Abstract: A method for performing object scan data correction in an X-ray imaging system having a photon-counting detector is provided. The method includes acquiring calibration scan data from a calibration scan performed using a plurality of slabs, performing function fitting based on the acquired calibration scan data to generate a plurality of fitting functions corresponding to the plurality of slabs, each corresponding fitting function representing a relationship between a counting measurement detected with respect to one slab of the plurality of slabs and a tube current applied to the X-ray tube, establishing a calibration table based on the acquired calibration scan data, acquiring object scan data from an object scan performed on an imaging object, performing data correction for the acquired object scan data, based on the established calibration table and the generated plurality of fitting functions and reconstructing an image of the imaging object based on the performed data correction.

Abstract: A method for performing detector response correction in an X-ray imaging system having a photon-counting detector is disclosed. The method includes obtaining calibration data stored in a calibration data storage, which is generated during a calibration procedure performed with the X-ray imaging system at a first time. The method also includes acquiring air scan data generated through an air scan performed with the X-ray imaging system at a second time after the first time. The method further includes performing, with the X-ray imaging system, an object scan on an imaging object at a third time to generate object scan data. The third time is after the second time. The method further includes performing, using the generated object scan data, detector response correction based on the acquired air scan data and the obtained calibration data, and reconstructing, based on the performed detector response correction, an image of the imaging object.

Abstract: A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a detector section, an aggregation section, and a storage section. The detection section includes a plurality of detector elements configured to convert radiation into electric signals. The aggregation section aggregates imaging data carried by the electric signals from the detector section. The storage section is arranged in a manner corresponding to the detector elements regarding an output from the detector section and an input to the aggregation section. The storage section includes a predetermined number of non-volatile memories configured to store the imaging data from the corresponding detector elements.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a plurality of sets of a detector section, a storage section, and an aggregation section. The detector section includes a plurality of detector elements each being configured to convert radiation into electric signals. The aggregation section is configured to aggregate imaging data carried by the electronic signals from the detector section. The storage section is connected with an output of the detector section and an input of the aggregation section. The storage section comprises a predetermined number of non-volatile memories to store the imaging data from the corresponding detector elements.

Abstract: A method, system, and computer readable medium to compensate for consecutive missing views in Computed Tomography (CT) reconstruction. By utilizing at least one complementary ray from a previous or subsequent view, the missing view(s) can be filled in. When plural complementary rays exist, a linear or non-linear combination of rays can be used to fill in the missing views, and the weights used in the combination may be smoothed to prevent over-emphasis of the replacement views.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a plurality of sets of a detector section, a storage section, and an aggregation section. The detector section includes a plurality of detector elements each being configured to convert radiation into electric signals. The aggregation section is configured to aggregate imaging data carried by the electronic signals from the detector section. The storage section is connected with an output of the detector section and an input of the aggregation section. The storage section comprises a predetermined number of non-volatile memories to store the imaging data from the corresponding detector elements.

Abstract: A computer-tomography (CT) imaging system, comprising an imaging data acquisition system. The imaging data acquisition system includes a detector section, an aggregation section, and a storage section. The detection section includes a plurality of detector elements configured to convert radiation into electric signals. The aggregation section aggregates imaging data carried by the electric signals from the detector section. The storage section is arranged in a manner corresponding to the detector elements regarding an output from the detector section and an input to the aggregation section. The storage section includes a predetermined number of non-volatile memories configured to store the imaging data from the corresponding detector elements.

Abstract: A method of computing statistical weights for a computed tomography (CT) iterative reconstruction process is provided. The method includes obtaining detector count data from a CT scan of an object; calculating variance data based on the count data and an electronic noise variance; transforming the calculated variance data to obtain statistical weight data; and performing the CT iterative reconstruction process using the statistical weight data and raw projection data to obtain a reconstructed CT image.

Abstract: Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, differentiating the reference image; generating synthetic data by forward projection of the differentiated reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and optionally applying a scaling factor. Ultimately, the cone beam artifact is substantially reduced by generating a corrected image using the reference image and the correction image that has been optimally scaled based upon the adaptively determined scaling factor value.

Abstract: Cone beam artifacts arise in circular CT reconstruction. The cone beam artifacts are substantially removed by reconstructing a reference image from measured data at circular source trajectory, differentiating the reference image; generating synthetic data by forward projection of the differentiated reference image along a pre-determined source trajectory, which supplements the circular source trajectory to a theoretically complete trajectory, reconstructing a correction image from the synthetic data and optionally applying a scaling factor. Ultimately, the cone beam artifact is substantially reduced by generating a corrected image using the reference image and the correction image that has been optimally scaled based upon the adaptively determined scaling factor value.

Abstract: A method of computing statistical weights for a computed tomography (CT) iterative reconstruction process is provided. The method includes obtaining detector count data from a CT scan of an object; calculating variance data based on the count data and an electronic noise variance; transforming the calculated variance data to obtain statistical weight data; and performing the CT iterative reconstruction process using the statistical weight data and raw projection data to obtain a reconstructed CT image.

Abstract: Streak artifacts arise in helical CT reconstruction with cone beam weighting (CBW) with helical pitch ratio between 0.5 and 1.0 in a prevalent 2PI mode. The sreak artifacts are substantially removed by applying upsampling to the measured data in the segment direction before weighting. Furthermore, by making the upsampling adaptive to the view Z-position, an amount of extra processing is greatly reduced to near 1%.

Abstract: A method of computed-tomography and a computed-tomography apparatus where the portion of the field of view of a subject were full scan data is available is reconstructed using a full-scan algorithm. In the areas where full scan data is not available, half-scanning is used. Data is also extrapolated from the full scan data. The extrapolated data overlaps a portion of the half-scanning data. The extrapolated data and the overlapped portion of the half-scanning data are feathered. The image is reconstructed using the full-scan, half-scan and feathered data. Corner regions in an image are exposed and reconstructed to produce more uniform z-coverage of the reconstruction field of view.

Abstract: Streak artifacts arise in helical CT reconstruction with cone beam weighting (CBW) with helical pitch ratio between 0.5 and 1.0 in a prevalent 2PI mode. The sreak artifacts are substantially removed by applying upsampling to the measured data in the segment direction before weighting. Furthermore, by making the upsampling adaptive to the view Z-position, an amount of extra processing is greatly reduced to near 1%.

Abstract: A method of computed-tomography and a computed-tomography apparatus where the portion of the field of view of a subject were full scan data is available is reconstructed using a full-scan algorithm. In the areas where full scan data is not available, half-scanning is used. Data is also extrapolated from the full scan data. The extrapolated data overlaps a portion of the half-scanning data. The extrapolated data and the overlapped portion of the half-scanning data are feathered. The image is reconstructed using the full-scan, half-scan and feathered data. Corner regions in an image are exposed and reconstructed to produce more uniform z-coverage of the reconstruction field of view.