General Electric Company

Ge Precision Healthcare LLC

University of Michigan

Purdue Research Foundation

University of Notre Dame Du Lac

Rensselaer Polytechnic Institute

Lin Fu

Bruno Kristiaan Bernard De Man

Sathish Ramani

Jean-Baptiste Thibault

Zhou Yu

Jeffrey Allen Fessler

Debashish Pal

Charles Addison Bouman

Ken David Sauer

Xue Rui

Somesh Srivastava

Jiang Hsieh

Peter Michael Edic

Jed Douglas Pack

Bruno Kristiaan Bernard DeMan

Thomas Matthew Benson

Donghwan Kim

Ge Wang

Brian Edward Nett

Brian David Yanoff

Robert F Senzig

Vasile Bogdan Neculaes

James Vradenburg Miller

Yannan Jin

Geng Fu

Jang Hwan Cho

Sangtae Ahn

Kai Zeng

Mingye Wu

Guangzhi Cao

Evgeny Drapkin

Jie Tang

Fredrik Gronberg

Madison Gray McGaffin

Hung Nien

Various methods and systems are provided for stationary computed tomography (CT) imaging. In one embodiment, a stationary CT system includes one or more detector arrays extending around at least a portion of an imaging volume, a stationary distributed x-ray source unit comprising a plurality of emitters including a first set of emitters configured to operate at a first voltage and a second set of emitters configured to operate at a second voltage, different than the first voltage, and a source controller for triggering the first set of emitters for acquiring first projection data by the one or more detector arrays and triggering the second set of emitters for acquiring second projection data by the one or more detector arrays, the first projection data and the second projection data usable to reconstruct one or more basis material composition images or monochromatic images of an object within the imaging volume.

Systems and methods for a stationary CT imaging system

Various methods and systems are provided for stationary CT imaging. In one embodiment, an imaging system comprises a chamber shaped to enclose a subject to be imaged, a support surface disposed within the chamber and shaped to maintain the subject in an upright position, and an annular imaging unit encircling the chamber and having a fixed angular orientation to the chamber, the annular imaging unit including a distributed x-ray unit and a detector array arranged opposite to each other across the chamber. The imaging system may image the subject without rotation of the annular imaging unit.

A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

System and method for normalizing dynamic range of data acquired utilizing medical imaging

Various methods and systems are provided for stationary CT imaging. In one embodiment, a method for an imaging system includes activating a plurality of emitters of a stationary distributed x-ray source unit to emit x-ray beams toward an object within an imaging volume, where the x-ray source unit does not rotate around the imaging volume, receiving attenuated x-ray beams with one or more detector arrays to form a sparse view projection dataset, where each attenuated x-ray beam generates a different view, and reconstructing an image from the sparse view projection dataset using a sparse view reconstruction method.

The present disclosure relates to image reconstruction with favorable properties in terms of noise reduction, spatial resolution, detail preservation and computational complexity. The disclosed techniques may include some or all of: a first-pass reconstruction, a simplified datafit term, and/or a deep learning denoiser. In various implementations, the disclosed technique is portable to different CT platforms, such as by incorporating a first-pass reconstruction step.

Iterative image reconstruction framework

A computer-implemented method for image processing is provided. The method includes acquiring multiple multi-energy spectral scan datasets and computing basis material images representative of multiple basis materials from the multi-energy spectral scan datasets, wherein the multiple basis material images include correlated noise. The method also includes jointly denoising the multiple basis material images in at least a spectral domain utilizing a deep learning-based denoising network to generate multiple de-noised basis material images.

System and method for processing data acquired utilizing multi-energy computed tomography imaging

A method for imaging an object to be reconstructed includes acquiring projection data corresponding to the object. Furthermore, the method includes generating a measured sinogram based on the acquired projection data and formulating a forward model, where the forward model is representative of a characteristic of the imaging system. In addition, the method includes generating an estimated sinogram based on an estimated image of the object and the forward model and formulating a statistical model based on at least one of pile-up characteristics and dead time characteristics of a detector of the imaging system. Moreover, the method includes determining an update corresponding to the estimated image based on the statistical model, the measured sinogram, and the estimated sinogram and updating the estimated image based on the determined update to generate an updated image of the object. Additionally, the method includes outputting a final image of the object.

System and method for statistical iterative reconstruction and material decomposition

The present disclosure relates to the use of a hierarchical tomographic reconstruction approach that employs data representations in intermediate steps are between a full line integral and a voxel (e.g., an intermediate line integral). Each of the steps is progressively more local in nature and therefore has computational advantages and is also amenable to a deep learning solution using trained neural networks. The proposed hierarchical structure provides a mechanism to divide a large-scale inverse problem into a series of smaller-scale problems.

Hierarchical tomographic reconstruction

The present approach relates to the use of machine learning and deep learning systems suitable for solving large-scale, space-variant tomographic reconstruction and/or correction problems. In certain embodiments, a tomographic transform of measured data obtained from a tomography scanner is used as an input to a neural network. In accordance with certain aspects of the present approach, the tomographic transform operation(s) is performed separate from or outside the neural network such that the result of the tomographic transform operation is instead provided as an input to the neural network. In addition, in certain embodiments, one or more layers of the neural network may be provided as wavelet filter banks.

Tomographic reconstruction based on deep learning

The present approaches relate to frequency-split iterative reconstruction approaches. In some embodiment, such approaches provide for the combination of the low frequency components of an analytical reconstruction (e.g., a filtered back projection) and the high frequency components of an iterative reconstruction. In certain embodiments, frequency-split iterative reconstruction is used for generating region of interest images.

Method for tomographic reconstruction

A framework for an iterative reconstruction algorithm is described which combines two or more of an ordered subset method, a preconditioner method, and a nested loop method. In one type of implementation a nested loop (NL) structure is employed where the inner loop sub-problems are solved using ordered subset (OS) methods. The inner loop may be solved using OS and a preconditioner method. In other implementations, the inner loop problems are created by augmented Lagrangian methods and then solved using OS method.

Accelerated iterative reconstruction

Methods, systems, and non-transitory computer readable media for image reconstruction are presented. Measured data corresponding to a subject is received. A preliminary image update in a particular iteration is determined based on one or more image variables computed using at least a subset of the measured data in the particular iteration. Additionally, at least one momentum term is determined based on the one or more image variables computed in the particular iteration and/or one or more further image variables computed in one or more iterations preceding the particular iteration. Further, a subsequent image update is determined using the preliminary image update and the momentum term. The preliminary image update and/or the subsequent image update are iteratively computed for a plurality of iterations until one or more termination criteria are satisfied.

Ordered subsets with momentum for X-ray CT image reconstruction

The use of the channelized preconditioners in iterative reconstruction is disclosed. In certain embodiments, different channels correspond to different frequency sub-bands and the output of the different channels can be combined to update an image estimate used in the iterative reconstruction process. While individual channels may be relatively simple, the combined channels can represent complex spatial variant operations. The use of channelized preconditioners allows empirical adjustment of individual channels.

Iterative reconstruction in image formation

A system and method include acquisition of projection data from a scanned object, the set of projection data comprising a plurality of projection measurements. The system and method also include calculation of a set of modified statistical weights from the projection data, wherein a respective modified statistical weight of the set of modified statistical weights comprises a deviation from an inverse variance of a corresponding projection measurement of the projection data. The system and method further include reconstruction of an image of the scanned object using the set of modified statistical weights as coefficients in an iterative reconstruction algorithm.

System and method of iterative image reconstruction for computed tomography

A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Growing community of inventors

Niskayuna, NY, United States of America

Lin Fu