Wisconsin Alumni Research Foundation

General Electric Company

Ge Precision Healthcare LLC

Guang-Hong Chen

Jie Tang

Jiang Hsieh

Ke Li

Charles Anthony Mistretta

Thomas M Grist

Yinsheng Li

Brian Edward Nett

Charles Milton Strother

Shuai Leng

Alexey A Samsonov

Howard Andrew Rowley

Huimin Wu

Michael Scott VanLysel

Juan Camilo Montoya

Ran Zhang

Walter F Block

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.

Systems and methods for improved computed tomography using photon-counting detectors

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.

Systems and methods for controlling scatter in computed tomography data

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.

System and method for controlling errors in computed tomography number

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.

Systems and methods for computed tomography image reconstruction

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.

System and method for quantitative blood volume imaging

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.

System and method for reconstructing image volumes from sparse two-dimensional projection data

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.

System and method for spectral computed tomography using single polychromatic x-ray spectrum acquisition

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.

System and method for multi-architecture computed tomography pipeline

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.

System and method for temporal fidelity enhanced medical imaging using temporal deconvolution

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.

System and method for data-consistency preparation and image reconstruction

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.

System and method for denoising medical images by enforcing low rank spatial-temporal or spatial-spectral image matrices

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.

System and method for high-temporal resolution, time-resolved cone beam CT angiography

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.

System and method for simulataneous image artifact reduction and tomographic reconstruction of images depicting temporal contrast dynamics

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.

Systems and methods for artifact removal for computed tomography imaging

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.

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Madison, WI, United States of America

Guang-Hong Chen