Abstract: A method is disclosed for x-ray CT scanning with a dual-source system, in which two radiation bundles are each delimited by diaphragms such that these radiation bundles are free of mutual points of intersection at least in the examination object. An embodiment of the invention also relates to a dual source CT system, including a controller configured to control radiation-delimiting diaphragms, which delimit and align the radiation bundles such that these run free of mutual points of intersection at least in the examination object.

Abstract: A method is disclosed for x-ray CT scanning with a dual-source system, in which two radiation bundles are each delimited by diaphragms such that these radiation bundles are free of mutual points of intersection at least in the examination object. An embodiment of the invention also relates to a dual source CT system, including a controller configured to control radiation-delimiting diaphragms, which delimit and align the radiation bundles such that these run free of mutual points of intersection at least in the examination object.

Abstract: A method, a computation unit and a CT system are disclosed for reconstructing CT image data, which includes a plurality of image voxels, by way of a weighted filtered back-projection. To weight the projection data during the filtered back-projection, a weight function is used, which weights the projection data from central beams which strike a detector row relatively close to the edge of the row relatively less, and which weights the projection data of central beams, which strike a detector row more relatively centrally, relatively more.

Abstract: The method includes a weighted highpass filtering of the image data takes place here, with the weighting taking account, image element by image element, of an image noise on the respective image element in different directions such that increasing noise results in a stronger highpass effect. A noise-reducing smoothing of the image data takes place using the weighted highpass filtering.

Abstract: A method, a computation unit and a CT system are disclosed for reconstructing CT image data, which includes a plurality of image voxels, by way of a weighted filtered back-projection. To weight the projection data during the filtered back-projection, a weight function is used, which weights the projection data from central beams which strike a detector row relatively close to the edge of the row relatively less, and which weights the projection data of central beams, which strike a detector row more relatively centrally, relatively more.

Abstract: The method includes a weighted highpass filtering of the image data takes place here, with the weighting taking account, image element by image element, of an image noise on the respective image element in different directions such that increasing noise results in a stronger highpass effect. A noise-reducing smoothing of the image data takes place using the weighted highpass filtering.

Abstract: A method is disclosed for reconstructing CT image data. In at least one embodiment, the method includes providing measured CT projection data p based on the CT projection data p, reconstructing first CT image data fk=1, and on the basis of the first image data fk=1 iteratively generating k+1-th CT-image data according to the formula: fk+1=fk??(Q(Pfk?p)+?R(fk)) until the standard ?fk+1?fk?2 is ?n, with the reconstruction operator Q containing a noise weighting according to Q=B·Wstatist.·H. Aside from suppressing “cone” artifacts, the proposed method of at least one embodiment indicates a significant reduction in the image noise even after a few iterations.

Abstract: A method is disclosed for reconstructing CT image data. In at least one embodiment, the method includes provisioning CT projection data p. Secondly, it includes reconstruction of first image data fk=1 based on the CT projection data p. Thirdly, it includes iterative determination of k+1-th CT image data fk+1 on the basis of the first CT image data fk=1 as a function of: k-th CT image data fk, a reconstruction of differential projection data, the differential projection data being produced as the difference between reprojected CT image data fk and the CT projection data p, as well as a local contrast-dependent smoothing of the CT image data fk using a non-quadratic correction operator R(fk). Besides suppressing “cone” artifacts, the proposed method of at least one embodiment exhibits a significant reduction in image noise after just a few iterations.

Abstract: A method is disclosed for reconstructing CT image data. In at least one embodiment, the method includes providing measured CT projection data p based on the CT projection data p, reconstructing first CT image data fk=1, and on the basis of the first image data fk=1 iteratively generating k+1-th CT-image data according to the formula: fk+1=fk??(Q(Pfk?p)+?R(fk)) until the standard ?fk+1?fk?2 is ?n, with the reconstruction operator Q containing a noise weighting according to Q=B·Wstatist.·H. Aside from suppressing “cone” artifacts, the proposed method of at least one embodiment indicates a significant reduction in the image noise even after a few iterations.

Abstract: A method is disclosed for reconstructing CT image data. In at least one embodiment, the method includes provisioning CT projection data p. Secondly, it includes reconstruction of first image data fk=1 based on the CT projection data p. Thirdly, it includes iterative determination of k+1-th CT image data fk+1 on the basis of the first CT image data fk=1 as a function of: k-th CT image data fk, a reconstruction of differential projection data, the differential projection data being produced as the difference between reprojected CT image data fk and the CT projection data p, as well as a local contrast-dependent smoothing of the CT image data fk using a non-quadratic correction operator R(fk). Besides suppressing “cone” artifacts, the proposed method of at least one embodiment exhibits a significant reduction in image noise after just a few iterations.