1. A method for automatically differentiating types of kidney stones via computed tomography, the method comprising: recording two computed tomography 3D-raw-data sets of an object area including kidney stones, each of the two computed tomography 3D-raw-data sets being associated with a different spectral distribution of an X-radiation; reconstructing, from the two 3D-raw-data sets, two 3D-image data records of the object area that include at least two X-ray attenuation values of voxels of the object area; determining if a mean value of the at least two X-ray attenuation values of voxels associated with one slice of the object area is above a threshold value that is characteristic of kidney stones; and if the determining step determines the mean value is above the threshold value, calculating from the two 3D-image data records, for each voxel of the at least one slice of the object are, a ratio r based on at least one of the at least two X-ray attenuation values of the voxels and from two averaged X-ray attenuation values that are obtained by averaging the at least two X-ray attenuation values of voxels inside a prescribed volume around the voxel in the respective 3D-image data record, and prescribed X-ray attenuation values o 1 , o 2 of pure urine: r = x 1 - o 1 x 2 - o 2 each of x 1 and x 2 being at least one of the at least two X-ray attenuation values and averaged X-ray attenuation values, the respective voxel being assigned to at least one type of kidney stones as a function of the magnitude of r, wherein for each voxel whose mean value from the at least two assigned X-ray attenuation values of the two 3D-image data records lies above the threshold value that is characteristic of kidney stones, firstly a three-dimensional volume area around the voxel is defined.