Abstract: A system and method for generating a computed tomography (CT) image of an object includes establishing a narrow-spectrum high-energy bin and other bins with wider-spectrum and lower-energies and acquiring a first dataset using the narrow-spectrum high-energy bin. The method also includes acquiring second or more datasets using the wide-spectrum, low-energy bins, reducing data attributable to scatter from the second or more datasets using the first dataset to create reduced-scatter datasets, and reconstructing CT images of the object from the reduced-scatter datasets.

Abstract: A system and method for creating computed tomography (CT) images includes acquiring or accessing sinogram data and determining a photon count for the sinogram data. The method includes generating unbiased sinogram data from the sinogram data using an unbiased estimator and the photon count and reconstructing a CT image from the unbiased sinogram data.

Abstract: A system and method for generating a computed tomography (CT) image of an object includes establishing a narrow-spectrum high-energy bin and other bins with wider-spectrum and lower-energies and acquiring a first dataset using the narrow-spectrum high-energy bin. The method also includes acquiring second or more datasets using the wide-spectrum, low-energy bins, reducing data attributable to scatter from the second or more datasets using the first dataset to create reduced-scatter datasets, and reconstructing CT images of the object from the reduced-scatter datasets.

Abstract: A system and method for creating computed tomography (CT) images that includes acquiring or accessing CT data of a subject, identifying zero counts in the CT data, and replacing the zero counts in the CT data with at least one non-zero number to create zero-count free CT data. The method also includes estimating a probability function of the zero-count free CT data, removing bias in the zero-count free CT data using the probability function, and reconstructing the zero-count free CT data after removal of the bias to create a corrected image of the subject with preserved conditional independence and spatial resolution.

Abstract: A system and method for creating computed tomography (CT) images of a subject imaged with a CT system including acquiring or accessing data that includes or can be transformed into a first sinogram acquired by the CT system without the patient present and a second sinogram acquired by the CT system with the patient present. The method also includes determining a sinogram bias using the first sinogram and the second sinogram, generating a bias image from the CT data using the sinogram bias, and generating an unbiased image of the subject using the bias image and an image produced from the second sinogram.

Abstract: A system and method for a hybrid detector for a computed tomography (CT) system are provided that include a semiconductor detector arranged to generate photon-counting CT data upon receiving x-rays and a scintillator detector arranged proximate to the semiconductor detector to receive scattered x-rays and generate energy-integrating CT data upon receiving the scattered x-rays.

Abstract: A system and method for generating a computed tomography (CT) image of an object includes establishing a narrow-spectrum high-energy bin and other bins with wider-spectrum and lower-energies and acquiring a first dataset using the narrow-spectrum high-energy bin. The method also includes acquiring second or more datasets using the wide-spectrum, low-energy bins, reducing data attributable to scatter from the second or more datasets using the first dataset to create reduced-scatter datasets, and reconstructing CT images of the object from the reduced-scatter datasets.

Abstract: Methods and systems are provided for increasing a quality of computed tomography (CT) images reconstructed from high helical pitch scans. In one embodiment, the current disclosure provides for a method comprising generating a first computed tomography (CT) image from projection data acquired at a high helical pitch; using a trained multidimensional statistical regression model to generate a second CT image from the first CT image, the multidimensional statistical regression model trained with a plurality of target CT images reconstructed from projection data acquired at a lower helical pitch; and performing an iterative correction of the second CT image to generate a final CT image.

Abstract: A system and method for generating reports on perfusion blood volume from computed tomography (CT) data acquired from a subject. The method includes receiving multi-faceted CT data acquired from the subject using one of a multi-energy or polychromatic CT acquisition and deriving an iodine concentration in an artery feeding a volume of interest (VOI) in the multi-faceted CT data. The method further includes determining an effective atomic number of a spatial distribution in the VOL calculating a perfused blood volume of the VOI using the iodine concentration and the effective atomic number, and generating a report of the perfused blood volume of the VOI.

Abstract: A system and method for creating computed tomography (CT) images includes acquiring or accessing sinogram data and determining a photon count for the sinogram data. The method includes generating unbiased sinogram data from the sinogram data using an unbiased estimator and the photon count and reconstructing a CT image from the unbiased sinogram data.

Abstract: A system and method for generating reports on perfusion blood volume from computed tomography (CT) data acquired from a subject. The method includes receiving multi-faceted CT data acquired from the subject using one of a multi-energy or polychromatic CT acquisition and deriving an iodine concentration in an artery feeding a volume of interest (VOI) in the multi-faceted CT data. The method further includes determining an effective atomic number of a spatial distribution in the VOL calculating a perfused blood volume of the VOI using the iodine concentration and the effective atomic number, and generating a report of the perfused blood volume of the VOI.

Abstract: Methods and systems are provided for increasing a quality of computed tomography (CT) images reconstructed from high helical pitch scans. In one embodiment, the current disclosure provides for a method comprising generating a first computed tomography (CT) image from projection data acquired at a high helical pitch; using a trained multidimensional statistical regression model to generate a second CT image from the first CT image, the multidimensional statistical regression model trained with a plurality of target CT images reconstructed from projection data acquired at a lower helical pitch; and performing an iterative correction of the second CT image to generate a final CT image.

Abstract: A system and method for acquiring medical images of a subject includes performing two-dimensional (2D) scan of a subject using a medical imaging system to acquire 2D data from at least two view angles and generating a three-dimensional (3D) model of the subject from the 2D data. The method also includes extracting desired images of the subject from the 3D model. The desired images are at view angles different from the at least two view angles. The method further includes prescribing an imaging study of the subject using the desired images of the subject to control at least one of a signal-to-noise ratio of data acquired using the imaging study or a dose of ionizing radiation delivered to the subject during the imaging study. The method also includes performing the imaging study using the medical imaging system to acquire imaging data from the subject and reconstructing images of the subject from the imaging data.

Abstract: A system and method for material decomposition of a single energy spectrum x-ray dataset includes accessing the single energy spectrum x-ray dataset, receiving a user-selection of a desired energy for decomposition, and decomposing the single energy spectrum x-ray dataset into material bases as a linear combination of energy dependence function of selected basis materials and the corresponding spatial dependence material bases images.

Abstract: A system and method for reconstructing an image of a subject acquired using a tomographic imaging system includes at least one computer processor configured to form an image reconstruction pipeline. The reconstruction pipeline at least includes an automated correction module configured to receive imaging data acquired from a subject using ionizing radiation generated by the tomographic imaging system and generate corrected data using a first learning network. The reconstruction pipeline also includes an intelligent reconstruction module configured to receive at least one of the imaging data and the corrected data and reconstruct an image of the subject using a second learning network.

Abstract: A system and method for material decomposition of a single energy spectrum x-ray dataset includes accessing the single energy spectrum x-ray dataset, receiving a user-selection of a desired energy for decomposition, and decomposing the single energy spectrum x-ray dataset into material bases as a linear combination of energy dependence function of selected basis materials and the corresponding spatial dependence material bases images.

Abstract: A system and method for acquiring medical images of a subject includes performing two-dimensional (2D) scan of a subject using a medical imaging system to acquire 2D data from at least two view angles and generating a three-dimensional (3D) model of the subject from the 2D data. The method also includes extracting desired images of the subject from the 3D model. The desired images are at view angles different from the at least two view angles. The method further includes prescribing an imaging study of the subject using the desired images of the subject to control at least one of a signal-to-noise ratio of data acquired using the imaging study or a dose of ionizing radiation delivered to the subject during the imaging study. The method also includes performing the imaging study using the medical imaging system to acquire imaging data from the subject and reconstructing images of the subject from the imaging data.

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 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 and method for reconstructing an image of a subject acquired using a tomographic imaging system includes at least one computer processor configured to form an image reconstruction pipeline. The reconstruction pipeline at least includes an automated correction module configured to receive imaging data acquired from a subject using ionizing radiation generated by the tomographic imaging system and generate corrected data using a first learning network. The reconstruction pipeline also includes an intelligent reconstruction module configured to receive at least one of the imaging data and the corrected data and reconstruct an image of the subject using a second learning network.