[ 0043 ] fig1 illustrates schematically a computed tomography device for a fan beam method which operates in accordance with an embodiment of the present invention . in this device , x - ray tubes 1 and beam receivers 2 ( detectors ) rotate jointly about a centre of rotation , which is also the centre of the circular measuring field 5 , and at which the patient 3 to be examined is located on a patient couch 4 . in order to be able to examine different parallel planes of the patient 3 , the patient couch can be displaced along the longitudinal axis of the body . as can be seen from the drawing , transverse slices emerge during ct recordings , that is to say depictions of layers of the body which are oriented substantially at right angles to the axis of the body . this layer representation method represents the distribution of the attenuation value μ z ( x , y ) itself ( z is the position on the longitudinal axis of the body ). computed tomography ( referred to as ct below ) needs projections at very many angles α . in order to produce a layer recording , the radiation cone emitted by the x - ray tube 1 is masked out in such a way that a flat beam fan is produced , which forms one - dimensional central projections of the transilluminated layer . for the purpose of exact reconstruction of the distribution of the attenuation values μ z ( x , y ), this beam fan must be at right angles to the axis of rotation and , in addition , must be spread to such an extent that , from each projection direction a , it completely covers the envisaged layer of the measured object . this beam fan passing through the object is intercepted by detectors which are arranged linearly on a circular segment . in the case of commercially available devices , these are up to 1000 detectors . the individual detector reacts to the incident beams with electrical signals whose amplitude is proportional to the intensity of the beams . each individual detector signal belonging to a projection a is in each case picked up by measuring electronics 7 and forwarded to a computer 8 . using the computer 8 , the measured data can then be processed in a suitable way and initially visualized on a monitor 6 in the form of a sinugram ( in which the projection α is plotted as a function of the measured values from the corresponding channel β ) in what are known as gordon units , but finally in the form of a natural x - ray image in hounsfield units . it is an object of an embodiment of the present invention , by way of a suitable method which is ultimately to be implemented and carried out in the computer 8 , to construct in a simple and rapid way a ct image in which the different anatomical tissue types are represented with different image characteristics ( image sharpness and image noise ) and thus an organ - specifically optimized ct image is obtained . an embodiment of the present invention makes use of the fact that the ct values ( hu values ) occupy different regions in the hounsfield scale , depending on the organ . the hounsfield scale is illustrated in fig2 . the ct values of the individual organs occupy specific regions , largely irrespective of the x - ray spectrum used . for example , lung tissue and fat , because of their low density and the low attenuation induced by this , exhibit negative ct values in the range from − 950 to − 550 and − 100 to − 80 , respectively . most other tissues lie in the positive range ( kidneys : 20 - 40 , heart : 40 - 100 , blood : 50 - 60 , liver : 50 - 70 ). bone tissue , because of the high order number of calcium , and therefore the higher attenuation , has ct values up to 2000 hu . for conventional ct devices , 4096 (= 2 12 ) different gray values are available to represent the entire hounsfield scale . however , only a maximum of 60 to 80 gray steps can be distinguished by the observer . for this reason , during ct imaging — as already mentioned — windowing is performed , in which the entire gray value scale is allocated to an hu interval of interest . in fig3 this is illustrated by way of example for the region of compact bone substance . the user defines the window interactively by way of its centre ( window position c ) and width ( window width w ), for example by way of a mouse or rotary knob . in the example of fig3 the centre is at c = 2000 , the window width is w = 400 . in this case , 10 gray steps between white and black are allocated to the window . an inventive aspect of an embodiment of the present applicatrion resides in optimizing a ct image in an organ - specific manner on the basis of the ct values in the hu scale and by way of appropriate windowing . according to an embodiment of the invention , it is proposed firstly to represent the ct values ( hu values ) of the image of a recorded layer in a histogram . a corresponding histogram lists the frequency of the hu values occurring in a ct image as a function of the hu values themselves . because of the practical restriction of the integer ct and hu values to a closed value range , the representation of the image data in hu values is analogous to a representation of image data in gray values . therefore , a histogram built up from hu values is frequently also referred to as a gray value histogram in the literature . a histogram which is exemplary of the thorax region is illustrated in fig4 . individual hu regions of the histogram ( a , b , c , d , e , f ) correlate with specific organ structures or , expressed in other words : segment sections ( a , b , c , d , e , f ) of the hu distribution curve 9 are allocated uniquely to an organ or a tissue structure . in the case of the histogram of fig4 the hu region a , for example , correlates with lung tissue , the region b with fat tissue , c with embedded water , d with blood ( e . g . aorta ), e with liver tissue and f with heart tissue . in order to be able to perform organ - specific setting of the image characteristics ( image noise and image sharpness ), the respective organ - and tissue - specific hu regions are defined in the histogram . then , in the simplest case of the embodiment according to the invention , each organ - specific curve section hu ( x , y ) is assigned its own transfer function λ ( hu ( x , y )). in this case , hu ( x , y ) signifies the hu value at the coordinates ( x , y ) of the ct image whose total frequency is plotted at the corresponding hu value in the histogram . the transfer function λ ( hu ( x , y )) changes the respective hu value hu ( x , y ) in such a way that anatomically associated , that is organ - or tissue - specific , image regions are delimited optically from other image regions . λ can therefore be understood as a filter function with which all the image pixels i ( x , y ) of the respective hu region are filtered . mathematically , this may be represented as follows : in the simplest case , λ is a constant function which has a specific value for each organ - specific hu region . for the image representation , this indicates that the tissue - specific hu value regions to be emphasized are each assigned a specific gray value in the image . this specific value has to be defined by the user , taking account of the desired image characteristic of the corresponding organ . in the ct image , this leads to the pixel ranges which belong to corresponding organ - specific gray value zones being filtered or manipulated differently . [ 0055 ] fig5 represents in simplified form a ct image 10 which has been reconstructed from the raw data and in which the organ - specific gray value zones which belong to the histogram sections from fig4 are circled . as can be seen from the hu region a , the gray value zones do not necessarily have to cohere . in the example of fig4 and 5 , the organ - specific regions a , b , c , d , e , f are filtered on an hu basis . i ( λ 1 )= λ 1 · i ( hu ( x , y )), huε a i ( λ 2 )= λ 2 · i ( hu ( x , y )), huε b i ( λ 6 )= λ 6 · i ( hu ( x , y )), huε f according to an embodiment of the invention , each transfer function a is selected in such a way that the corresponding organ or tissue i appears with the desired optimum image characteristic . following image filtering in accordance with the above procedure , in a further step of an embodiment of the invention , the different hu regions are represented with different windowing . in this case , the average hu value of the structure i of interest is chosen as the centre of the window . the window width depends on the attenuation differences of the respective structure : for the representation of very small attenuation differences , such as in the case of brain tissue , a narrow window is chosen , in the case of large attenuation differences , such as in the case of lung tissue , a wide window is chosen . the advantage of the method according to an embodiment of the invention can be seen clearly by using fig5 : according to a known method , with regard to the image characteristic , a large number of ct images 10 have to be produced , selected by hand and then , in the selected ct images , the appropriate organ structures have to be segmented and inserted into the final image . in an embodiment of the present invention , the automatically created histogram of the image data provides the relationship between the organ structure i and the associated image elements ( pixels : x , y ). it is merely necessary for the respective transfer function λ i to be predefined by the user . under certain circumstances , it is desirable to create the histogram only after previously executed manipulation of the reconstructed ct image in the form of image filtering . for example , following smoothing of the ct image , it is possible to represent vessels in the lung parenchyma with the same sharpness as the lung parenchyma itself ( a parenchyma is the assembly of cells in an organ which determines its function ). if the ct image of a lung recording is sufficiently highly smoothed , then the entire lung , including its vessel structures ( not part of the lungs ) can be represented in a coherent interval of the histogram . an initial image smoothed in this way will be designated i smooth in the following text . in exactly the same way , however , it may be that , in order to create the histogram , a high - contrast initial image i sharp is required in order , for example , to represent extra - organic tissue with just as high a sharpness . the respective hu - based image filtering may be represented as follows in these two cases : i ( λ i )= λ i · i smooth ( hu ( x , y )), huε i i ( λ i )= λ i · i sharp ( hu ( x , y )), huε i where i here stands symbolically for the hu value region of the organ structure i . in a further embodiment of the invention , the transfer function x , with which the organ - specific histogram regions are filtered , is not discrete but — as illustrated for example in fig6 — has a continuous course over the entire histogram region . the course is defined by the user for each layer of the body to be measured . the magnitude of x moves in an interval between 0 and 1 . hu - based continuous image filtering of this type may be described mathematically as a mixture of two extreme initial ct images i smooth and i sharp : i ( λ ( hu ( x , y ))= λ ( hu ( x , y ))· i sharp ( x , y )+( 1 − λ ( hu ( x , y ))) · i smooth ( x , y ) i ( λ ( hu ( x , y ))= λ ( hu ( x , y ))· i smooth ( x , y )+( 1 − λ ( hu ( x , y ))) · i sharp ( x , y ) the mixture of the two initial images is carried out in this way locally or pixel by pixel by way of the hu - value - dependent transfer function λ ( hu ( x , y )). the production of initial ct images i sharp and i smooth , that is to say of ct images with an extremely sharp or extremely smooth image characteristic , may be implemented in different ways . as early as during the reconstruction of the image from the ct raw data — for example by way of fourier transformation — it is possible to influence the image characteristic before the back - transformation by way of the selection of an appropriate convolution core ( or its fourier transform ). a sharp , edge - emphasizing convolution core supplies a high - contrast initial image i sharp with correspondingly high image noise , while a more gently smoothing convolution core reduces the local resolution but produces a low - noise image i smooth . another possibility is to set the image characteristic caused by a ct convolution core retrospectively from the already reconstructed initial ct image by way of a two - dimensional separable image filter . it is even simpler to replace a two - dimensional separable image filter by two one - dimensional image filters . as fig7 illustrates , in the case of a two - dimensional separable image filter with a 1 - pixel width , a total of 9 pixels are involved in order to filter one pixel . if the two - dimensional separable image filter according to fig7 is replaced by 2 one - dimensional image filters each having a 1 - pixel width , then only three pixels , a total therefore of six pixels , are involved in the filtering for each pixel in each dimension . ultimately , retrospective manipulation of the ct image with 2 one - dimensional image filters firstly signifies a considerable reduction in the computing time . secondly , filtering of the reconstructed image makes it unnecessary to store the raw data . the method and the technique of setting the image characteristic brought about by a ct convolution core by way of a two - dimensional separable image filters or by way of two one - dimensional image filter is presented extensively in t . flohr , s . schaller , a . stadtler et al ., “ fast image filters as an alternative to reconstruction kernels in computed tomography ”, proceedings of spie , vol . 4322 ( 2001 ), pp 924 - 933 , the entire contents of which are hereby incorporated herein by reference . the invention being thus described , it will be obvious that the same may be varied in many ways . such variations are not to be regarded as a departure from the spirit and scope of the invention , and all such modifications as would be obvious to one skilled in the art are intended to be included within the scope of the following claims .