Abstract: A method for performing truncation artifact correction includes acquiring a projection dataset of a patient, the projection dataset including measured data and truncated data, generating an initial estimate of a boundary between the measured data and the truncated data, using the measured data to revise the initial estimate of the boundary, estimating the truncated data using the revised estimate of the boundary, and using the measured data and the estimated truncated data to generate an image of the patient.

Abstract: An imaging system includes a rotatable gantry for receiving an object to be scanned, a generator configured to energize an x-ray source to generate x-rays, a detector positioned to receive the x-rays that pass through the object, and a computer. The computer is programmed to obtain knowledge of a metal within the object, scan the object using system scanning parameters, reconstruct an image of the object using a reconstruction algorithm, and automatically select at least one of the system scanning parameters and the reconstruction algorithm based on the obtained knowledge.

Abstract: A method is provided. The method includes acquiring a first dataset at a first energy spectrum and a second dataset at a second energy spectrum. The method also includes extracting a metal artifact correction signal using the first dataset and the second dataset or using a first reconstructed image and a second reconstructed image generated respectively from the first and the second datasets. The method further includes performing metal artifact correction on the first reconstructed image using the metal artifact correction signal to generate a first corrected image.

Abstract: Systems and methods for iterative multi-material correction are provided. A system includes an imager that acquires projection data of an object. A reconstructor reconstructs the acquired projection data into a reconstructed image, utilizes the reconstructed image to perform a multi-material correction on the acquired projection data to generate a multi-material corrected reconstructed image, and utilizes the multi-material corrected reconstructed image to perform one or more iterations of the multi-material correction on the projection data to generate an iteratively corrected multi-material corrected 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 method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.

Abstract: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer programmed to acquire a plurality of helical projection datasets of the object, reconstruct a first image using the acquired plurality of helical projection datasets and using a first reconstruction algorithm, reconstruct a second image using the acquired plurality of helical projection datasets and using a second reconstruction algorithm that is different from the first reconstruction algorithm, extract frequency components from each of the first and second images, sum the frequency components from each of the first and second images, and inverse transform the sum of the frequency components to generate a final image.

Abstract: A method is provided. The method includes acquiring a first dataset at a first energy spectrum and a second dataset at a second energy spectrum. The method also includes extracting a metal artifact correction signal using the first dataset and the second dataset or using a first reconstructed image and a second reconstructed image generated respectively from the first and the second datasets. The method further includes performing metal artifact correction on the first reconstructed image using the metal artifact correction signal to generate a first corrected image.

Abstract: A method is provided. The method includes acquiring a first dataset at a first energy spectrum and a second dataset at a second energy spectrum. The method also includes extracting a metal artifact correction signal using the first dataset and the second dataset or using a first reconstructed image and a second reconstructed image generated respectively from the first and the second datasets. The method further includes performing metal artifact correction on the first reconstructed image using the metal artifact correction signal to generate a first corrected image.

Abstract: An imaging system includes a rotatable gantry for receiving an object to be scanned, a generator configured to energize an x-ray source to generate x-rays, a detector positioned to receive the x-rays that pass through the object, and a computer. The computer is programmed to obtain knowledge of a metal within the object, scan the object using system scanning parameters, reconstruct an image of the object using a reconstruction algorithm, and automatically select at least one of the system scanning parameters and the reconstruction algorithm based on the obtained knowledge.

Abstract: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer. The computer is programmed to acquire a plurality of projection datasets of the object, define a temporal subset of projection datasets from the plurality of projection datasets, reconstruct a working image of the object using the plurality of projection datasets, identify a region of motion in the working image, and minimize motion artifacts in the region of motion in the working image using the temporal subset of projection datasets.

Abstract: A method for performing truncation artifact correction includes acquiring a projection dataset of a patient, the projection dataset including measured data and truncated data, generating an initial estimate of a boundary between the measured data and the truncated data, using the measured data to revise the initial estimate of the boundary, estimating the truncated data using the revised estimate of the boundary, and using the measured data and the estimated truncated data to generate an image of the patient.

Abstract: A method is provided. The method includes acquiring a first dataset at a first energy spectrum and a second dataset at a second energy spectrum. The method also includes extracting a metal artifact correction signal using the first dataset and the second dataset or using a first reconstructed image and a second reconstructed image generated respectively from the first and the second datasets. The method further includes performing metal artifact correction on the first reconstructed image using the metal artifact correction signal to generate a first corrected image.

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: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer programmed to acquire a plurality of helical projection datasets of the object, reconstruct a first image using the acquired plurality of helical projection datasets and using a first reconstruction algorithm, reconstruct a second image using the acquired plurality of helical projection datasets and using a second reconstruction algorithm that is different from the first reconstruction algorithm, extract frequency components from each of the first and second images, sum the frequency components from each of the first and second images, and inverse transform the sum of the frequency components to generate a final image.

Abstract: A method for reconstructing an image of an object includes acquiring a set of measured projection data, reconstructing the measured projection data using a first algorithm to generate a first reconstructed image dataset, reconstructing the measured projection data using a second algorithm to generate a second reconstructed image dataset, the second algorithm being utilized to improve the temporal resolution of the second reconstructed image dataset, and generating a final image dataset using both the first and second image datasets.

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 method for reconstructing an image of an object having reduced motion artifacts includes reconstructing a set of initial images using acquired data, performing a thresholding operation on the set of initial images to generate a set of contrast images that identify areas of contrast from which motion artifacts originate, transforming the thresholded images into a conjugate domain, combining the conjugate domain representations of the contrast images, transforming the combined conjugate domain representations to an image domain to generate a residual image, and using the residual image to generate a final image of the object.

Abstract: A tomographic system includes a gantry having an opening for receiving an object to be scanned, a radiation source, a detector positioned to receive radiation from the source that passes through the object, and a computer. The computer is programmed to acquire a plurality of projection datasets of the object, define a temporal subset of projection datasets from the plurality of projection datasets, reconstruct a working image of the object using the plurality of projection datasets, identify a region of motion in the working image, and minimize motion artifacts in the region of motion in the working image using the temporal subset of projection datasets.