Siemens Aktiengesellschaft

University of Utah Research Foundation

Siemens Healthcare Gmbh

Siemens Healthineers Ag

Harald Schöndube

Karl Stierstorfer

Rainer Raupach

Herbert Bruder

Frédéric Noo

Thomas Flohr

Thomas Allmendinger

Bernhard Schmidt

Steffen Kappler

Holger Kunze

Christian Hofmann

Martin Petersilka

Ernst Klotz

Johan Sunnegaardh

Matthias Baer

Katharina Schmitt

Nora Steinich

A multi-spectral CT imaging method, preferably a CT imaging method, is described. Spectrally resolved projection scan data is acquired from a region to be imaged of an examination object. The data is assigned to a plurality of pre-determined different partial spectra. Spectrally resolved image data is reconstructed with a plurality of attenuation values for each image point of the region to be imaged. The attenuation values are each assigned to one of the pre-determined different partial spectra. Furthermore, an extremal attenuation value is determined for each image point on the basis of the plurality of attenuation values. A representative image data set is determined such that the determined extremal attenuation value is assigned to each image point.

Multi-spectral CT imaging

A method for reconstructing image data during CT imaging is described. In the method, a plurality of independent data records of projection measured data are captured. A combined data record is then determined based on the captured data records. In addition, morphological information is determined based on the combined data record. A target data record is also determined based on the captured independent data records. A target image data record is reconstructed based on the target data record and the determined morphological information. An image data determination facility and a computed tomography system are also described.

Structure-compliant noise reduction during multispectral computed tomography imaging

A method and calculation unit are disclosed for reconstructing an image from measured projection data. In an embodiment, the method includes reconstructing an initial image from the projection data via back-projection; dividing the initial image into contiguous blocks of voxels; defining groups of voxels, wherein each group is defined by selecting one voxel from each block, each voxel having the same position relative to the other voxels within its block; updating the voxel values for all voxels within one group by minimizing a function consisting of a weighted sum of a first term representing the geometry of the data acquisition process, and a second regularization term; and repeating the updating of the voxel values for all voxels within one group until all groups have been updated at least once.

Iterative reconstruction of image data in CT

A method is disclosed for the reconstruction of image data of an object under examination from measured data, wherein the measured data has been acquired during a relative rotational movement between a radiation source of a computed tomography system and the object under examination. A delimited area between the radiation source and a detector represents a field of view, in respect of which measured data can be acquired. During the measured data acquisition, parts of the object under examination were located at least partly outside of the field of view. A reconstruction is carried out of first image data from the measured data. The first image data is modified via a threshold value comparison, and the modified data is processed with a morphological filter, and projection data is calculated. The measured data is modified using the projection data, and, second image data is reconstructed.

CT image reconstruction in the extended field of view

A method is disclosed for the reconstruction of image data of an examination object from measurement data. First and second image data are reconstructed from the measurement data with a first and second respective image characteristic, with an enhanced signal-to-noise ratio of the second image characteristic relative to the first image characteristic. Enhanced image data is calculated using an iterative algorithm using the first and the second image data. In the case of the iterative algorithm, a low pass is applied to a difference between the first image data and the image data of an iteration cycle, and a high pass to a difference between the second image data and the image data of the iteration cycle.

Method for iterative image reconstruction for bi-modal CT data

A method is disclosed for the reconstruction of image data of a moving object to be examined from measurement data, wherein the measurement data has previously been established in a relative rotational movement between a radiation source of a computed tomography system and the object to be examined. In at least one embodiment, first image data is reconstructed from an incomplete measurement data record by way of an iterative algorithm, wherein in the iterative reconstruction a dimension is used which contains probability information relating to pixel values of the image data to be reconstructed.

Increased temporal resolution in the case of CT images by means of iterative view reconstruction with limiting conditions

A method and computer system are disclosed with software for artifact reduction in CT image datasets for spiral scans from a CT system with an inclined gantry. In at least one embodiment, for each of at least two positions of the center of rotation, which is moved during the spiral scan, the removal of annular artifacts is carried out around these positions, during which the position concerned is used as the center point of the annular artifacts to be removed.

Method and computer system for the reduction of artifacts in reconstructed CT image datasets

A method is disclosed for the reconstruction of picture data of a moving object under examination from measurement, with the measurement data having been recorded beforehand for a relative rotational movement between a radiation source of a computed tomography system and the object under examination. In at least one embodiment, first picture data is computed from a complete measurement dataset of the measurement data for a picture reconstruction, and second picture data is computed from an incomplete measurement dataset for a computed tomography picture reconstruction. The first picture data and the second picture data are combined into third picture data, with the combination being computed using location-dependent movement information of the object under examination.

Temporal resolution in cardio CT

At least one embodiment of the invention relates to a method for the reconstruction of image data of an examined object from measuring data, wherein the measuring data were detected by a detector within and outside of its Tam-Danielsson window during a relative spiral movement between a radiation source of a computer tomography system and the examined object. As a result of the spiral movement, the measuring data outside of the Tam-Danielsson contain interruptions. A mathematically precise first reconstruction of first image data is realized in at least one embodiment based on the measuring data by using only measuring data from the Tam-Danielsson window. A mathematically precise second reconstruction is furthermore realized of second image data from the measuring data in at least one embodiment, using at least among other things measuring data from outside of the Tam-Danielsson window, wherein the interruption of the measuring data is compensated for by using existing measuring data and/or the first image data and/or other image data obtained from existing measuring data. Finally, the first image data and the second image data are combined.

Precise image reconstruction of spiral CT images at optional pitch values

A method is disclosed for generating computed tomography image data of a volume of interest from X-ray CT data sets generated by a computed tomography system during scanning of an examination subject on a helical path rotating around a longitudinal system axis in the infeed direction. In at least one embodiment, at least two volume-based reconstructions of the volume of interest are performed by way of differential back-projection over surfaces constituted by different groups of M-lines, followed in each case by an inverse Hilbert transformation. The method further includes combining different reconstructed volumes to form a final reconstruction of the volume of interest.

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Erlangen, Germany

Harald Schöndube