Abstract: Approaches for reconstructing multi-phase images are disclosed. In certain embodiments, calibrated X-ray projection data acquired over at least a partial axial or low-pitch helical rotation is accessed and used to reconstruct one or more initial images. A frequency transform is performed on the images to generate respective frequency domain representations. Elements of the frequency domain representations are weighted based on at least the difference between the phase associated with the elements and a specified phase of interest. The weighted frequency domain representations are combined to generate a frequency domain representation at the phase of interest, which can be used to generate an image at the phase of interest.

Abstract: Algorithms are disclosed that recombine acquired data so as to generate a substantially uniform and complete set of frequency data where frequency data might otherwise be incomplete. This process, or its equivalent, may be accomplished in a computationally efficient manner using filtering steps in one or both of the reconstruction space and/or the post-processing space.

Abstract: 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.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: A system and method for CT sinogram extrapolation are provided. The method comprises receiving a CT projection for extrapolation. The method also comprises selecting a target patch comprising at least one pixel of a row to be extrapolated. The method further comprises generating a correlation profile between the target patch and one or more source patches, wherein the source patches comprise measured pixels in the CT projection in one or more rows adjacent to the target patch. The projection data is generated for at least one pixel of the target patch based on the correlation profile and the measured pixels of at least one of the source patches.

Abstract: Approaches are described for processing half-scan or full-scan cone beam image data using one or more half-ramp filtering operations. In one embodiment, the half-ramp filtering operations allow extraction and use of missing frequency data so as to generate a reconstructed image that is relatively complete in terms of frequency data and which has suitable temporal resolution. In addition, in certain embodiments, the reconstructed image may have uniform frequency weighting.

Abstract: 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.

Abstract: Algorithms are disclosed that recombine acquired data so as to generate a substantially uniform and complete set of frequency data where frequency data might otherwise be incomplete. This process, or its equivalent, may be accomplished in a computationally efficient manner using filtering steps in one or both of the reconstruction space and/or the post-processing space.

Abstract: 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.

Abstract: 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.

Abstract: A method of performing a computed tomographic image reconstruction is provided. The method provides for performing a short scan of an imaging object to acquire a short scan data, performing a plurality of image reconstructions based on the short scan data wherein the plurality of image reconstructions result in a corresponding plurality of image volumes wherein the image reconstructions use different view weighting functions, filtering the plurality of image volumes such that when the volumes are added together, the frequency domain data is substantially uniformly weighted. Further, the method provides for combining the plurality of image volumes together to produce a final image volume.

Abstract: 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.

Abstract: 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.

Abstract: A system and method include acquisition of a set of image data corresponding to a time period of data acquisition, the set of image data corresponding to a plurality of voxels, wherein each of the plurality of voxels corresponds to a distinct acquisition time within the time period of data acquisition. The system and method further include the modeling of the plurality of voxels as a function of time based on a plurality of kinetic parameters associated therewith and reconstruction of an image from the set of image data based on the modeled plurality of voxels.

Abstract: An improved iterative reconstruction method to reconstruct a first image includes generating an imaging beam, receiving said imaging beam on a detector array, generating projection data based on said imaging beams received by said detector array, providing said projection data to an image reconstructor, enlarging one of a plurality of voxels and a plurality of detectors of the provided projection data, reconstructing portions of the first image with the plurality of enlarged voxels or detectors, and iteratively reconstructing the portions of the first image to create a reconstructed image.

Abstract: A method of performing a computed tomographic image reconstruction is provided. The method provides for performing a short scan of an imaging object to acquire a short scan data, performing a plurality of image reconstructions based on the short scan data wherein the plurality of image reconstructions result in a corresponding plurality of image volumes wherein the image reconstructions use different view weighting functions, filtering the plurality of image volumes such that when the volumes are added together, the frequency domain data is substantially uniformly weighted. Further, the method provides for combining the plurality of image volumes together to produce a final image volume.