Rensselaer Polytechnic Institute

University of Iowa Research Foundation

Virginia Tech Intellectual Properties, Inc.

Shandong University

Shandong Provincial Chest Hospital

General Electric Company

University of Central Florida Research Foundation, Inc.

Wenxiang Cong

Ge Wang

Qingsong Yang

Yan Xi

Hongming Shan

Guang Li

Zaifeng Shi

Fenglin Liu

Lars Arne Gjesteby

Hengyong Yu

Alexander Cong

Haiou Shen

Ye Yangbo

Shuwei Mao

Yingmei Wang

Chenyu You

Alok Mani Srivastava

Holly Ann Comanzo

Robert John Filkins

Peter William Lorraine

Xiaolei Shi

Vasile Bogdan Neculaes

Jun Zhao

Ahmad Nadeem Ishaque

Gregory Boverman

Yang Lu

Sam Joseph Camardello

Shuo Pang

Matthew Webber Getzin

Uwe Kruger

Yong Xu

Juergen Hahn

Mengzhou Li

Lin Yang

Weimin Han

Ming Jiang

Huidong Xie

Zheng Fang

Weiwen Wu

Mianyi Chen

Chao Wang

Mannudeep Kalra

Jiyang Chu

In one embodiment, there is provided a micro-CT (computed tomography) apparatus. The micro-CT apparatus includes an x-ray source coupled to a source robotic arm, an x-ray detector coupled to a detector robotic arm, and a computing device. The computing device includes a data acquisition module and a reconstruction module. The data acquisition module is configured to acquire local scan data of a volume of interest (VOI) contained in an imaging object. The reconstruction module is configured to reconstruct an image of the VOI based, at least in part, on the local scan data, and based, at least in part, on background compensation data.

Robotic arm-based clinical micro-CT system and method

Generally, there is provided a hybrid image reconstruction system. The hybrid image reconstruction system includes a deep learning stage and a compressed sensing stage. The deep learning stage is configured to receive an input data set that includes measured tomographic data and to produce a deep learning stage output. The deep learning stage includes a mapping circuitry, and at least one artificial neural network. The mapping circuitry is configured to map image domain data to a tomographic data domain. The compressed sensing stage is configured to receive the deep learning stage output and to provide refined image data as output.

Hybrid image reconstruction system

Systems and methods for reconstructing images for computed tomography are provided. Image reconstruction can be based on a realistic polychromatic physical model, and can include use of both an analytical algorithm and a single-variable optimization method. The optimization method can be used to solve the non-linear polychromatic X-ray integral model in the projection domain, resulting in an accurate decomposition for sinograms of two physical basis components.

Image reconstruction method for computed tomography

Imaging systems and methods are provided. Systems and methods of the subject invention can include the use of nanoparticles (for example, nanophosphors) within a sample to be imaged. Excitation with radiation, such X-ray radiation, can be performed on the nanoparticles to give rise to a change in one or more resonance parameters of the nanoparticles, and this change can be measured using magnetic resonance imaging to provide localization information.

Nanoparticle-enabled x-ray magnetic resonance imaging (NXMRI)

Systems and method for performing X-ray computed tomography (CT) that can improve spectral separation and decrease motion artifacts without increasing radiation dose are provided. The systems and method can be used with either a kVp-switching source or a single-kVp source. When used with a kVp-switching source, an absorption grating and a filter grating can be disposed between the X-ray source and the sample to be imaged. Relative motion of the filter and absorption gratings can by synchronized to the kVp switching frequency of the X-ray source. When used with a single-kVp source, a combination of absorption and filter gratings can be used and can be driven in an oscillation movement that is optimized for a single-kVp X-ray source. With a single-kVp source, the absorption grating can also be omitted and the filter grating can remain stationary.

Rapid filtration methods for dual-energy X-ray CT

A simultaneous emission-transmission tomography in an MRI hardware framework is described. A method of multimodality imaging includes reconstructing, by a simultaneous emission transmission (SET) circuitry, a concentration image based, at least in part, on a plurality of selected γ-rays; and reconstructing, by the SET circuitry, an attenuation image based, at least in part, on the plurality of selected γ-rays. The plurality of selected γ-rays is emitted by a polarized radio tracer included in a test object. The selected γ-rays are selected based, at least in part, on a radio frequency (RF) pulse and based, at least in part, on a gradient magnetic field.

Simultaneous emission-transmission tomography in an MRI hardware framework

A system for generating a high resolution (HR) computed tomography (CT) image from a low resolution (LR) CT image is described. The system includes a first generative adversarial network (GAN) and a second GAN. The first GAN includes a first generative neural network (G) configured to receive a training LR image dataset and to generate a corresponding estimated HR image dataset, and a first discriminative neural network (D) configured to compare a training HR image dataset and the estimated HR image dataset. The second GAN includes a second generative neural network (F) configured to receive the training HR image dataset and to generate a corresponding estimated LR image dataset, and a second discriminative neural network (D) configured to compare the training LR image dataset and the estimated LR image dataset. The system further includes an optimization module configured to determine an optimization function based, at least in part, on at least one of the estimated HR image dataset and/or the estimated LR image dataset. The optimization function contains at least one loss function. The optimization module is further configured to adjust a plurality of neural network parameters associated with at least one of the first GAN and/or the second GAN, to optimize the optimization function.

CT super-resolution GAN constrained by the identical, residual and cycle learning ensemble (GAN-circle)

A machine-learning-based monochromatic CT image reconstruction method is described for quantitative CT imaging. The neural network is configured to learn a nonlinear mapping function from a training data set to map a CT image, which is reconstructed from a single spectral current-integrating projection data set, to monochromatic projections at a pre-specified energy level, realizing monochromatic CT imaging and overcoming beam hardening. An apparatus, method and/or system are configured to determine, by a trained artificial neural network (ANN), a monochromatic projection data set based, at least in part, on a measured CT image. The measured CT image may be reconstructed based, at least in part, on measured projection data. The measured projection data may be polychromatic. The apparatus, method and/or system may be further configured to reconstruct a monochromatic CT image based, at least in part, on the monochromatic projection data set.

Monochromatic CT image reconstruction from current-integrating data via machine learning

A system for few-view computed tomography (CT) image reconstruction is described. The system includes a preprocessing module, a first generator network, and a discriminator network. The preprocessing module is configured to apply a ramp filter to an input sinogram to yield a filtered sinogram. The first generator network is configured to receive the filtered sinogram, to learn a filtered back-projection operation and to provide a first reconstructed image as output. The first reconstructed image corresponds to the input sinogram. The discriminator network is configured to determine whether a received image corresponds to the first reconstructed image or a corresponding ground truth image. The generator network and the discriminator network correspond to a Wasserstein generative adversarial network (WGAN). The WGAN is optimized using an objective function based, at least in part, on a Wasserstein distance and based, at least in part, on a gradient penalty.

Few-view CT image reconstruction system

A detection scheme for x-ray small angle scattering is described. An x-ray small angle scattering apparatus may include a first grating and a complementary second grating. The first grating includes a plurality of first grating cells. The complementary second grating includes a plurality of second grating cells. The second grating is positioned relative to the first grating. A configuration of the first grating, a configuration of the second grating and the relative positioning of the gratings are configured to pass one or more small angle scattered photons and to block one or more Compton scattered photons and one or more main x-ray photons.

Detection scheme for x-ray small angle scattering

A 3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network is described, A machine learning method for low dose computed tomography (LDCT) image correction is provided. The method includes training, by a training circuitry, a neural network (NN) based, at least in part, on two-dimensional (2-D) training data. The 2-D training data includes a plurality of 2-D training image pairs. Each 2-D image pair includes one training input image and one corresponding target output image. The training includes adjusting at least one of a plurality of 2-D weights based, at least in part, on an objective function. The method further includes refining, by the training circuitry, the NN based, at least in part, on three-dimensional (3-D) training data. The 3-D training data includes a plurality of 3-D training image pairs. Each 3-D training image pair includes a plurality of adjacent 2-D training input images and at least one corresponding target output image. The refining includes adjusting at least one of a plurality of 3-D weights based, at least in part, on the plurality of 2-D weights and based, at least in part, on the objective function. The plurality of 2-D weights includes the at least one adjusted 2-D weight.

3-D convolutional autoencoder for low-dose CT via transfer learning from a 2-D trained network

Methods and systems for stationary computed tomography are disclosed. The methods and systems include a gantry having alternating x-ray sources and x-ray detectors that are stationary during operation of the system. The gantry and pairs of x-ray sources and detectors substantially surrounds an object positioned inside the gantry during operation of the system. Dynamically adjustable collimators are positioned between the x-ray sources and the object. Each of the x-ray sources projects an x-ray beam through the collimators and through the object and the x-ray detectors receive the x-ray beam. The x-ray detectors include means for converting the x-ray beam to raw image data. One or more microprocessors control the x-ray sources and the process raw image data. A data storage device stores instructions, which upon execution by the microprocessor, control the x-ray sources and process the raw image data by converting the raw image data to a digital image.

Methods and systems for stationary computed tomography

A stationary in-vivo grating-enabled micro-CT (computed tomography) architecture (SIGMA) system includes CT scanner control circuitry and a number of imaging chains. Each imaging chain includes an x-ray source array, a phase grating, an analyzer grating and a detector array. Each imaging chain is stationary and each x-ray source array includes a plurality of x-ray source elements. Each imaging chain has a centerline, the centerlines of the number of imaging chains intersect at a center point and a first angle between the centerlines of a first adjacent pair of imaging chains equals a second angle between the centerlines of a second adjacent pair of imaging chains. A plurality of selected x-ray source elements of a first x-ray source array is configured to emit a plurality of x-ray beams in a multiplexing fashion.

Stationary in-vivo grating-enabled micro-CT architecture (sigma)

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Wenxiang Cong