Rensselaer Polytechnic Institute

University of Iowa Research Foundation

Virginia Tech Intellectual Properties Inc

General Electric Company

University of California

Ge Medical Systems Global Technology Company, LLC

Shandong University

Shandong Provincial Chest Hospital

Koninklijke Philips Corporation N.v.

University of Central Florida Research Foundation Inc

Washington University

Pusan National University

Research Foundation of State of N.y.

Ge Wang

Wenxiang Cong

Hongming Shan

Qingsong Yang

Lars Arne Gjesteby

Yan Xi

Hengyong Yu

Jiang Hsieh

Michael W Vannier

Guang Li

Daniel David Harrison

Zaifeng Shi

David S Rundle

Yuchuan Wei

Matthew Webber Getzin

Fenglin Liu

Bruno Kristiaan Bernard De Man

Jun Zhao

Eric A Hoffman

Yang Lu

Geoffrey McLennan

Lizhi Sun

Yingmei Wang

Shiying Zhao

Ye Yangbo

Yangbo Ye

Shuwei Mao

Ruibin Feng

Chenyu You

Mengzhou Li

Rachel Berry

Yong Xu

Alexander Cong

Haiou Shen

Alok Mani Srivastava

Yi Liu

Peter Michael Edic

Holly Ann Comanzo

Robert John Filkins

Peter William Lorraine

Xiaolei Shi

Jed Douglas Pack

Vasile Bogdan Neculaes

Seung Wook Lee

Ming Jiang

Dominic J Heuscher

Alexander Katsevich

Yannan Jin

Lin Fu

Christopher Rylander

Thomas A Campbell

Ahmad Nadeem Ishaque

Sathish Ramani

Gregory Boverman

Jie Tang

Joseph A O'Sullivan

Donald L Snyder

Shuo Pang

Sam Joseph Camardello

Klaus Mueller

Xun Jia

David D Matthews

Juergen Hahn

Mehmet Alpaslan Kosoglu

Uwe Kruger

Guohua Cao

Seungwook Lee

Ohsung Oh

Zhiguo Wang

Er-wei Bai

Qing Lyu

Tao Xu

Dongxu Liu

Zhijian Hu

Zhan Zhang

Bruce P Brown

Elizabeth G McFarland

Gary D Schweiger

Ping-Chin Cheng

Huidong Xie

Mianyi Chen

Zheng Fang

Weiwen Wu

Seho Lee

Chao Wang

Jiyang Chu

Jiansheng Yang

Mannudeep Kalra

Lin Yang

Weimin Han

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

The present disclosure relates to an apparatus for enhancing contrast sensitivity and resolution in a grating interferometer by machine learning, which can improve both image contrast sensitivity and spatial resolution in a grating interferometer by machine learning, the apparatus including: an image acquisition unit; a numerical phantom generation unit, a convolution layer generation unit to extract features from input data; an activation function application calculation unit that can apply a rectified linear activation function to an output value of the convolution calculation to perform smooth repetitive machine learning; a CNN repetitive machine learning unit that corrects a convolution calculation factor while repeatedly performing forward propagation and backward propagation processes; and an image matching output unit that matches and outputs features extracted by repetitive machine learning of the CNN repetitive machine learning unit.

Enhancing contrast sensitivity and resolution in a grating interferometer by machine learning

In some embodiments, a method of machine learning includes identifying, by an auto encoder network, a simulator feature based, at least in part, on a received first simulator data set and an emulator feature based, at least in part, on a received first emulator data set. The method further includes determining, by a synthesis control circuitry, a synthesized feature based, at least in part, on the simulator feature and based, at least in part, on the emulator feature; and generating, by the auto encoder network, an intermediate data set based, at least in part, on a second simulator data set and including the synthesized feature. Some embodiments of the method further include determining, by a generative artificial neural network, a synthesized data set based, at least in part, on the intermediate data set and based, at least in part, on an objective function.

CT big data from simulation, emulation and transfer learning

Methods and systems for performing optogenetics using X-rays or ultrasound waves are provided. Visible-light-emitting nanophosphors can be provided to a sample, and X-ray stimulation can be used to stimulate the nanophosphors to emit visible light. Alternatively, ultrasonic waves can be provided to the sample to cause sonoluminescence, also resulting in emission of visible light, and this can be aided by the use of a chemiluminescent agent present in the sample. The emitted light can trigger changes in proteins that modulate membrane potentials in neuronal cells.

X-optogenetics / U-optogenetics

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)

A method for x-ray photon-counting data correction. The method includes generating, by a training data generation module, training input spectral projection data based, at least in part, on a reference spectral projection data. The training input spectral projection data includes at least one of a pulse pileup distortion, a charge splitting distortion, and/or noise. The method further includes training, by a training module, a data correction artificial neural network (ANN) based, at least in part, on training data. The data correction ANN includes a pulse pileup correction ANN, and a charge splitting correction ANN. The training data includes the training input spectral projection data and the reference spectral projection data.

X-ray photon-counting data correction through deep learning

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 deep neural network for metal artifact reduction is described. A method for computed tomography (CT) metal artifact reduction (MAR) includes receiving a first CT image data; receiving a second CT image data; and generating, by an artificial neural network (ANN), CT output image data configured to include fewer artifacts compared to the first and second CT image data. The ANN includes at least two parallel CT image data streams and a CT output image data stream. A first of the at least two parallel CT image data streams is based, at least in part, on the first CT image data, a second of the at least two parallel CT image data stream is based, at least in part, on the second CT image data. The CT output image data stream is based, at least in part, on respective outputs of the at least two parallel CT image data streams.

Deep neural network for CT metal artifact reduction

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

Training a CNN with pseudo ground truth for CT artifact reduction is described. An estimated ground truth apparatus is configured to generate an estimated ground truth image based, at least in part, on an initial CT image that includes an artifact. Feature addition circuitry is configured to add a respective feature to each of a number, N, copies of the estimated ground truth image to create the number, N, initial training images. A computed tomography (CT) simulation circuitry is configured to generate a plurality of simulated training CT images based, at least in part, on at least some of the N initial training images. An artifact reduction circuitry is configured to generate a plurality of input training CT images based, at least in part, on the simulated training CT images. A CNN training circuitry is configured to train the CNN based, at least in part, on the input training CT images and based, at least in part, on the initial training images.

Training a CNN with pseudo ground truth for CT artifact reduction

Growing community of inventors

Loudonville, NY, United States of America

Ge Wang