vessels measurable and thereby comparable, various gradation systems are known which divide up the continuum of conditions into discrete classes. Some of these classifications describe the macroscopic circulation of blood, others the circulation of blood in the capillaries. The most commonly used classifications were drawn up by the scientific organization “Thrombolysis in Myocardial Infarction” (TIMI). These classifications are deemed to be the standard. In multi-center studies in which reproducible and comparable results are of particular importance, the TIMI classifications are frequently used. The classifications are complex and can be applied only in a time-consuming manner. They are therefore not generally used in routine clinical work.  
         [0012]     By far the most frequently used method in the prior art is the visual assessment of “myocardial blush” on a screen. This procedure is often used for multi-center studies. A prerequisite for this procedure is that the angiographic recording is long enough, in order to be able to see the input and washout of the contrast medium. The visual assessment requires a lot of experience and is in practice carried out only by TIMI-blush experts, as they are known.  
         [0013]     Various procedures are also known, in which an attempt is made to carry out this subjective and personal visual assessment with the aid of computers. An example is to be found in the above-mentioned technical article by Urban Malsch et al.  
         [0014]     The procedure in the above-mentioned technical article represents a good initial approach but still displays shortcomings. For example it is particularly necessary to identify the vessels of the vascular system in the projection images in order to mask out these vessels when evaluating the “myocardial blush”. It is also necessary in the case of the procedure in the technical article to work with DSA images. This gives rise to a significant risk of artifacts, to avoid which computation-intensive methods are in turn required in order to compensate for motion.  
         [0015]     Image evaluation methods for two-dimensional projection images are also described in the German patent application DE  10   2005   039   189 . 3 . At the date of filing of the present invention said patent application is not yet available to the public and therefore does not represent a general prior art. Said patent application is only to be taken into account in the context of the examination as to novelty in the German procedure for granting patents.  
       SUMMARY OF INVENTION  
       [0016]     The present invention is based on the doctrine of DE 10 2005 039 189.3. The doctrine of DE 10 2005 039 189.3 is described in detail below in conjunction with FIGS.  1  to  18 .  
         [0017]     In accordance with  FIG. 1 , a recording arrangement  1  is controlled by a control facility  2 . The recording arrangement  1  is used to capture images B of an examination object  3 . In the present case, in which the examination object  3  is a person, images B of the heart or of the brain of the person  3  are captured for example.  
         [0018]     In order to capture the images B, the recording arrangement  1  has a radiation source  4 , here for example an X-ray source  4 , and a corresponding detector  5 .  
         [0019]     In order to capture the images B, the examination object  3  and the recording arrangement  1  are firstly positioned in a step S 1 , as shown in  FIG. 2 . Positioning can depend in particular on which region (heart, brain, etc.) of the examination object  3  is to be captured and which part of the region is specifically relevant, for example which coronary artery (RCA, LAD, LCX) is to be observed. Step SI can alternatively be carried out purely manually by a user  6 , fully automatically by the control facility  2  or by the user  6  assisted by the control facility  2 . The performance of step SI may be associated with a recording of control images.  
         [0020]     The control facility  2  then waits in a step S 2  for a start signal from the user  6 . After the start signal has been received, the detector  5  captures an image B of the examination object  3  and feeds it to the control facility  2 . The control facility  2  receives the image B in a step S 3  and adds to the image B a corresponding capture time t. If the examination object  3  or the relevant part of the examination object  3  should move iteratively, in a step S 4  the control facility  2  also receives a phase signal of the examination object  3  from a corresponding capture facility  7 .  
         [0021]     Also as part of step S 4 , the control facility  2  determines corresponding phase information φ and adds the phase information φ to the captured image B. For example, the control facility  2  can, as part of step S 4 , receive an ECG signal and derive the phase information φ therefrom. Also the control facility  2  can optionally control the recording arrangement  1  based on the phase signal supplied, such that the capturing of the images B takes place only at one or more predefined phase positions of the examination object  3 , for example only 0.3 and 0.6 seconds after the R-wave of the ECG signal.  
         [0022]     As a rule, the examination object  3  is not influenced externally in its iterative motion. If for example the heart of the person  3  is beating very irregularly, an external stimulation of the heart can take place with a cardiac pacemaker, in order to force a regular heartbeat.  
         [0023]     In a step S 5 , the control facility  2  corrects the captured image B. The control facility  2  preferably corrects the captured image B exclusively with detector-specific corrections but does not carry out any more far-reaching image processing. For example it does not apply any noise-reduction methods.  
         [0024]     In a step S 6 , a check is carried out to establish whether an injection of a contrast medium is to be made. If this check is answered in the affirmative, the contrast medium is injected into the examination object  3  in a step S 7 . Steps S 6  and S 7  can—like step S 1 —be carried out by the user  6  themselves, be performed fully automatically by the control facility  2  or be carried out by the user  6  but aided by the control facility  2 .  
         [0025]     In a step S 8 , the control facility  2  checks whether the capturing of the images B is to be terminated. If this is not the case, the control facility  2  goes back to step S 3 . Otherwise in a step S 9  it transmits the captured images B, preferably corrected with detector-specific corrections, their capture times t and optionally also their phase information φ to an evaluation facility  8 . As an alternative to the transmission of the images B, the capture times t and the phase information (p, as part of the subordinate step S 9 , the transmission could of course also be carried out image by image, i.e. between steps S 5  and S 6 .  
         [0026]     The method outlined above was sketched out roughly in DE 10 2005 039 189.3, as it is of only secondary importance within the scope of the invention there. Thus for example the—manual, fully automatic or computer-aided—adjustment of the recording parameters of the recording arrangement  1  (operating voltage of the radiation source  4 , image rate, image pre-processing, positioning, etc.) was taken as self-evident. Any necessary calibration of the recording arrangement I was also not examined in more detail. It also goes without saying that the capturing of the images B has to be carried out over a sufficiently long period, namely starting before injection of the contrast medium and ending after washout of the contrast medium.  
         [0027]      FIG. 3  shows one of the captured images B by way of example. It can immediately be seen from  FIG. 3  that the image B is two-dimensional and contains a plurality of pixels  9 . The resolution of the image B is even so high that the individual pixels  9  are no longer identifiable in the image B shown. One of the pixels  9  is marked with the reference symbol  9  purely by way of example. Each pixel  9  has a pixel value which lies for example between 0 and 255 (=2 8 −1).  
         [0028]     It can also be seen from  FIG. 3  that the examination object  3  contains a vascular system and its surroundings. Due to the fact that in their entirety the images B form a time sequence, the images B consequently show the temporal profile of the distribution of the contrast medium in the examination object  3 .  
         [0029]     If the examination object  3  was motionless during the capturing of the images B (for example because images B of the brain of the person  3  were recorded) or if, due to corresponding triggering of the recording (for example always  0 . 6  seconds after the R-wave of the ECG), the images B constantly show the examination object  3  in the same phase position, image capturing as such already guarantees that the pixel values of pixels  9  corresponding to one another in the images B are defined by at least essentially locationally identical areas of the examination object  3 . In this case, all the captured images B can be defined as projection images B within the meaning of the comments that follow. Otherwise an appropriate selection must be made. This is explained in detail below in conjunction with  FIGS. 4 and 5 .  
         [0030]     In accordance with  FIG. 4 , the evaluation facility  8 —which can in principle be identical to the control facility  2 —comprises inter alia a computation unit  10  and a mass memory  11 . A computer program  12  is filed in the mass memory  11 . When the computer program  12  is called, the evaluation facility  8  executes an image evaluation method which is described in detail below. The evaluation facility  8  constitutes a computer within the meaning of the invention there. It should also be mentioned that the computer program  12  must of course previously have been routed to the evaluation facility  8 . Routing can for example be carried out by means of a suitable data medium  13 , on which the computer program  12  is stored. The data medium  13  is introduced into a suitable interface  14  of the evaluation facility  8 , so that the computer program  12  stored on the data medium  13  can be read out and filed in the mass memory  11  of the evaluation facility  8 .  
         [0031]     In accordance with  FIG. 5 , the images B are fed to the evaluation facility  8  in a step S 11  via a corresponding interface  15 . The same applies to the corresponding capture times t and the assigned phase information φ.  
         [0032]     In order to select the projection images B from the captured series of images B, the corresponding selection criteria φ*, δφ, namely a reference phase position φ* and a phase boundary δφ, must also be known to the evaluation facility  8 . It is possible here for the reference phase φ* and the phase boundary δφ to be stored within the evaluation facility  8 . In accordance with  FIG. 5 , the reference phase φ* and the phase boundary δφ are preferably predefined for the evaluation facility  8  in a step S 12  by the user  6  via an appropriate input device  17 . For example it is possible for the user  6 , by means of appropriate inputs, to scroll through the captured sequence of images B and to select one of the images B. The phase information φ of the image B selected in this way defines the reference phase φ* and the distance to the immediately succeeding and immediately preceding image B defines the phase boundary δφ. It is equally possible for the user  6  to predefine the corresponding values φ*, δφ explicitly by means of numerical values. Finally it is possible for the ECG signal to be output to the user  6  via a display device  16  and for the user  6  to place corresponding markers in the ECG signal. In all cases, the user  6  can predefine the values φ* and δφ alternatively as absolute time values or as relative phase values.  
         [0033]     In steps S 13  to S 17 , the actual selection of the projection images B from the entire series of images B takes place. To this end in step S 13  an index i is set to the value one. The evaluation facility  8  then selects the images B of the iteration i of the examination object  3  in step S 14 . Within the images B now selected, the evaluation facility  8  generally defines one (exceptionally also none) of the images B as a projection image B. For it looks firstly in step S 15  for the particular image among the selected images B, in which the size of the difference between the phase information φ and the reference phase φ* is minimal. It then checks whether this difference is less than the phase boundary δφ. If the evaluation facility  8  can determine such an image B, it defines this image B in step S 15  as the projection image B for the respective iteration i. If it cannot determine any such image B, it notes this correspondingly.  
         [0034]     In step S 16  the evaluation facility  8  checks whether the index i has already reached its maximum value. If this is not the case, the evaluation facility  8  increments the index i in step S 17  and goes back to step S 14 . Otherwise, the definition of the projection images B is terminated.  
         [0035]     This procedure, which is an integral part of the invention there, ensures that the pixel values of pixels corresponding to one another  9  in the projection images B are also defined, where the examination object  3  has moved iteratively during the capturing of the entire series of images B, by at least essentially locationally identical areas of the examination object  3 .  
         [0036]     In a step S 18 , the evaluation facility  8  outputs the number of projection images B determined and the number of iterations of the examination object  3  to the user  6  via the display device  16 . Said user  6  can thus identify whether they have made a good selection in respect of the reference phase φ* and/or the phase boundary δφ.  
         [0037]     In a step S 19 , the evaluation facility  8  waits for a user input. If such an input has been made, the evaluation facility  8  checks in a step S 20  whether this input was a confirmation by the user  6 . If this is the case, the selection of projection images B is completed and the process can continue with the actual image evaluation method.  
         [0038]     Otherwise, the evaluation facility  8  checks in a step S 21  whether the user  6  has input a request for the reference phase φ* and/or the phase boundary δφ to be changed. If this is the case, the evaluation facility  8  goes back to step S 12 .  
         [0039]     Otherwise the user  6  has input a request for one of the projection images B to be displayed. In this case, the evaluation facility  8  receives a corresponding selection from the user  6  in a step S 22 . In a step S 23 , it then displays the selected projection image B on the display device  16 . It also outputs, together with the selected projection image B, the corresponding phase information φ of the selected projection image B, the reference phase φ*, their difference and the phase boundary  8 φ to the user  6  via the display device  16 . It then goes back to step Si 9 . It would optionally also be possible to display an overall representation of the phase profile and to show the phase information φ of all the projection images B simultaneously.  
         [0040]     For the sake of completeness, it should be mentioned that steps S 12  to S 23  are only expedient and/or necessary when a selection of projection images B has to be made from the entire series of images B. If, on the other hand, the captured images B are already all suitable a priori, steps S 12  to S 23  can be omitted.  
         [0041]     It should furthermore be mentioned that, as an alternative to the procedure described above in conjunction with  FIG. 5 , it is also possible to stipulate in advance suitable intervals for the phase information φ and to determine for each interval the number of possible projection images B. In this case the evaluation facility  8  can output a list or table, based on which the user  6  can identify how many projection images B are available to them and for which phase interval respectively. In this case the user  6  only has to select the phase interval they desire.  
         [0042]     When the selection of projection images B from the entire series of images B is completed, the process continues with  FIG. 6 . Steps S 31  and S 32  in  FIG. 6  correspond on the one hand to step S 11  and on the other hand to steps S 12  to S 23  in  FIG. 5 . Since step S 32  is only optional, as previously mentioned, it is represented in  FIG. 6  by dashed lines only.  
         [0043]     In a step S 33 , the evaluation facility  8  receives a sub-area  18  from the user  6 . The evaluation facility  8  overlays this sub-area  18  in a step S 34  into one of the projection images B and outputs this projection image B together with the marking of the sub-area  18  to the user  6  via the display device  16 . This can also be seen from  FIG. 3 . The sub-area  18  corresponds to the black frame in  FIG. 3 .  
         [0044]     In a step S 35  the computer  8  then determines the type of each pixel  9  which lies within the predefined sub-area  18 . Type  1  corresponds to the non-perfused part of the surroundings of a vessel. Type  2  corresponds to a vessel and type  3  to the perfused part of the surroundings of a vessel.  
         [0045]     In a step S 36 , the evaluation facility  8  checks for each pixel  9  within the sub-area  18 , whether the type  3  was assigned to this pixel  9 . If this is the case, the evaluation facility  8  determines an extent of perfusion for the respective pixel  9  in a step S 37  and assigns the extent determined to the relevant pixel  9 .  
         [0046]     The assignment of the respective type and optionally also the extent of perfusion to the individual pixels  9  defines an evaluation image A. Due to the way in which the evaluation image A is generated, each pixel  9  of the evaluation image A corresponds to the corresponding pixels  9  of the projection images B. In particular the evaluation image A is also two-dimensional and comprises a plurality of pixels  9 . The evaluation facility  8  outputs the evaluation image A to the user  6  via the display device  16  as part of a step S 38 .  
         [0047]     Steps S 35  to S 37 , which relate to the actual core of the invention of DE 10 2005 039 189.3, will be examined in more detail again later.  
         [0048]      FIG. 7  shows an evaluation image A. In accordance with  FIG. 7 , the evaluation facility  8  has converted the extent of perfusion and also the type to color values based on an assignment rule. The evaluation facility  8  consequently outputs the evaluation image A in the form of a color-coded representation to the user  6  via the display device  16 . The evaluation facility  8  can optionally output the assignment rule together with the color-coded representation to the user  6  via the display device  16 .  
         [0049]     It is possible for the computer  8  to display the entire displayed evaluation image A in a color-coded manner. In the meantime however it is preferred that the computer  8  displays the evaluation image A outside the perfusion area in black/white or as a gray-scale image. In particular the computer  8  can also subdivide the evaluation image A outside the perfusion area into parcels  19  and assign a gray-scale or one of the black/white values to the parcels  19  of the evaluation image A outside the perfusion area.  
         [0050]     As an alternative to the representation as shown in  FIG. 7 , it is also possible, as shown in  FIG. 8 , to overlay one of the projection images B in the evaluation image A.  
         [0051]     As can further be seen from  FIG. 7 , other data can also be overlaid in the evaluation image A, for example a first threshold value SW 1 , a limit time GZP, a factor F and further values. The significance of these values will become evident later.  
         [0052]     In accordance with  FIGS. 7 and 8 , only the sub-area  18  is displayed and output. It is however of course also possible to output the entire evaluation image A, over and above the sub-area  18 , to the user  6  via the display device  16  and in this case to mark the sub-area  18  correspondingly as in  FIG. 3 .  
         [0053]     In a step S 39 , the evaluation facility  8  waits for an input from the user  6 . When this input has been made, the evaluation facility  8  checks in a step S 40  whether the input was a confirmation. If this is the case, in a step S 41  the evaluation facility  8  generates a report based on the evaluation image A and assigns the evaluation image A and the report to the projection images B. It then archives at least the projection images B, the evaluation image A and the report as a unit.  
         [0054]     Otherwise the evaluation facility  8  checks in a step S 42  whether the input was an instruction to reject the evaluation image A. In this case, the image evaluation method is simply quit without further ado, without saving the report.  
         [0055]     Otherwise the evaluation facility  8  checks in a step S 43  whether the criteria for defining the type and/or the extent of perfusion are to be changed. If this is the case, in a step S 44  the evaluation facility  8  receives new criteria and goes back to step S 35 .  
         [0056]     Even if the criteria are not to be changed, the user  6  may have selected only a pixel  9  or a group of pixels  9 . In this case, in a step S 45  the evaluation facility  8  receives a corresponding selection of a pixel  9  or of a group of pixels. In a step S 46  it determines for the selected pixel  9  or for the selected group of pixels the temporal profile of the mean value of the corresponding areas of the projection images B, by means of which it determined the extent of perfusion for the selected pixel  9  or the selected group of pixels, and outputs this profile to the user  6  via the display device  16 .  
         [0057]      FIG. 9  shows a possible implementation of steps S 35  to S 37  from  FIG. 6 .  
         [0058]     In accordance with  FIG. 9 , in a step S 51  the evaluation facility  8  subdivides the projection images B into two-dimensional parcels  19 . The subdivision of parcels  19  can be seen for example from  FIG. 3 . According to  FIG. 3  the parcels are rectangular. This is the simplest type of subdivision into parcels  19 . However other parcel forms are also possible, in particular equilateral triangles and regular hexagons.  
         [0059]     The size of the parcels  19  is in principle freely selectable. They must be two-dimensional. Furthermore they should comprise so many pixels  9  that, when a mean value is formed, the noise tends to be averaged out and motion artifacts are negligible, at least as a general rule. On the other hand the resolution should be sufficiently good. It was determined in trials that the parcels  19  should preferably contain between about 60 and around 1,000 pixels  9 , which in the case of rectangular parcels  19  may correspond to an edge length from for example 8×8 pixels to for example 32×32 pixels.  
         [0060]     In steps S 52  and S 53 , the evaluation facility  8  sets the serial indices i, j to the value one. The index i runs sequentially through each parcel  19  of the two-dimensional arrangement of parcels  19  as shown in  FIG. 3 . The index j runs sequentially through the projection images B.  
         [0061]     In a step S 54  the evaluation facility  8  determines the—weighted or unweighted—mean value M(j) of the pixel values of the parcel  19  defined by the index i in the projection image B defined by the index j.  
         [0062]     In a step S 55  the evaluation facility  8  checks whether the index j has already reached its maximum value. If this is not the case, the evaluation facility  8  increments the index j in a step S 56  and goes back to step S 54 , in order to determine the next mean value M(j).  
         [0063]     When all the mean values MO) are determined for a specific parcel  19 , in a step S 57  the evaluation facility  8  uses these mean values M(j) to define the type of the respective parcel  19  and assigns the determined type to a parcel  19  of the evaluation image A—see  FIG. 7 . The parcels  19  of the evaluation image A correspond 1:1 to the parcels  19  of the projection images B.  
         [0064]     The evaluation facility  8  then checks in a step S 58  whether the type determined corresponds to type  3 , i.e. to the type “perfused part of the surroundings”. If this is the case, in a step S 59  the evaluation facility  8  uses the same mean values M(j) to define the extent of perfusion for this parcel  19  and assigns it likewise to the corresponding parcel  19  of the evaluation image A.  
         [0065]     The evaluation facility  8  then checks in a step S 60  whether it has already carried out step S 53  to S 59  for all the parcels  19 . If this is not the case, it increments the index i in a step S 61  and goes back to step S 53 . Otherwise the determination and assignment of the type and also of the extent of perfusion according to the doctrine of DE 10 2005 039 189.3 is terminated.  
         [0066]     According to the doctrine of DE 10 2005 039 189.3 modifications of the method described in conjunction with  FIG. 9  are possible. Thus for example the order of the indices i, j in particular can be swapped. In this case, a number of modified projection images B′ are determined. Each of these modified projection images B′ has a uniform value per parcel  19 , namely the mean value M(j) determined in step S 54 . An example of a projection image B′ modified in this way is shown in  FIG. 10 .  
         [0067]     The procedure according to the invention described above achieves in particular the following features:  
         [0068]     The evaluation facility  8  carries out the assignment of the type based on the temporal profile of the pixel values of the projection images B.  
         [0069]     The evaluation facility  8  carries out the assignment of the type and of the extent of perfusion based on the temporal profile of the pixel values of those pixels  9  of the projection images B, which lie in a two-dimensional evaluation core  19  of the projection images B defined by the respective pixel  9  of the evaluation image A. For the evaluation core  19  corresponds to the respective parcel  19 .  
         [0070]     For the same reason, the evaluation facility  8  also carries out the assignment of type and extent for all the pixels  9  of a parcel  19  in a uniform manner.  
         [0071]     Furthermore the same parcels  19  are used for determining type and for determining extent.  
         [0072]      FIG. 7  and also  FIG. 8  show the outcome of the assignment.  
         [0073]     As an alternative to the parcel-by-parcel assignment of type and extent of perfusion of the individual pixels  9  of the evaluation image A, it would also be possible for the evaluation facility  8  to define a specific two-dimensional evaluation core in the projection images B for each pixel  9  of the evaluation image A, the respective pixel  9  of the evaluation image A being arranged in the center of the respective evaluation core. Even then a fully analogous procedure is possible. However a considerably greater computation outlay would be required for this and it would not be matched by any significant gain in accuracy.  
         [0074]     If a lot of contrast medium is present in the examination object  3 , only a relatively low level of transmission takes place. This produces a relatively low level of brightness (tending toward black) in the projection images B. Conversely, if only a small amount of contrast medium is present in the examination object  3 , a higher level of transmission takes place, as a result of which greater brightness is produced in the projection images B (tending toward white). As a rule, when the projection images B are digitized, black is assigned the pixel value zero, white the maximum possible pixel value e.g. 2 8 −1=255. The converse of the conventional procedure is followed below. Thus white is assigned the pixel value zero and black the maximum possible pixel value. This assignment facilitates understanding of the remarks below. The assignment of zero to white and maximum value to black is however not required in principle.  
         [0075]     It will now be described in conjunction with FIGS.  11  to  13  how the evaluation facility  8  determines the type of the individual parcels  19 . For this purpose the evaluation facility  8  needs two decision criteria, namely the first threshold value SW 1  and the limit time GZP.  
         [0076]     If in a specific parcel  19  in all the projection images B the difference between the mean values M(j) determined and the corresponding mean value M( 1 ) of the first projection image B reaches as a maximum the threshold value SW 1 , the type “background” or “non-perfused part of the surroundings” is assigned to the respective parcel  19 .  FIG. 11  shows a typical example of such a mean-value profile.  
         [0077]     The first threshold value SW 1  can be predefined in a fixed manner. It can for example amount to 5 or 10% of the maximum control range. It can also be defined relative to the mean value M( 1 ) of the respective parcels  19  of the first projection image B. It can amount for example to 10 or 20% of the mean value M( 1 ). The first threshold value SWI is preferably a function of both an input of the user  6  and the mean value M( 1 ) of the corresponding parcel  19  of the temporally first projection image B. This can be achieved in particular in that the user  6  predefines the factor F for the evaluation facility  8  in accordance with a step S 71  shown in  FIG. 14  and in a step S 72  the evaluation facility  8  then defines the first threshold value SW 1  for the respective parcel  19  as a product of the factor F and of the mean value M( 1 ) of the respective parcel  19 .  
         [0078]     If the type of a parcel  19  does not correspond to the type “background”, the parcel  19  must either be assigned the type “vessel” or the type “perfused part of the surroundings”. The limit time GZP serves to distinguish between these two types. If the first threshold value SW 1  is exceeded for the first time before the limit time GZP, the type “vessel” is assigned to a parcel  19 , otherwise the type “perfused part of the surroundings” is assigned.  
         [0079]     The limit time GZP can also be predefined in a fixed manner for the evaluation facility  8 . The limit time GZP also preferably depends on an input by the user  6 . Steps S 73  to S 75 , as shown in  FIG. 14 , are available for this purpose. In step S 73  the evaluation facility  8  receives the limit time GZP from the user  6 . In step S 74  the evaluation facility  8  determines the particular projection image B which lies closest in time to the limit time GZP. It outputs this projection image B as part of step S 74  to the user  6  via the display device  16 . In step S 75  the evaluation facility  8  checks whether the user  6  confirms the limit time GZP or whether said user  6  desires a new predefinition. Accordingly the method either returns to step S 73  or proceeds with a step S 76 , in which the type assignment takes place for the individual parcels  19 .  
         [0080]      FIGS. 12 and 13  each show an example of a temporal profile for a parcel  19  of the type “vessel” and “perfused part of the surroundings”.  
         [0081]     In accordance with step S 76 , the following type assignment takes place: If it is true for all possible indices j that the amount of the difference between the mean value M(j) of the projection image B(j) and the mean value M( 1 ) of the temporally first projection image B( 1 ) is less than the first threshold value SW 1 , type  1  (background) is assigned to the corresponding parcel  19 . If a value for the index j exists, for which the above-mentioned difference exceeds the first threshold value SW 1  and the index j corresponds to a capture time t(j), which lies before the limit time GZP, type  2  (vessel) is assigned to the relevant parcel  19 . Otherwise type  3  (perfused part of the surroundings) is assigned to the relevant parcel  19 .  
         [0082]     In a step S 77  the evaluation facility  8  checks whether the type determined is type  3 . Only if this is the case are steps S 78  to S 80  carried out. Otherwise steps S 78  to S 80  are skipped.  
         [0083]     In step S 78  the evaluation facility  8  carries out a calculation of the extent of perfusion. This calculation can be done in many different ways. This is explained in more detail below in conjunction with  FIG. 15 . It should be mentioned in advance that in the simplest case only two or three values are distinguished for the extent of perfusion, that is only high and low or high, moderate and low. Finer subdivisions are however also possible.  
         [0084]     In accordance with  FIG. 15 , the temporal profile of the mean value M in a parcel  19  of type  3  exceeds the first threshold value SW 1  for the first time at a time T 1 . At a time T 2  the mean value M reaches 90% of its maximum Mmax for example. At a time T 3  the mean value M reaches its maximum Mmax. At a time T 4  the mean value M drops back to 90% of its maximum Mmax for example. At a time T 5  the mean value M drops back to below the first threshold value SW 1 . The numerical value 90% is given only by way of example. A different percentage could of course also be used. Also a correction by a base value M 0  can optionally be carried out. The base value M 0  is defined here for the parcel  19  under consideration as the mean value of the mean values M before the limit time GZP or before the time T 1 .  
         [0085]     In addition to the above-mentioned times T 1  to T 5 , an auxiliary time T 6  can be defined, in which the mean value M exceeds a second threshold value SW 2 . Here the second threshold value SW 2  preferably corresponds to what is known as FWHM (FWHM=full width at half maximum).  
         [0086]     As regards the extent of perfusion, it is possible for the evaluation facility  8  to determine this from one of these variables or from a number of these variables. Several possible evaluations are indicated in DE 10 2005 039 189.3.  
         [0087]     In step S 79  the evaluation facility  8  checks whether the time period T 6  exceeds a minimum time Tmin. If this is not the case, the time period T 6  is extremely short. An example of such a profile is shown in  FIG. 16 . This points with high probability to what is known as an artifact. In this case the evaluation facility  8  skips to step S 80 . In step S 80  it ensures that the corresponding projection image B is ignored with regard to the parcel  19  currently being processed. In the simplest case the respective projection image B (restricted of course to the respective parcel  19 ) is omitted. The evaluation facility  8  preferably makes a replacement. It replaces the parcel  19  being processed with the corresponding parcel  19  of the projection image B immediately preceding in time, of the projection image B immediately succeeding in time or with an interpolation of the corresponding parcels  19  of the projection images B immediately preceding in time and immediately succeeding in time. After step S 80  has been executed, the evaluation facility  8  goes back to step S 78 .  
         [0088]     The image evaluation method of DE 10 2005 039 189.3, as described above, can optionally be refined as required. For example it is possible, after the extent of perfusion has been determined parcel by parcel, to carry out a finer determination. This is described in more detail below in conjunction with  FIG. 17 .  
         [0089]     As shown in  FIG. 17 , the evaluation facility  8  selects a parcel  19  in a step S 91 . In a step S 92  the evaluation facility  8  checks whether type  3  is assigned to the selected parcel  19 . Only if this is the case is the move made to a step S 93 . In step S 93  the evaluation facility  8  calculates the logical auxiliary variable OK. OK assumes the value “true” if, and only if, the selected parcel  19  is completely surrounded by parcels  19  to which type  3  is also assigned.  
         [0090]     The value of the logical auxiliary variable OK is checked in step S 94 . Depending on the result of the check, steps S 95  and S 96  are then executed. In step S 95  the selected parcel  19  is subdivided, for example into 2×2=4 sub-parcels. In step S 96  the evaluation facility  8  carries out a determination and assignment of the extent of perfusion again for each sub-parcel.  
         [0091]     In step S 97  the evaluation facility  8  checks whether it has already carried out steps S 92  to S 96  for all the parcels  19 . If this is not the case, it moves on to a step S 98  by selecting a different, previously not yet selected, parcel  19 . From step S 98  it goes back to step S 92 .  
         [0092]     The procedure shown in  FIG. 17  can of course be modified. Thus for example step S 95  can be brought forward before step S 91  so that it is carried out for all the parcels  19 . Steps S 91  to S 94  and S 97  and S 98  are then carried out respectively with the sub-parcels. Irrespective of whether the one or the other procedure is adopted however, the evaluation facility  8  determines the extent of perfusion again only for those pixels  9  of the evaluation image A, to which the type “perfused part of the surroundings” is assigned and which are surrounded within a predetermined minimum distance (here a parcel  19  or a sub-parcel) exclusively by pixels  9  to which the type “perfused part of the surroundings” is also assigned.  
         [0093]     In the context of the above explanation it was assumed that the recording parameters of the recording arrangement  1 , including the operating parameters of the radiation source  4 , were kept constant during the capturing of the images B. If this prerequisite is not satisfied, brightness fluctuations can occur in the captured images B, which can impair the evaluation and in extreme cases even render it impossible. Within the scope of the doctrine of DE 10 2005 039 189.3, there is therefore provision for carrying out corresponding corrections, so that an evaluation can nevertheless take place. These corrections take place before step S 35  or after step S 44  in  FIG. 6 . They are described in more detail below in conjunction with  FIG. 18 .  
         [0094]     In accordance with  FIG. 18 , in a step S 101  a reference area  20  of the projection images B is firstly defined. In the simplest case the reference area  20  is defined by means of a corresponding user input. The evaluation facility  8  then overlays the reference area  20  into one of the projection images B in a step S 102 . This can be seen for example in  FIG. 3 .  
         [0095]     Next, in a step S 103 , the evaluation facility  8  defines one of the projection images B as a reference image B. It is in principle random, which of the projection images B is defined as the reference image B. As a rule, the first or the last of the projection images B is defined as the reference image B.  
         [0096]     In a step S 104 , the evaluation facility  8  selects one of the projection images B.  
         [0097]     In a step S 105  the evaluation facility  8  compares the selected projection image B with the reference image B. The comparison is carried out only within the reference areas  20  that correspond to one another. In a step S 106  the evaluation facility  8  defines a transformation of the pixel values of the selected projection image B based on the comparison. The transformation is defined such that the mean value of the pixels  9  of the reference area  20  of the transformed projection image B on the one hand and the mean value of the pixels  9  of the reference image B on the other hand have a predetermined functional relationship to one another. The functional relationship can consist in particular of the fact that the mean value of the pixels  9  of the reference area  20  of the transformed projection image B is equal to the mean value of the pixels  9  of the reference image B. The transformation can alternatively be linear or non-linear.  
         [0098]     In accordance with the transformation specified in step S 106 , in a step S 107  the evaluation facility  8  transforms all the pixels  9  of the selected projection image B, in other words both the pixels  9  inside the reference area  20  and the pixels  9  outside the reference area  20 .  
         [0099]     In a step S 108 , the evaluation facility  8  checks whether it has already carried out steps S 104  to S 107  for all the projection images B. If this is not yet the case, it moves on first to a step S 109  in which it selects another of the projection images B. It then goes back to step S 105 . Otherwise the transformation of the projection images B is terminated.  
         [0100]     As an alternative to the user  6  predefining the reference area  20 , it is possible for the evaluation facility  8  to determine the reference area  20  automatically. For example the evaluation facility  8  can determine the reference area  20  from the pixels  9  of the evaluation image A, to which it has assigned the type “non-perfused part of the surroundings”, in other words type  1 . Parcels  19  which lie outside the exposure area are not taken into account here. This is described in more detail in DE 10 2005 039 189.3.  
         [0101]     In contrast to the known prior art, with DE 10 2005 039 189.3 it is no longer necessary for the user to predefine which area of the projection images corresponds to the myocardium. Rather the type assignment can take place from the projection images themselves.  
         [0102]     The image evaluation method in DE 10 2005 039 189.3 is universally applicable. It is also applicable in particular therefore, when the examination object is not moving. An example of such an examination object is the human brain, in which the same blood supply problems can occur as in the human heart. When they occur acutely, these blood supply problems are known as a stroke.  
         [0103]     As already mentioned, the present invention is based on this doctrine. An understanding of it is therefore assumed below. This doctrine is also an integral part of the present invention, unless the statements below contain information to the contrary.  
         [0104]     An object of the present invention is to create an image evaluation method, which allows an image evaluation, which is optimized compared with the image evaluation described in DE 10 2005 039 189.3.  
         [0105]     The object is achieved by an image evaluation method with the features of an independent claim.  
         [0106]     According to the invention the object is thus achieved by means of the following features:  
         [0107]     A computer assigns a two-dimensional evaluation core that is uniform for all corresponding pixels to pixels of projection images that correspond to one another at least in a sub-area of the projection images that is uniform for the projection images.  
         [0108]     The computer determines at least one characteristic value for each pixel within each projection image based on the evaluation core assigned to the respective pixel and assigns this value to the relevant pixel.  
         [0109]     The computer determines parameters of at least one function of time based on the temporal profile of the characteristic values assigned to corresponding pixels, so that any deviation between the function of time parameterized with the parameters and the temporal profile of the characteristic values assigned to corresponding pixels is minimized.  
         [0110]     The computer uses the parameters to determine a type and/or an extent and assigns the type and/or extent to a pixel of a two-dimensional evaluation image corresponding to the pixels of the projection images.  
         [0111]     The type is characteristic of whether the respective pixel of the evaluation image corresponds to a vessel of a vascular system, a perfused part of the surroundings of a vessel of the vascular system (perfusion area) or a non-perfused part of the surroundings of a vessel of the vascular system (background). The extent is characteristic of the extent of perfusion (degree of perfusion).  
         [0112]     The computer outputs at least the sub-area of the evaluation image to the user via a display device.  
         [0113]     In a corresponding manner the object for the data medium or computer is achieved in that a computer program for carrying out such an image evaluation method is stored on the data medium or such a computer program is filed in the mass memory of the computer, in such a manner that the computer executes said image evaluation method after the computer program has been called.  
         [0114]     The first two and last two of the aforementioned features indented with a dash above are already described in DE 10 2005 039 189.3. The two middle features indented with a dash are however novel in respect of DE 10 2005 039 189.3.  
         [0115]     The computer preferably assigns the maximum of the pixel values occurring in the evaluation core assigned to the respective pixel to the pixels as a characteristic value. In this case it is possible for the computer to use the temporal profile of the maximum to define parameters of a maximum function, so that any deviation between the maximum function parameterized with the parameters and the temporal profile of the maximum is minimized. It is also possible in this case for the computer to use the parameters of the maximum function to determine whether it assigns the type “vessel” to the corresponding pixel of the evaluation image.  
         [0116]     This procedure is novel in respect of DE 10 2005 039 189.3, as only mean values are processed there.  
         [0117]     Alternatively or additionally it is possible for the computer to assign the mean value of the pixel values occurring in the evaluation core assigned to the respective pixel to the pixels as a characteristic value. This procedure is known from the start from DE 10 2005 039 189.3.  
         [0118]     The last-described procedure is possible as an alternative to determining the maxima as characteristic values. It is preferably carried out as an addition.  
         [0119]     If the computer  8  determines the mean values as characteristic values, it is possible for the computer to use the temporal profile of the mean value to define parameters of a mean value function, so that any deviation between the mean value function parameterized with the parameters and the temporal profile of the mean value is minimized. In this case the computer can use the parameters of the mean value function to determine whether it assigns the type “perfusion area” or the type “background” to the corresponding pixel of the evaluation image and/or can use the parameters of the mean value function to determine which degree of perfusion it assigns to the corresponding pixel.  
         [0120]     The mode of operation in principle described above already provides very good results. It can be even further improved using the procedures described below.  
         [0121]     It is thus for example possible for the computer to determine a histogram of the pixel values occurring in the respective evaluation core to define the mean value and to determine said mean value using the histogram. In particular the computer can use the histogram to determine statistic variables relating to the distribution of the pixel values and to decide based on the statistic variables, which of the pixel values it takes into account when determining the mean value.  
         [0122]     The parameterizable function can be adapted to the overall anticipated blush profile. In this case the at least one parameterizable function increases from an initial value to a maximum value and then decreases from the maximum value to a final value, as time progresses. For example the function can have the form 
 
 y=K 1*(1+ e   −a(t−T′) ) −1 ·(1 +e   b(t−T″) ) −1   +K 2 
 
 where y is the function value, t the time and K 1 , K 2 , a, b, T′ and T″ are the parameters of the function. 
 
         [0123]     It is possible for the size of the evaluation core to be independent of location. However the size of the evaluation core is preferably a function of location. For example in the outer region, where no or only a small amount of image information is expected, a large evaluation core can be established with a smaller evaluation core in the inner area.  
         [0124]     The size of the evaluation core for a specific pixel can be determined beforehand. However the computer preferably determines the size of the evaluation core iteratively based on the type assigned to the pixels, at least for some of the pixels.  
         [0125]     It is currently preferable for the computer to subdivide the projection images and the evaluation image into parcels, for the computer to carry out the type and/or extent assignment parcel by parcel and for the evaluation core to correspond to the respective parcel.  
         [0126]     In particular if the iterative determination of the size of the evaluation core and the subdivision of the images into parcels are combined, it is possible for the computer to subdivide parcels, to which it has assigned the type “vessel” in one of the iterations, into sub-parcels and to carry out an iteration again, if the relevant parcel is surrounded in an angle range, which is larger than a first minimum angle range, by parcels, to which the computer did not assign the type “vessel” in the first-mentioned iteration, and the parcel is larger than a minimum size.  
         [0127]     It is possible for the computer to retain the sub-parcels as such. However it is preferable for the computer to combine sub-parcels, to which it does not assign the type “vessel” in the new iteration, with a parcel adjacent to the relevant sub-parcel, to which the type “vessel” is also not assigned, to form an overall parcel. This measure can reduce computation outlay.  
         [0128]     Computation outlay can be further reduced, if the computer combines adjacent parcels of the same type to form an overall parcel. This is particularly so, where parcel combination is carried out for parcels of the type “perfusion area”.  
         [0129]     It is possible for the computer to change the type to “background” for parcels, to which it assigned the type “perfusion area”, if the relevant parcel is surrounded in an angle range, which is larger than a second minimum angle range, by parcels, to which the computer has assigned the type “background”. It is possible in particular to eliminate outliers with this procedure.  
         [0130]     It is possible for the computer always to carry out the type change, when the last-mentioned condition is satisfied. It is alternatively also possible for the computer only to carry out the type change, when the relevant parcel is surrounded exclusively by parcels of the type “background” and/or the characteristic values of the relevant parcel satisfy a change condition.  
         [0131]     The evaluation of the projection images can be further optimized, if the computer determines internally related areas of the evaluation image, in which it has assigned exclusively the type “perfusion area” to the parcels and defines the size of the parcels within the respective area as a function of the size of the respective area. In particular it is possible to select the parcels to be even smaller, the smaller the internally related area.  
         [0132]     It is possible for the computer to carry out a vessel segmentation for each projection image based on the pixel values of the respective projection image and to take into account the vessels identified based on the vessel segmentation during the type assignment. This procedure allows more defined separation of the vessels of the vascular system from the remainder of the image.  
         [0133]     For example the computer can use the vessels identified in the projection images to determine the vascular system and to take the vascular system as a whole into account during the type assignment.  
         [0134]     The evaluation of the projection images is on the one hand particularly simple and on the other hand particularly true to reality, if the computer uses the parameters to determine a characteristic input time for inputting the contrast medium into the corresponding evaluation core for a pixel under consideration and a characteristic washout time for washing the contrast medium out from the corresponding evaluation core and determines the degree of perfusion based on the input and washout times.  
         [0135]     The computation time for the required image evaluation can be reduced, if the computer only assigns the type “vessel” to a pixel of the evaluation image, if the parameterized function of time demonstrates a predefined minimum increase before a limit time, the computer defines the parameters of the function exclusively based on the characteristic values of the pixels of the projection images, which are before the limit time in time, and the projection images, which are as a maximum a predefined limit after the limit time in time, and carries out assignment of the type “vessel” based on the parameters thus defined.  
         [0136]     The evaluation of the projection images can be even further optimized, if the computer uses at least two different parameterizable functions to determine the individual types and/or the degree of perfusion. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0137]     Further advantages and details will emerge from the description which follows of exemplary embodiments in conjunction with the drawings, in which in schematic representation:  
         [0138]      FIG. 1  shows a block diagram of a recording arrangement, a control computer and an evaluation facility,  
         [0139]      FIG. 2  shows a flow diagram,  
         [0140]      FIG. 3  shows an example of a projection image,  
         [0141]      FIG. 4  shows a block diagram of an evaluation facility,  
         [0142]      FIGS. 5 and 6  show flow diagrams,  
         [0143]      FIG. 7  shows an evaluation image,  
         [0144]      FIG. 8  shows the evaluation image from  FIG. 7  with an overlaid projection image,  
         [0145]      FIG. 9  shows a flow diagram,  
         [0146]      FIG. 10  shows an intermediate image derived from a projection image,  
         [0147]     FIGS.  11  to  13  show temporal profiles of mean values,  
         [0148]      FIG. 14  shows a flow diagram,  
         [0149]      FIG. 15  shows a temporal profile of a mean value,  
         [0150]      FIG. 16  shows a further temporal profile of a mean value,  
         [0151]     FIGS.  17  to  19  show flow diagrams,  
         [0152]      FIG. 20  shows a projection image,  
         [0153]      FIG. 21  shows a flow diagram,  
         [0154]      FIGS. 22 and 23  show time diagrams,  
         [0155]      FIGS. 24 and 25  show flow diagrams,  
         [0156]      FIG. 26  shows an evaluation core and a histogram,  
         [0157]      FIG. 27  shows a flow diagram,  
         [0158]      FIG. 28  shows parcels and  
         [0159]      FIGS. 29 and 30  show flow diagrams. 
     
    
     DETAILED DESCRIPTION OF INVENTION  
       [0160]     Large sections of the present invention correspond to the procedure described in DE 10 2005 039 189.3. The above comments relating to FIGS.  1  to  8  also apply within the scope of the present invention. Thus for example the present invention relates not only to an image evaluation method as such but also—see also  FIG. 4 —to a data medium  13  with a computer program  12  stored on the data medium  13 , to carry out an inventive image evaluation method. It also relates—see also  FIG. 4  again—in addition to the image evaluation method to a computer  8  with a mass memory  11 , in which a computer program  12  is filed, so that the computer  12  executes an evaluation method of this type after the computer program  12  has been called.  
         [0161]     The essential difference between the present invention and the procedure in DE 10 2005 039 189.3 is the implementation of steps S 35  to S 37  in  FIG. 6 . These differences are examined in more detail below. The wording of DE 10 2005 039 189.3 and the reference characters used there (which were also used in FIGS.  1  to  18 ) are therefore retained, in as far as this is possible and expedient.  
         [0162]      FIG. 19  shows a relatively general possibility for implementing steps S 35  to S 37  in  FIG. 6  according to the present invention.  
         [0163]     According to  FIG. 19 , in a step S 201  the computer  8  defines an evaluation core  19  for each pixel  9  in the projection images B and assigns it to the relevant pixel  9 . The evaluation core  19  is uniform for all pixels  9  corresponding to one another in the projection images B.  
         [0164]     For example the computer  8  can define an individual evaluation core  19  for every pixel  9 , comprising all the pixels  9 , which are at a distance from the relevant pixel  9  within the projection images B, said distance not exceeding a maximum distance, or which are located in a parcel  19  of predefined contour, with the relevant pixel  9  being arranged in the center of this parcel  19 .  
         [0165]     In step S 201  the computer  8  preferably subdivides the projection images B -as in the procedure in DE 10 2005 039 189.3—into two-dimensional parcels  19  and assigns the respective parcel  19  as an evaluation core  19  to each pixel  9  contained in a parcel  19 . In this case the evaluation core  19  corresponds to the respective parcel  19 .  
         [0166]     In the case of parcel assignment the subsequent determination and assignment of type (vessel, perfusion area or background) and extent (or degree of perfusion) take place parcel by parcel. This significantly reduces computation outlay.  
         [0167]     The size of the evaluation core  19  can—with or without parceling—be independent of location. This case is shown by way of example in  FIGS. 3, 7 ,  8  and  10 . The size of the evaluation core  19  is preferably a function of its location. This is shown in  FIG. 20 .  
         [0168]     In particular the size of the evaluation core  19  in an outer region  18 a of the projection images B can be relatively large. For there is generally no relevant incorrect information contained there. A maximum size of for example 2,000 to 4,000 pixels should however not be exceeded even in the outer region.  
         [0169]     In an inner region  18   b  of the projection images B the size of the evaluation core  19  should be smaller. For the relevant image information is generally contained in the inner region  18   b.  The size should however not be less than the minimum size of for example 16 to 65 pixels.  
         [0170]     The inner region  18   b  can be predefined in a fixed manner for the computer  8 . It is preferably defined automatically by the computer or predefined for the computer  8  by the user  6 .  
         [0171]     In step S 201  the computer assigns the respective evaluation core  19  to the pixels  9  at least if the relevant pixels  9  are located in the sub-area  18  of the projection images B. The sub-area  18  can also be predefined in a fixed manner or can be defined by the user  6 . It is also possible for the assignment of the evaluation cores  19  to take place in the projection images B as a whole. In any case the evaluation core  19  assigned to a specific pixel  9  is however uniform for all the projection images B.  
         [0172]     In a step S 202  the computer  8  defines at least one characteristic value C 1 , C 2  within every projection image B for every pixel  9  (parcel by parcel in the case of the parcels  19 ) based on the evaluation core  19  (e.g. the respective parcel  19 ) assigned to the respective pixel  9  and assigns it to the respective pixel  9  (or the respective parcel  19 ). For example the computer  8  can—in the same manner as DE 10 2005 039 189.3—assign the mean value C 1  of the pixel values of the pixels  9  contained in the relevant evaluation core  19  as the first characteristic value C 1 . Alternatively or additionally the computer  8  can assign the maximum of the pixel values of the pixels  9  contained in the relevant evaluation core  19  as the second characteristic value C 2 .  
         [0173]     It is possible for the computer  8  to subtract another projection image B (hereafter referred to as reference image B) from the selected projection image B before determining the at least one characteristic value C 1 , C 2 . This is shown by a step S 203  in  FIG. 19 . Step S 203  is however only optional and therefore only shown in  FIG. 19  with a dashed line.  
         [0174]     In principle any projection image B can be a reference image B within the meaning of step S 203 . For example the first projection image B in time can be used. A specific reference image B can also be used for every projection image B.  
         [0175]     In a step S 204  the computer  8  defines parameters of at least one parameterizable function of time for every sequence of characteristic values C 1 , C 2  (=for the temporal profile of the characteristic values C 1 , C 2 ). Definition takes place in such a manner that any deviation between the functions of time parameterized with the parameters and the temporal profile of the corresponding characteristic value C 1 , C 2  is minimized. Such a procedure is generally known as “fitting functions”.  
         [0176]     In the present case, in which the computer  8  determines both the first and second characteristic value C 1 , C 2 , the computer  8  uses the temporal profile of the first characteristic values C 1  to determine parameters of at least one first function (hereafter referred to as the mean value function) and the temporal profile of the second characteristic values C 2  to determine parameters of at least one second function (hereafter referred to as the maximum function).  
         [0177]     Generally the number of parameters of the parameterizable functions is smaller than the number of projection images B. This is however not essentially so.  
         [0178]     In a step S 205  the computer  8  uses the parameters of the at least one mean value function and/or the parameters of the at least one maximum function to determine the type (vessel, perfusion area or background) and/or extent of perfusion of the respective pixel  9  and assigns the type and/or extent to the corresponding pixel  9  of the evaluation image A. If the assignment of type and/or extent takes place parcel by parcel, the determination of type and/or extent can of course also take place parcel by parcel.  
         [0179]     After executing step S 205  in  FIG. 19 , the computer outputs at least the sub-area  18  of the evaluation image A to the user  6  via a display device  16 . This output takes place as in the procedure in DE 10 2005 039 189.3. The user  6  can optionally adjust the setting values as part of this output. For example the user  6  can predefine the limit time GZP and the threshold values SW 1 , SW 2 .  
         [0180]     In DE 10 2005 039 189.3 the determination of type and degree of perfusion takes place solely based on the mean value (referred to there with the reference character M(j)) of the individual parcels  19 . With the present invention the sequence of mean values M(j) corresponds to the sequence of first characteristic values C 1 .  
         [0181]     Within the scope of the present invention it is also possible to work exclusively with the mean values C 1 . However a different procedure is preferred. This procedure is described below in conjunction with  FIG. 21 .  
         [0182]      FIG. 21  shows a possible refinement of step S 205  in  FIG. 19 . According to  FIG. 21  in a step S 211  the computer  8  selects a pixel  9  of the evaluation image A. In a step S 212  the computer  8  checks whether the type “vessel” is to be assigned to the selected pixel  9  and optionally assigns it to the selected pixel  9 . Contrary to the doctrine of DE 10 2005 039 189.3 this check (optionally including assignment).takes place on the basis of the parameters of the maximum function.  
         [0183]     In a step S 213  the computer  8  checks whether it has assigned the type “vessel” to the selected pixel  9 . If this is not the case, the computer moves on to a step S 214 . In step S 214  the computer  8  determines whether it is to assign the type “perfusion area” or the type “background” to the selected pixel  9 . Furthermore in step S 214  it assigns the corresponding type to the selected pixel  9 . The computer  8  preferably carries out this type definition and assignment based on the parameters of the mean value function.  
         [0184]     In a step S 215  the computer  8  checks whether it has assigned the type “perfusion area” to the selected pixel  9 . If this is the case, the computer  8  moves on to a step S 216 . In step S 216  the computer  8  determines which degree of perfusion it is to assign to the selected pixel  9 . It also carries out the corresponding assignment in step S 216 . The determination in step S 216  preferably also takes place based on the parameters of the mean value function.  
         [0185]     If the computer  8  determines parameters of a number of mean value functions, the same or different mean value functions can alternatively be used for the type assignment (perfusion area or background) and extent assignment.  
         [0186]     In a step S 217  the computer  8  checks whether it has already carried out steps S 212  to S 216  for all the pixels  9  of the evaluation image A (or the sub-area  18  of the evaluation image A). If this is not the case, the computer  8  moves on to a step S 218 , in which it selects a different pixel  9  of the evaluation image A. From step S 218  the computer  8  goes back to step S 212 . Otherwise the method according to  FIG. 21  is terminated.  
         [0187]     The procedure in  FIG. 21  can be implemented parcel by parcel, if the projection images B and the evaluation image A are parceled.  
         [0188]     The mean value function, on the basis of which the computer  8  distinguishes the types “degree of perfusion” and “background” and on the basis of which primarily it determines and assigns the degree of perfusion, should preferably have the typical profile of what is known as a blush. The mean value function should therefore (independently of its specific parameterization) increase from an initial value to a maximum value and decrease from the maximum value to a final value, as time progresses.  FIG. 22  shows such a function by way of example and also the mean values C 1  of the corresponding evaluation cores  19  of the projection images B.  
         [0189]     The exemplary mean value function in  FIG. 22  has parameters K 1 , K 2 , a, b, T′ and T″. These parameters K 1 , K 2 , a, b, T′, T″ are optimized such that the deviation between the function of time parameterized with the parameters K 1 , K 2 , a, b, T′, T″ and the temporal profile of the mean values C 1  is minimized.  
         [0190]     As mentioned above, the computer  8  uses the parameters K 1 , K 2 , a, b, T′, T″ to determine a degree of perfusion and assigns the degree of perfusion to the respective parcel  19 . For example the computer  8  can use the parameters K 1 , K 2 , a, b, T′, T″ to determine three times TA, TB, TC.  
         [0191]     The time TB preferably corresponds to the time, when the mean value function reaches its maximum value.  
         [0192]     The time TA can for example be defined in that the mean value function increases the most at time TA or that at time TA the mean value function has accomplished the increase from its initial value to its maximum value by a predefined percentage (in particular approx. 40 to 60%, e.g. 50%). The difference between the times TA, TB is characteristic of an input period T 7 , in which the contrast medium is input into the evaluation core  19  of the relevant pixel  9 .  
         [0193]     Similarly the time TC can for example be defined in that the mean value function decreases the most at time TC or that at time TC the mean value function has accomplished the decrease from its maximum value to its final value by a predefined percentage (in particular approx. 40 to 60%, e.g. 50%). The difference between the times TB and TC is characteristic of a washout time T 8 , in which the contrast medium is washed out of the evaluation core  19  of the relevant pixel  9 .  
         [0194]     The quotient (e.g. T 8 /T 7 ) of and/or the difference (e.g. T 8 -T 7 ) between the two periods T 7 , T 8  in this case forms a good basis for determining the degree of perfusion. In particular TIMI blush grade  2  can be assigned to the respective parcel  19 , if the quotient is within a predefined interval. If the quotient is outside the interval, TIMI blush grade  1  or TIMI blush grade  3  is assigned to the parcel  19 . Whether TIMI blush grade  1  or TIMI blush grade  3  is assigned to the parcel  19  is a function of whether the quotient is greater than the upper limit of the interval or smaller than the lower limit of the interval. The interval limits can for example be defined on the basis of empirical values.  
         [0195]     TIMI blush grade  0  cannot be determined with the last-described procedure. This can however be tolerated within the scope of the present invention, since within the scope of the present invention the type “background” is assigned to parcels  19 , to which blush grade  0  should be assigned according to the TIMI classification.  
         [0196]     The maximum function can have the same parameters as the mean value function. The specific values of the parameters in this case of course have to be defined independently of the parameters of the mean value function. To determine the parameters of the maximum function the first characteristic values C 1  (in other words the means values) are in particular not used, but the second characteristic values C 2  (in other words the maxima) are used.  
         [0197]     Alternatively it is possible for the computer  8  to determine the type “vessel” using a function which can be parameterized differently from the function used to distinguish between the types “background” and “perfusion area” and/or to determine the degree of perfusion. For example a function with the form 
 
 y=K 1(1 +e   −a(t−T′) ) −1   +K 2 
 
 can be used to define the type “vessel”. A function of this type is shown in  FIG. 23 . 
 
         [0198]     It is furthermore possible, when distinguishing between the types “background” and “perfusion area” to use a function other than the function, on the basis of which the blush grade is defined. A specific parameterizable function can thus also be used for this function. In particular for example a function can be used, by means of which a linear (in particular constant profile) can be clearly identified with statistical scattering around the straight line thus defined. It is possible in this manner for example to determine parcels  19  of the type “background”. Once the parcels of the type “vessel” and the parcels  19  of the type “background” have been determined, in this case the remaining parcels  19  must be of the type “perfusion area”.  
         [0199]     As a rule—as already described from the start in DE 10 2005 039 189.3—the computer  8  only assigns the type “vessel” to a pixel  9  (or a parcel  19 ) of the evaluation image A, if the temporal profile of the characteristic values C 1 , C 2  shows a predefined minimum increase before the limit time GZP, for example (see also  FIG. 12 ) exceeding the threshold value SW 1 . It is therefore possible for the computer in accordance with  FIG. 24  in a step S 221  to select the characteristic values C 1 , C 2  of the projection images B, which 
        are before the limit time GZP in time or     are as a maximum a time limit behind the limit time GZP in time.        
 
         [0202]     The time limit is preferably defined such that only one or maximum two projection images B are taken into account, which are behind the limit time GZP in time. The reason for taking these projection images B into account is that it is easier to identify from these projection images B behind the limit time GZP, whether there is actually a significant increase before the limit time GZP or whether it is simply an outlier (see also  FIG. 16 ). In some instances outliers can be identified in this manner and not be taken into account.  
         [0203]     In a step S 222  the computer  8  determines parameters of at least one function of time based on the temporal profile of the selected characteristic values C 1 , C 2 . In particular the computer  8  can for example define parameters K 1 , K 2 , a, T′ of a corresponding maximum function based on the second characteristic values C 2 .  
         [0204]     The procedure according to  FIG. 24  can be realized with the first characteristic values C 1 , which are characteristic of the mean values of the evaluation cores  19 . However it is preferably realized with the second characteristic values C 2 , which are characteristic of the maxima of the evaluation cores  19 .  
         [0205]     In a step S 223  the computer  8  checks—e.g. based on the parameters K 1 , K 2 , a, T′ defined in step S 222 —whether the type “vessel” is to be assigned to the respective pixel  9  and optionally carries out this assignment.  
         [0206]     It is in particular possible to identify arteries using the procedure described above. In order also to be able to identify veins, it is possible also to assign the type “vessel” to a parcel  19 , if the above-mentioned minimum increase (or a slightly smaller increase) only occurs after a further limit time, which is a sufficiently long time after the first-mentioned limit time GZP.  
         [0207]     To determine the mean values (in other words the first characteristic values C 1 ) of the individual evaluation cores  19 , it is usually possible to form the general mean value (in other words taking into account all pixel values). The mean value C 1  is preferably determined in such a manner, as described below in conjunction with  FIGS. 25 and 26 .  
         [0208]     According to  FIG. 25  in a step S 231  the computer  8  determines a histogram H of the pixel values occurring in the respective evaluation core  19 . An example of such a histogram H is shown in  FIG. 26 .  
         [0209]     In a step S 232  the computer  8  determines statistical variables of the histogram H. In particular the computer  8  can determine the mean value MW and the standard deviation SAW of the pixel values of the respective evaluation core  19 . All the pixel values occurring are taken into account when determining the mean value MW.  
         [0210]     In a step S 233  the computer  8  decides, based on the statistical variables MW, SAW, which of the pixel values it takes into account when determining the first characteristic value C 1 . For example it may only take into account pixel values, which deviate from the mean value MW by less than the standard deviation SAW.  
         [0211]     The procedure described above already provides a very good outcome. The procedure can however be even further optimized.  
         [0212]     Thus it is for example possible for the computer  8  to determine the size of the evaluation core  19  iteratively based on the type assigned to the pixels  9 , at least for some of the pixels  9 . This procedure can be expedient in particular for the pixels  9 , to which the type “vessel” is assigned. This is described in more detail below in conjunction with  FIGS. 27 and 28  for a parcel  19 . The procedure in  FIGS. 27 and 28  could also be realized, if the evaluation core  19  was defined individually for each individual pixel  9 .  
         [0213]      FIG. 27  shows a possible detailed refinement of steps S 201  to S 205  in  FIG. 19 . The difference formation, which is in principle possible in step S 203  in  FIG. 19  is not shown in  FIG. 27 .  FIG. 28  shows a number of parcels  19 .  
         [0214]     According to  FIG. 27  in a step S 241  the computer  8  subdivides the projection images B into parcels  19 . In a step S 242  the computer  8  determines for every parcel  19 , whether the type “vessel” is to be assigned to the respective parcel  19 . For example it determines the maximum function, as in steps S 211  and S 212  in  FIG. 21 , and checks whether the maximum function increases to above the first threshold value SW 1  before the limit time GZP in accordance with steps S 2221  to S 223  in  FIG. 24 . The computer  8  optionally assigns the type “vessel” temporarily to the parcels  19  as part of step S 242 .  
         [0215]     In a step S 243  the computer  8  selects a parcel  19 , to which the type “vessel” is temporarily assigned. In a step S 244  the computer  8  checks whether the selected parcel  19  exceeds a minimum size. The minimum size can be between 60 and 250 pixels for example.  
         [0216]     If the size of the parcel  19  does not exceed the minimum size, the computer  8  finally assigns the type “vessel” to the selected parcel  19  in a step S 245 .  
         [0217]     If the size of the selected parcel  19  exceeds the minimum size, in a step S 246  the computer  8  defines an angle range, in which the selected parcel  19  is surrounded by parcels  19 , to which the type “vessel” is not assigned, neither temporally nor finally. The center (=vertex), in relation to which the angle range is determined, is located within the selected parcel  19 , in particular in the center of mass.  
         [0218]     In a step S 247  the computer  9  checks whether the angle range defined in step S 246  is greater than a first minimum angle range. If this is not the case, the computer  8  assigns the type “vessel” finally to the relevant parcel  19  in a step S 248 .  
         [0219]     The first minimum angle range can for example be 90° or 180°. It can also have any intermediate value. It can also be sufficient, if the selected parcel  19  is adjacent to at least one further parcel  19 , to which the type “vessel” is assigned neither temporarily nor finally.  
         [0220]     If the first minimum angle range is exceeded, in a step S 249  the computer  8  subdivides the selected parcel  19  into sub-parcels  19 a. In a step S 250  the computer  8  also assigns the type “vessel” temporarily to the sub-parcels  19 . The sub-parcels  19 a are processed in the same manner as normal parcels  19  for the further method in  FIG. 27 .  
         [0221]     In a step S 251  the computer  8  checks whether all the parcels  19 , to which the type “vessel” is assigned, have already been finally assigned this type. If this is not the case, the computer  8  goes back to step S 243 .  
         [0222]      FIG. 28  shows by way of example the advantage of the procedure in  FIG. 27 . In accordance with  FIG. 28 , the selected parcel  19  is divided—purely by way of example—into four equally sized sub-parcels  19   a . If it is assumed for example that a parcel  19  is identified as a vessel, if the maximum of the pixel values exceeds the value  40 , in the example in  FIG. 28  only the left upper sub-parcel  19   a  would have to be classified as a vessel. The three other sub-parcels  19   a  would either be background or perfusion area.  
         [0223]     The procedure shown in  FIG. 28  is not the only one possible. It would for example also be possible, in the case of the parcel  19  in  FIG. 28 , gradually to reduce the parcel  19  slightly at each edge or each corner and to check whether the part of the parcel  19 , which is now no longer contained in the reduced parcel  19   a , should not be classified as a vessel. It would be possible in this manner to draw a more precise boundary, which could possibly be accurate to one pixel.  
         [0224]     As part of the procedure in  FIG. 27 , as described above in conjunction with  FIG. 28 , the computer  8  as a rule does not also finally assign the type “vessel” in full to all the parcels  19 , to which the type “vessel” is temporally assigned. As a rule sub-parcels  19   a  result, to which the type “vessel” is not assigned. It is possible to process the resulting sub-parcels  19   a  as independent parcels  19 , to which the computer  8  later assigns one of the types “perfusion area” or “background”. It is also possible to combine the sub-parcels  19   a  with an adjacent parcel  19 , to which the type “vessel” is not assigned (neither temporarily nor finally) to form an overall parcel  19   c . This is described in more detail below in conjunction with  FIGS. 28 and 29 .  
         [0225]      FIG. 29  shows a modification of steps S 249  to S 251  in  FIG. 27 . In accordance with  FIG. 29  step S 250  can be replaced by steps S 261  to S 264 .  
         [0226]     In step S 261  the computer  8  determines those of the sub-parcels  19   a , to which the type “vessel” is to be assigned and assigns the type “vessel” temporarily to said sub-parcels  19   a.    
         [0227]     The remaining sub-parcels  19   a , to which the type “vessel” is not temporarily assigned, are combined by the computer  8  in step S 262  with an adjacent parcel  19   b , to which the type “vessel” is also not assigned (neither temporarily nor finally) to form an overall parcel  19   c.    
         [0228]     It is possible always to execute the step S 262 . It is also possible to check, before executing step S 262 , whether the resulting overall parcel  19   c  exceeds a maximum size, and then only to carry out the combination, if the maximum size is not exceeded. The check to determine whether the maximum size is exceeded preferably takes place after the combination of the parcels  19   a ,  19   b . In this case the computer  8  checks in step S 263 , whether the overall parcel  19   c  exceeds the maximum size. If this is the case, the computer  8  divides the overall parcel  19   c  in step S 264  into two parcels  19 , preferably having an identical size.  
         [0229]     As already described in conjunction with  FIG. 21 , the computer  8  assigns either the type “perfusion area” or the type “background” to the parcels  19 , to which it does not assign the type “vessel”. It also assigns a degree of perfusion to the parcels  19  of the type perfusion area. This procedure, described in principle in conjunction with  FIG. 21 , can also be further optimized. This is described in more detail below in conjunction with  FIG. 30 . It is pointed out earlier in this context that as part of the procedure in  FIG. 30  the assignment of the type “perfusion area” is initially only temporary. It is also assumed in the context of  FIG. 30  that the parcels  19  have already been assigned their type.  
         [0230]      FIG. 30  shows a possible implementation of steps  214  to S 217  in FIG- 21 . In accordance with  FIG. 30  in a step S 271  the computer  8  selects a parcel  19  of the type “perfusion area”. In a step S 272  the computer  8  determines a logical variable OK for the selected parcel  19 . The logical variable OK assumes the value “TRUE” if, and only if, an angle range, in which the selected parcel  19  is completely surrounded by parcels  19  of the type “background”, is greater than a second minimum angle range. The angle range is determined as in step S 246  in  FIG. 27 .  
         [0231]     The second minimum angle range is as a rule greater than the first minimum angle range. In extreme cases it can be so great that the logical variable OK can only assume the value “TRUE”, if the selected parcel  19  is completely surrounded by parcels  19  of the type “background”.  
         [0232]     In a step S 273  the computer  8  checks the value of the logical variable OK. If the logical variable OK has the value “TRUE”, the computer  8  moves on to a step S 274 . In step S 274  the computer  8  checks whether the selected parcel  19  is completely surrounded by parcels  19  of the type “background”. If this is the case, the computer  8  moves on to a step S 275 , in which it assigns the type “background” to the selected parcel  19 .  
         [0233]     If the selected parcel  19  is not completely surrounded by parcels  19  of the type “background”, the computer  8  moves on to a step S 276 . In step S 276  the computer  8  checks whether the characteristic values C 1 , C 2  of the selected parcel  19  of the projection images B (or variables derived therefrom, for example the parameters of the mean value function and/or the maximum function) satisfy a change condition. If the change condition is satisfied, the computer  8  also moves on to step S 275 .  
         [0234]     If the change condition is not satisfied, or if the logical variable OK has the value “UNTRUE”, the computer  8  moves on to a step S 277 . In step S 277  the computer  8  determines the degree of perfusion and assigns it to the corresponding parcel  19 . It also assigns the type “perfusion area” finally to the selected parcel  19 .  
         [0235]     In a step S 278  the computer  8  checks whether it has already carried out steps S 271  to S 277  for all the parcels  19  of the type “perfusion area”. If this is not the case, the computer  8  goes back to step S 271 . Otherwise the procedure is terminated in accordance with  FIG. 30 .  
         [0236]     Step S 274  in  FIG. 30  is only optional. It can therefore be omitted. In particular step S 274  is meaningless, if the check in step S 274  is already implied in step S 273 .  
         [0237]     If step S 274  is omitted, step S 276  can be inserted between steps S 273  and S 275 . It can alternatively be omitted.  
         [0238]     Determination of the degree of perfusion can be optimized. This is described in more detail below in conjunction with  FIG. 31 . In the context of  FIG. 31  it is assumed that the assignment of the type “perfusion area” to the parcels  19  is at first only temporary.  
         [0239]     The method in accordance with  FIG. 31  can be realized independently of the method in  FIG. 30 . If it is combined with the method in  FIG. 30 , it follows the method in accordance with  FIG. 30 . In this case step S 277  in  FIG. 30  is omitted, in so far as it relates to the determination and assignment of the degree of perfusion. For this reason step S 277  in  FIG. 30  is only shown with a broken line.  
         [0240]     In accordance with  FIG. 31  in a step S 281  the computer  8  selects a parcel  19 , to which the type “perfusion area” is temporarily assigned.  
         [0241]     In a step S 282  the computer  8  checks whether a neighboring parcel  19 , to which the type “perfusion area” is similarly (temporarily or finally) assigned, exists next to the selected parcel  19 . If this check is negative, in a step S 283  the computer assigns the type “perfusion area” finally to the selected parcel  19 .  
         [0242]     If the computer  8  finds a neighboring parcel  19 , in a step S 284  the computer  8  checks whether the sum of the sizes of the selected parcel  19  and the neighboring parcel  19  exceeds a maximum size. If this is the case, in a step S 285  the computer  8  assigns the type “perfusion area” finally to at least one of the two parcels  19 . Otherwise the computer  8  combines the selected parcel  19  and the neighboring parcel  19  in a step S 286  to form an overall parcel  19   c  and assigns the type “perfusion area” temporarily to this.  
         [0243]     In a step S 287  the computer  8  checks whether it has already finally assigned the type “perfusion area” to all the parcels  19 , to which the type “perfusion area” was initially temporarily assigned.  
         [0244]     The maximum size in step S 284  can be constant. Alternatively it is however possible for the computer  8 , before executing the method in  FIG. 31 , to determine internally related areas of the evaluation image A, in which it has assigned exclusively the type “perfusion area” to the parcels  19 . In this case the computer  8  can for example define the maximum size of the parcels  19  of the respective area as a function of the size of the respective area. In particular it can select the maximum size to be even smaller, the smaller the respective internally related area.  
         [0245]     It is also possible for the computer  8 , after assigning the type “vessel” to the parcels  19  (optionally including subsequent optimization of said parcels  19 , see also FIG.  28 ) to re-divide the remaining parcels  19 . In this case the computer  8  can select the parcel size of the remaining parcels  19  to be even smaller, the smaller the distance between the remaining parcels  19  and the parcels  19 , to which the type “vessel” is assigned.  
         [0246]     The procedures described to date are based totally on the definition of evaluation cores  19  and the use of characteristic variables C 1 , C 2 , which were determined based on the evaluation cores  19 . The assignment of the type “vessel” to the parcels  19  can in particular be even further optimized in a different manner. This is described in more detail below in conjunction with  FIG. 32 .  
         [0247]     In accordance with  FIG. 32  in a step S 291  the computer  8  carries out a vessel segmentation for each projection image B based on the pixel values of the respective projection image B—in other words without parceling or assignment of evaluation cores  19  and without evaluation of temporal profiles. In this manner it identifies vessels of the vascular system. Vessel segmentations are generally known. There is therefore no need to examine such procedures in more detail here.  
         [0248]     In a step S 292  the computer  8  determines the vascular system from the vessels identified in the projection images B. For example the vascular system can be determined by adding together the vessels identified in the individual projection images B.  
         [0249]     If necessary, in a step S 293  the computer  8  clears the vascular system determined in step S 292  of “outliers”. For example image areas identified originally as vessel can be deleted, if identification as a vessel only took place in a single or only in two projection images B.  
         [0250]     In a step S 294  the computer  8  takes into account the vascular system determined in step S 292  (optionally including step S 293 ) in the type assignment for the parcels  19 . For example the computer  8  can assign the type “vessel” beforehand to specific pixels  9  (even finally) and/or can shape the parcels  19  correspondingly.  
         [0251]     The inventive image evaluation method demonstrates a high degree of automation and a high processing speed. It is also very flexible, even in the context of visualizing the evaluation result and in the context of interactivity. Finally it is also possible to integrate the inventive image evaluation method as part of what is known as TIMI flow measurement. This avoids duplicated capturing of the projection images B and the x-ray load for the patient  3  associated therewith.  
         [0252]     The above description serves exclusively to explain the present invention. The scope of protection of the present invention should however only be defined by the accompanying claims.