Abstract: A system and method for reconstructing an image of a subject includes acquiring a reference dataset and reconstructing a prior image of the subject from the reference dataset and selecting at least a portion of the prior image that corresponds to a portion of the prior image of the subject that is free of artifacts. The method also includes acquiring a medical imaging dataset of the subject, performing a vertical comparison of the medical imaging dataset and the reference dataset to create a data inconsistency metric, and repeating the preceding steps to create a plurality of data inconsistency metrics. The method further includes performing a horizontal comparison of the data inconsistency metrics to identify inconsistent data, compensating for the inconsistent data, and reconstructing an image of the subject with reduced artifacts compared to the prior image.

Abstract: A system for measuring the profile or the refractive index of a transparent object by fringe projection techniques is provided and has an image generating device, an image capture device, and an image processor. The image generating device produces a reference image with a long depth of focus. This reference image is emitted into an inspected transparent object, and is distorted by the refractive index and the profile of the transparent object. The image capture device receives the distorted image. The image processor analyzes the difference between the distorted image and the reference image, so as to identify the profile or the refractive index of the inspected transparent object.

Abstract: A system and method for reconstructing an image using a cone-beam computed tomography (CT) imaging system includes acquiring data from a subject with the CT imaging system using a limited scan range that is less than 360 degrees. The process also includes reconstructing at least one image of the subject having a first temporal resolution from the data acquired, performing a temporal deconvolution of the at least one image using a finite temporal window to generate at least one image of the subject with a second temporal resolution that is greater than the first temporal resolution, and subtracting the at least one image of the subject with the second temporal resolution and a mask image of the subject to generate a time-resolved CT angiogram of the subject.

Abstract: Systems and methods for generating one or more denoised images from a series of noisy images acquired with a medical imaging system are described. In general, the systems and methods described here implement techniques whereby the series of noisy images are formed into a spatial-temporal or spatial-spectral image matrix in which each column represents a different noisy image. The image matrix is then processed to decompose the image matrix into basis images defined by a spatial and a temporal or spectral basis. Low rank solutions are enforced and extracted from the resulting decomposed image matrix as denoised images.

Abstract: Described here is a system and method for image reconstruction that can automatically and iteratively produce multiple images from one set of acquired data, in which each of these multiple images corresponds to a subset of the acquired data that is self-consistent, but inconsistent with other subsets of the acquired data. The image reconstruction includes iteratively minimizing the rank of an image matrix whose columns each correspond to a different image, and in which one column corresponds to a user-provided prior image of the subject. The rank minimization is constrained subject to a consistency condition that enforces consistency between the forward projection of each column in the image matrix and a respective subset of the acquired data that contains data that is consistent with data in the subset, but inconsistent with data not in the subset.

Abstract: An imaging system includes a computed tomography (CT) acquisition unit and at least one processor. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The at least one processor is operably coupled to the CT acquisition unit, and is configured to reconstruct an image using the CT imaging information; extract spatial frequency information from at least a portion of the image, wherein the spatial frequency is defined along a longitudinal direction; and remove a periodically recurring artifact from the at least a portion of the image based on a spatial frequency corresponding to a longitudinal collection periodicity to provide a corrected image.

Abstract: An imaging system includes a computed tomography (CT) acquisition unit and at least one processor. The CT acquisition unit includes an X-ray source and a CT detector configured to collect CT imaging data of an object to be imaged. The at least one processor is operably coupled to the CT acquisition unit, and is configured to reconstruct an image using the CT imaging information; extract spatial frequency information from at least a portion of the image, wherein the spatial frequency is defined along a longitudinal direction; and remove a periodically recurring artifact from the at least a portion of the image based on a spatial frequency corresponding to a longitudinal collection periodicity to provide a corrected image.

Abstract: A system for measuring the profile or the refractive index of a transparent object by fringe projection techniques is provided and has an image generating device, an image capture device, and an image processor. The image generating device produces a reference image with a long depth of focus. This reference image is emitted into an inspected transparent object, and is distorted by the refractive index and the profile of the transparent object. The image capture device receives the distorted image. The image processor analyzes the difference between the distorted image and the reference image, so as to identify the profile or the refractive index of the inspected transparent object.

Abstract: Described here is a system and method for image reconstruction that can automatically and iteratively produce multiple images from one set of acquired data, in which each of these multiple images corresponds to a subset of the acquired data that is self-consistent, but inconsistent with other subsets of the acquired data. The image reconstruction includes iteratively minimizing the rank of an image matrix whose columns each correspond to a different image. The rank minimization is constrained subject to a consistency condition that enforces consistency between the forward projection of each column in the image matrix and a respective subset of the acquired data that contains data that is consistent with data in the subset, but inconsistent with data not in the subset.

Abstract: Described here are systems and methods for iteratively reconstructing images from data acquired using a medical imaging system. The image reconstruction is decomposed into separate linear sub-problems that can be more efficiently solved. A statistical image reconstruction process is decomposed into a statistically-weighted algebraic reconstruction update sequence. After this step, the reconstructed image is denoised using a regularization function.

Abstract: A system and method for generating multi-energy computed tomography images of a subject using a polychromatic x-ray source with single spectrum includes acquiring a measure of a polychromatic spectrum of a polychromatic x-ray beam generated by the polychromatic x-ray source. The method also includes acquiring attenuation data generated by operating the polychromatic x-ray source, segmenting the attenuation data based on a plurality of component criteria to create plurality of segmented datasets, and generating template data from the segmented datasets. Using the template data and the measure of the polychromatic spectrum, polychromatic synthetic data is generated. Using the template data and each of the segmented datasets, component synthetic data is generated. Using the attenuation data, the polychromatic synthetic data, and the component synthetic data, a plurality of multi-energy images, including separable images weighted for each of the component criteria, is reconstructed.

Abstract: Described here are systems and methods for generating x-ray phase contrast images from conventional x-ray attenuation data. X-ray attenuation coefficients generated over a range of x-ray energies are used to compute the x-ray phase signal up to a calibration constant. This calibration constant is computed from provided calibration data, which may be obtained using a dedicated x-ray differential phase contrast imaging system to measure the decrement of the refractive index of a calibration phantom.

Abstract: Described here are a system and method for producing a statistical noise map that indicates the noise present in data acquired with an x-ray imaging system, such as an x-ray computed tomography system, an x-ray tomosynthesis system, a C-arm x-ray imaging system, and so on. In general, an image is reconstructed from the acquired data using, for example, any standard filtered back projection (“FBP”) image reconstruction algorithm. This image is used as a base line to estimate a noise standard distribution map. The raw projection data represents a typical measurement among many repeated measurements under the same experimental conditions; therefore, the measured projection data can be used to numerically generate an ensemble of many (e.g., twenty or more) noisy projection data sets. These noisy projection data sets are then used to reconstruct noisy images and from these noisy images and the original image, the statistical noise map can be computed.

Abstract: A system and method for generating multi-energy computed tomography images of a subject using a polychromatic x-ray source with single spectrum includes acquiring a measure of a polychromatic spectrum of a polychromatic x-ray beam generated by the polychromatic x-ray source. The method also includes acquiring attenuation data generated by operating the polychromatic x-ray source, segmenting the attenuation data based on a plurality of component criteria to create plurality of segmented datasets, and generating template data from the segmented datasets. Using the template data and the measure of the polychromatic spectrum, polychromatic synthetic data is generated. Using the template data and each of the segmented datasets, component synthetic data is generated. Using the attenuation data, the polychromatic synthetic data, and the component synthetic data, a plurality of multi-energy images, including separable images weighted for each of the component criteria, is reconstructed.

Abstract: Described here are systems and methods for generating x-ray phase contrast images from conventional x-ray attenuation data. X-ray attenuation coefficients generated over a range of x-ray energies are used to compute the x-ray phase signal up to a calibration constant. This calibration constant is computed from provided calibration data, which may be obtained using a dedicated x-ray differential phase contrast imaging system to measure the decrement of the refractive index of a calibration phantom.

Abstract: Described here are systems and methods for iteratively reconstructing images from data acquired using a medical imaging system. The image reconstruction is decomposed into separate linear sub-problems that can be more efficiently solved. A statistical image reconstruction process is decomposed into a statistically-weighted algebraic reconstruction update sequence. After this step, the reconstructed image is denoised using a regularization function.

Abstract: Described here are a system and method for producing a statistical noise map that indicates the noise present in data acquired with an x-ray imaging system, such as an x-ray computed tomography system, an x-ray tomosynthesis system, a C-arm x-ray imaging system, and so on. In general, an image is reconstructed from the acquired data using, for example, any standard filtered back projection (“FBP”) image reconstruction algorithm. This image is used as a base line to estimate a noise standard distribution map. The raw projection data represents a typical measurement among many repeated measurements under the same experimental conditions; therefore, the measured projection data can be used to numerically generate an ensemble of many (e.g., twenty or more) noisy projection data sets. These noisy projection data sets are then used to reconstruct noisy images and from these noisy images and the original image, the statistical noise map can be computed.

Abstract: Described here is a system and method for image reconstruction that can automatically and iteratively produce multiple images from one set of acquired data, in which each of these multiple images corresponds to a subset of the acquired data that is self-consistent, but inconsistent with other subsets of the acquired data. The image reconstruction includes iteratively minimizing the rank of an image matrix whose columns each correspond to a different image. The rank minimization is constrained subject to a consistency condition that enforces consistency between the forward projection of each column in the image matrix and a respective subset of the acquired data that contains data that is consistent with data in the subset, but inconsistent with data not in the subset.

Abstract: A method for increasing the temporal fidelity, increasing the temporal sampling density, and/or reducing the temporal noise of a series of image frames obtained with a medical imaging system is provided. The image frames are acquired with the medical imaging system. The medical imaging system may be, for example, an x-ray C-arm imaging system. A window function that is representative of a temporal fidelity window is selected and used to temporally deconvolve the image frames using a minimization technique. A temporal sampling density may also be selected and used in the temporal deconvolution. The resultant deconvolved image frames have a higher temporal fidelity to a time-varying image contrast depicted in the acquired image frames, and may also have an increased temporal sampling density and/or reduced temporal noise.

Abstract: A method for producing an image having a high signal-to-noise ratio (SNR) is provided. An image to be enhanced is provided, the provided image including a previously reconstructed image or an image reconstructed from acquired image data. A prior image is produced from the provided image, for example, by filtering the provided image such that noise from the provided image is substantially suppressed in the prior image. Synthesized image data is produced by performing a forward projection of the provided image. A sparsified image is produced by subtracting the prior image and the provided image. A target image having a higher SNR than the provided image is reconstructed using the sparsified image, the provided image, and the synthesized image data. The provided image may be, for example, a medical image produced by an x-ray imaging system, including computed tomography and C-arm systems; a magnetic resonance imaging system; and the like.