University of Michigan

General Electric Company

University of Notre Dame Du Lac

Purdue Research Foundation

University of Texas System

Jeffrey Allen Fessler

Bruno Kristiaan Bernard De Man

Jean-Baptiste Thibault

Lin Fu

Debashish Pal

Charles Addison Bouman

Zhou Yu

Ken David Sauer

Sathish Ramani

Somesh Srivastava

Donghwan Kim

Jiang Hsieh

Douglas C Noll

Madison Gray McGaffin

Samit Kumar Basu

Jang Hwan Cho

Theodore B Norris

Evgeny Drapkin

Zhaohui Zhong

James Balter

Jean-Baptiste Daniel Marie Thibault

Jon-Fredrik Nielsen

Guanhua Wang

Idris A Elbakri

Hung Nien

Ryan Thome

Yong Long

Hao Sun

Zhengyu Huang

The following relates generally to accelerated magnetic resonance imaging (MRI) reconstruction. In some embodiments, a MRI machine learning algorithm is trained based on reference MRI data in non-Cartesian k-space. During the training, at each iteration of a plurality of iterations: (i) a non-Cartesian sampling trajectory ω may be optimized under the physical constraints, and/or (ii) an image reconstructor may be jointly iteratively optimized. Examples of the image reconstructor include a convolutional neural network (CNN) denoiser, a model-based deep learning (MoDL) image reconstructor, iterative image reconstructor, a regularizer, and an invertible neural network.

Systems and methods for accelerated magnetic resonance imaging (MRI) reconstruction and sampling

Image security is becoming an increasingly important issue with the progress of deep learning based image manipulations, such as deep image inpainting and deep fakes. There has been considerable work to date on detecting such image manipulations using better and better algorithms, with little attention being paid to the possible role hardware advances may have for more powerful algorithms. This disclosure proposes to use a focal stack camera as a novel secure imaging device for localizing inpainted regions in manipulated images. Applying convolutional neural network (CNN) methods to focal stack images achieves significantly better detection accuracy compared to single image based detection.

Focal stack camera as secure imaging device and image manipulation detection method

A system and method for acquiring image data from an object that includes a plurality of spins is described. A magnetic field is applied to the object to align the spins along a longitudinal axis. A first pulse is applied to the spins for rotating the spins from the longitudinal axis toward a transverse plane. Image data is acquired from the spins during a free precession interval in which the spins precess in the transverse plane. A second pulse is applied to the spins for rotating the spins from the transverse plane to at least substantially along the longitudinal axis. At least one of the first and second pulses is spectrally or spectrally-spatially designed.

Method of MRI imaging using a spectrally designed pulse

A method for iteratively reconstructing an image is provided. The method includes acquiring, with a detector, computed tomography (CT) imaging information. The method also includes generating, with at least one processor, sinogram information from the CT imaging information. Further, the method includes generating, with the at least one processor, image domain information from the CT imaging information. Also, the method includes updating the image using the sinogram information. The method further includes updating the image using the image domain information. Updating the image using the sinogram information and updating the image using the image domain information are performed separately and alternately in an iterative fashion.

Systems and methods for parallel processing of imaging information

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 method is provided for iteratively reconstructing an image of an object. The method includes accessing measurement data associated with the image, and using a simultaneous algorithm to reconstruct the image. Using the simultaneous algorithm to reconstruct the image includes determining a scaling factor that is voxel-dependent, and applying the voxel-dependent scaling factor to a gradient of an objective function to reconstruct the image.

Method and apparatus for iterative reconstruction

Methods provided for forward and back-projection, which are referred to as separable footprint (SF) projectors: exemplified by the SF-TR and SF-TT projectors. These methods approximate the voxel footprint functions as 2D separable functions. Because of the separability of these footprint functions, calculating their integrals over a detector cell is greatly simplified and can be implemented efficiently. In some embodiments, the SF-TR projector uses trapezoid functions in the transaxial direction and rectangular functions in the axial direction. In some embodiments, the SF-TT projector uses trapezoid functions in both the axial and transaxial directions. Simulations and experiments showed that both SF projector methods are more accurate than conventional distance-driven (DD) projectors. Moreover, the SF-TT projector is more accurate than the SF-TR projector for rays associated with large cone angles. In some embodiments, the SF-TR projector has similar computation speed with the DD projector and the SF-TT projector is about two times slower.

Three-dimensional forward and back projection methods

A method is provided for reconstructing an image of an object that includes image elements. The method includes accessing measurement data associated with the image elements, introducing an auxiliary variable to transform an original problem of reconstructing the image to a constrained optimization problem, and solving the constrained optimization problem using a method of multipliers to create a sequence of sub-problems and solve the sequence of sub-problems. Solving the sequence of sub-problems includes reconstructing the image by optimizing a first objective function. The first objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. An inner loop iteratively optimizes a second objective function approximating the first objective function. An outer loop utilizes the solution of the second objective function to optimize the first objective function.

A method and system for controlling image reconstruction in an imaging system are provided. The method includes receiving scan data from an imaging system, and determining regularization parameters for a plurality of portions of an image for reconstructing the image based on the received scan data, wherein the regularization parameters vary for the plurality of portions of the image.

Method and system for controlling image reconstruction

A method of improving a resolution of an image using image reconstruction is provided. The method includes acquiring scan data of an object and forward projecting a current image estimate of the scan data to generate calculated projection data. The method also includes applying a data-fit term and a regularization term to the scan data and the calculated projection data and modifying at least one of the data fit term and the regularization term to accommodate spatio-temporal information to form a reconstructed image from the scan data and the calculated projection data.

Methods and systems for improving spatial and temporal resolution of computed images of moving objects

A method and system for image reconstruction of data acquired by a device such as computed tomography is provided. The method and system use a multi-stage statistical iterative reconstruction techniques to provide a three dimensional representation of the scanned object. In one embodiment, the first stage uses a projection-based reconstruction technique, such as Ordered Subset (OS) to converge on a solution for low frequency portion of the image. A subsequent stage uses a voxel-based reconstruction technique, such as Iterative Coordinate Descent (ICD), to converge on a solution for high frequency portions of the image. Systems and methods for reconstructing images from incomplete or partial projection data is also provided.

Method and system for image reconstruction

A method for statistically reconstructing images from a plurality of transmission measurements having energy diversity and image reconstructor apparatus utilizing the method are provided. A statistical (maximum-likelihood) method for dual-energy X-ray CT accommodates a wide variety of potential system configurations and measurement noise models. Regularized methods (such as penalized-likelihood or Bayesian estimations) are straightforward extensions. One version of the algorithm monotonically decreases the negative log-likelihood cost function each iteration. An ordered-subsets variation of the algorithm provides a fast and practical version. The method and apparatus provide material characterization and quantitatively accurate CT values in a variety of applications. The method and apparatus provide improved noise/dose properties.

Method for statistically reconstructing images from a plurality of transmission measurements having energy diversity and image reconstructor apparatus utilizing the method

A method for statistically reconstructing an X-ray computed tomography image produced by a single X-ray CT scan having a polyenergetic source spectrum and an image reconstructor which utilize a convergent statistical algorithm which explicitly accounts for the polyenergetic source spectrum are provided. First and second related statistical iterative methods for CT reconstruction based on a Poisson statistical model are described. Both methods are accelerated by the use of ordered subsets, which replace sums over the angular index of a sinogram with a series of sums over angular subsets of the sinogram. The first method is generalized to model the more realistic case of polyenergetic computed tomography (CT). The second method eliminates beam hardening artifacts seen when filtered back projection (FBP) is used without post-processing correction. The methods are superior to FBP reconstruction in terms of noise reduction. The method and image reconstructor of the invention are effective in producing corrected images that do not suffer from beam hardening effects.

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Ann Arbor, MI, United States of America

Jeffrey Allen Fessler