General Electric Company

Thomas Matthew Benson

David Allen Langan

Jiang Hsieh

Bruno Kristiaan Bernard De Man

Xiaoye Wu

Jed Douglas Pack

Dan Xu

Lin Fu

Bruno Kristiaan Bernard DeMan

John Eric Tkaczyk

Jean-Baptiste Thibault

Robert F Senzig

Jiahua Fan

James Walter LeBlanc

Colin Richard Wilson

Naveen Stephan Chandra

Zhye Yin

Hao Lai

Kai Zeng

Roy Arnulf Helge Nilsen

Michael B Black

Bryan Joseph

Approaches for acquiring CT image data corresponding to a full scan, but at a reduced dose are disclosed. In one implementation, X-ray tube current modulation is employed to reduce the effective dose. In other implementations, acquisition of sparse views, z-collimation, and two-rotation acquisition protocols may be employed to achieve a reduced dose relative to a full-scan acquisition protocol.

Method and system for reduced dose X-ray imaging

Approaches are described for addressing artifacts associated with iterative reconstruction of image data acquired using a cone-beam CT system. Such approaches include, but are not limited to, the use of asymmetric regularization during iterative reconstruction, the modulation of regularization strength for certain voxels, the modification of statistical weights, and/or the generation and use of synthesized data.

Method and system for reconstruction of tomographic images

The present disclosure relates to iterative reconstruction of images. In certain embodiments, a deconvolution filter is used to approximate the inversion of a Hessian matrix associated with the reconstruction. In one such embodiment, the desired image is not reconstructed directly in the iterative process. Instead, an image is reconstructed that yields the desired image when filtered by the deconvolution filter.

Tomographic iterative reconstruction

Methods are provided for iteratively reconstructing an image signal to generate a reconstructed image signal. In one embodiment, sub-iterations of each iteration are performed on pixel or voxel subsets. The subsets may be composed of neighboring or spatially separated pixel or voxels and may extend in the z-direction. In one embodiment, an update step of the iterative reconstruction involves the direct inversion of an approximation of a Hessian matrix associated with the respective subsets. In further embodiments, non-negativity or other limitations or constraints on update values may be enforced.

System and method for iterative image reconstruction

Methods and apparatus for statistical iterative reconstruction are provided. One method includes pre-processing acquired raw measurement data to modify the raw data measurement data and determining a change in a variance of the raw measurement data resulting from the modification to the raw measurement data during pre-processing. The method also includes reconstructing an image using the modified raw measurement data resulting from the pre-processing and the determined change in variance.

Method and apparatus for statistical iterative reconstruction

A method and apparatus include acquisition of a view dataset based on x-rays received by a detector corresponding to a energy level, reconstruction of an initial image using the view dataset, the initial image comprising a plurality of metal voxels at respective metal voxel locations, and generation of a metal mask corresponding to the plurality of metal voxels within the initial image. The method and apparatus also include forward projection of the metal mask onto the view dataset to identify metal dexels in the view dataset, performance of a weighted interpolation based on the identified metal dexels to generate a completed view dataset, reconstruction of a final image using the completed view dataset, the final image comprising a plurality of image voxels corresponding to the metal voxel locations, and replacement of a portion of the plurality of image voxels corresponding to the metal voxel locations with smoothed metal values.

Method and apparatus for reduction of metal artifacts in CT images

Methods and systems for iterative image reconstruction. The method includes selecting a particular X and Y location corresponding to one or more selected voxels in an image volume. Further, one or more Z locations are iteratively selected for the particular X and Y location. One or more partial sums corresponding to channel dependent portions of a separable system model associated with the particular X and Y location are pre-computed. Additionally, row-dependent portions of the separable system model are computed independent of the pre-computing of the one or more partial sums. The partial sums and the row-dependent portions of the separable system model are combined to compute corresponding updates for the selected voxels. The one or more partial sums are then updated to be consistent with the computed updates. Additionally, the computed updates are applied to the selected voxels independent of updating the one or more partial sums.

Methods and systems for fast iterative reconstruction using separable system models

The present disclosure relates to the generation of dual-energy X-ray data using a data sampling rate comparable to the rate utilized for single-energy imaging. In accordance with the present technique a reduced kVp switching rate is employed compared to conventional dual-energy imaging. Full angular resolution is achieved in the generated images.

Dual-energy imaging at reduced sample rates

A CT system includes a gantry, an x-ray source, a generator configured to energize the x-ray source to a first kVp and to a second kVp, a detector, and a controller. The controller is configured energize the x-ray source to the first kVp for a first time period, subsequently energize the x-ray source to the second kVp for a second time period, integrate data for a first integration period that includes a portion of a steady-state period of the x-ray source at the first kVp, integrate data for a second integration period that includes a portion of a steady-state period of the x-ray source at the second kVp, compare a signal-to-noise ratio (SNR) during the first integration period (SNR) and the second integration period (SNR), adjust an operating parameter of the CT system to optimize an SNRwith SNR, and generate an image using the integrated data.

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Clifton Park, NY, United States of America

Thomas Matthew Benson