General Electric Company

Bruno Kristiaan Bernard DeMan

Samit Kumar Basu

Jed Douglas Pack

Kai Zeng

Zhye Yin

Lin Fu

Thomas Matthew Benson

Jiang Hsieh

Jean-Baptiste Thibault

James Vradenburg Miller

Floribertus P M Heukensfeldt Jansen

Paulo Ricardo Dos Santos Mendonca

Kyle Morgan Champley

The present approaches relate to frequency-split iterative reconstruction approaches. In some embodiment, such approaches provide for the combination of the low frequency components of an analytical reconstruction (e.g., a filtered back projection) and the high frequency components of an iterative reconstruction. In certain embodiments, frequency-split iterative reconstruction is used for generating region of interest images.

Method for tomographic reconstruction

Embodiments of systems, methods and non-transitory computer readable media for imaging are presented. Preliminary image data corresponding to a first FOV of a subject at a first resolution is acquired using an imaging system including one or more radiation sources and at least one hybrid detector, specifically using at least one section of the hybrid detector having the first resolution. The target ROI is identified using the preliminary image data. Further, the subject is positioned to align the target ROI along a designated axis. Additionally, parameters associated with the sources, the hybrid detector and/or an imaging system gantry are configured for acquiring target image data at a second resolution greater than the first resolution using at least one section of the hybrid detector having the second resolution. Further, one or more images corresponding to at least the target ROI are reconstructed using the target and/or the preliminary image data.

Method and system for non-invasive imaging of a target region

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

Approaches are described for generating an initial reconstruction of CT data acquired using a wide-cone system. In one implementation, frequency data may be patched in to a first scan, such as an axial scan, from a second scan, such as a helical scan. In one embodiment, an initial reconstruction may be processed (such as via a non-linear operation) to correct frequency omissions and/or errors in the reconstruction. Corrected frequency information may be utilized to improve the reconstructed image.

Methods and systems are provided for correcting artifacts in iterative reconstruction processes. In certain embodiments, weighting schemes may be applied such that less than all of the available scan or projection data is utilized in the iterative reconstruction. In this manner, inconsistencies in the data undergoing reconstruction may be reduced.

Method and system for correcting artifacts in image reconstruction

Approaches are described for generating an initial reconstruction of CT data acquired using a wide-cone system. The initial reconstruction may be processed (such as via a non-linear operation) to correct frequency omissions and/or errors in the reconstruction. Corrected frequency information may then be added to the reconstruction to improve the reconstructed image.

In one embodiment, a first set of projection data is acquired using a first portion of a detector and a second set of projection data is acquired using a second portion of the detector. The second set of projection data is supplemented based upon the first set of projection data to generate a supplemented set of projection data. A volumetric image may be generated using the supplemented set of projection data.

Method and apparatus for computed tomography

A method for analytically reconstructing a multi-axial computed tomography (CT) dataset, acquired using one or more longitudinally-offset x-ray beams emitted from multiple x-ray sources is provided. The method comprises acquiring one or more CT axial projection datasets, wherein the CT axial projection datasets are acquired using less than a full scan of data. The method further comprises reconstructing the CT axial projection datasets to generate a reconstructed image volume. The reconstruction comprises back projecting one or more voxels comprising the multi-axial CT dataset, along one or more projection views, based upon a cone-angle weight determined for the voxels, wherein the cone-angle weight for the voxels is determined along a longitudinal direction.

Method for analytic reconstruction of cone-beam projection data for multi-source inverse geometry CT systems

A method for generating a multi-spectral image of an object is provided. The method comprises acquiring measurement data at a plurality of X-ray energy levels and defining a plurality of image voxels in one or more regions comprising the object. The method then comprises obtaining prior information associated with a plurality of image voxels comprising the object. The prior information is defined by a joint probability density function (PDF) between a plurality of basis components. The method further comprises reconstructing the measurement data to generate a multi-spectral reconstructed image of the object based on the prior information.

Method and system for generating a multi-spectral image of an object

A new method extends iterated coordinate descent ('ICD')&#x2014;an optimization method employed in some statistical reconstruction algorithms&#x2014;to handle material decomposition ('MD') for energy discriminating computed tomography (&#x201c;EDCT&#x201d;) acquisitions.

Method of iterative reconstruction for energy discriminating computed tomography systems

A method for iteratively reconstructing image data acquired by a computed tomography system is provided. The method comprises generating a calculated sinogram from an image estimate and generating an error sinogram based on the calculated sinogram and a measured sinogram. Then, one or more backprojections are performed, each based upon a reconstruction parameter. The reconstruction parameter impacts at least one of convergence speed and computational cost of each iterative step and corresponding reconstruction. A filtering step is performed prior to performing the one or more backprojections. Finally, the initial image is updated by adding corresponding results of the one or more backprojections to the image estimate to obtain the reconstructed image.

Method and system for iterative image reconstruction

A method for reconstructing image data from acquired tomographic projection data measurements is provided. The projection data measurements comprise one or more missing data measurements. The method comprises generating a coarse-resolution projection data set from the acquired projection data measurements and performing an iterative reconstruction on the coarse-resolution projection data set to generate a coarse-resolution reconstructed data set. Then, the method comprises reprojecting the coarse-resolution reconstructed data set to obtain one or more estimates for the one or more missing data measurements. The one or more estimated missing data measurements are then recombined with the acquired projection data measurements, to generate a recombined data set. Then, a direct reconstruction algorithm is applied to the recombined data set to generate the reconstructed image data.

System and method for reconstruction of cone beam tomographic projections with missing data

A reprojection/backprojection technique and apparatus for carrying out such a technique provides a hierarchical solution to speeding-up reprojection and backprojection of tomographic images. In the context of reprojection, a tomographic image is divided into a series of subimages. Each subimage is shifted to the origin, projected at a reduced number of views, and then up-sampled and shifted back (in the sinogram space). The resulting sinograms are then combined to provide a single sinogram. This process is applied one or more times recursively. In the context of backprojection, the above steps are transposed such that a sinogram is divided into a series of subsinograms. The subsinograms are then shifted and decimated by a given decimation factor. The decimated subsinograms are then backprojected onto hexagonal tiles whereupon the tiles are composited into a final image.

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Clifton Park, NY, United States of America

Bruno Kristiaan Bernard DeMan