Patents by Inventor Qiu Wang

Qiu Wang has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20180203085
    Abstract: A method for magnetic resonance (MR) imaging is provided. A first sampling mask is provided for sampling along a first set of parallel lines extending in a first direction in k-space. A second sampling mask is provided for sampling along a second set of parallel lines extending in a second direction in k-space. The second direction is orthogonal to the first direction. A first set of MR k-space data is sampled using an MR scanner, by scanning a subject in the first direction using the first sampling mask. A second set of MR k-space data is sampled using the MR scanner, by scanning the subject in the second direction using the second sampling mask. An MR image is reconstructed from a combined set of MR k-space data including the first set of MR k-space data and the second set of MR k-space data.
    Type: Application
    Filed: January 13, 2017
    Publication date: July 19, 2018
    Inventors: Julia Traechtler, Qiu Wang, Boris Mailhe, Xiao Chen, Marcel Dominik Nickel, Mariappan S. Nadar
  • Publication number: 20180204355
    Abstract: A method for performing Computed Tomography (CT) reconstruction includes acquiring a sparse measurement matrix using a CT scanner and applying a reconstruction process over a number of iterations to reconstruct image data from the sparse measurement matrix. The reconstruction process performed during each respective iteration includes generating a random view subset and determining a portion of the sparse measurement matrix corresponding to the random view subset. The reconstruction process further includes performing a stochastic gradient descent on the portion of the sparse measurement matrix to yield an image, applying a proximal total variation regularization to the image, and adjusting a step size associated with the Acquire CT sparse measurement stochastic gradient descent.
    Type: Application
    Filed: September 2, 2015
    Publication date: July 19, 2018
    Inventors: Boris MAILHE, Johannes FLAKE, Qiu WANG, Mariappan S. NADAR
  • Patent number: 10012717
    Abstract: A method for performing a magnetic resonance image reconstruction with spatially varying coil compression includes using a non-Cartesian acquisition scheme to acquire a multi-coil k-space dataset fully sampled along a fully sampled direction and decoupling the multi-coil k-space dataset along the fully sampled direction to yield a plurality of uncompressed coil data matrices. The plurality of uncompressed coil data matrices are compressed to yield a plurality of virtual coil data matrices which are aligned along the fully sampled direction to yield a plurality of aligned virtual coil data matrices. The aligned virtual coil data matrices are coupled along the fully sampled direction to yield a compressed multi-coil k-space dataset. Intensity values in the plurality of aligned virtual coil data matrices are normalized based on the plurality of uncompressed coil data matrices and an image is reconstructed using the compressed multi-coil k-space dataset.
    Type: Grant
    Filed: April 14, 2015
    Date of Patent: July 3, 2018
    Assignee: Siemens Healthcare GmbH
    Inventors: Qiu Wang, Marcel Dominik Nickel, Boris Mailhe, Mariappan S. Nadar
  • Patent number: 9858689
    Abstract: A computer-implemented method of performing image reconstruction with sequential cycle-spinning includes a computer system acquiring an input signal comprising k-space data using a magnetic resonance imaging (MRI) device and initializing an estimate of a sparse signal associated with the input signal. The computer system selects one or more orthogonal wavelet transforms corresponding to a wavelet family and performs an iterative reconstruction process to update the estimate of the sparse signal over a plurality of iterations. During each iteration, one or more orthogonal wavelet transforms are applied to the estimate of the sparse signal to yield one or more orthogonal domain signals, the estimate of the sparse signal is updated by applying a non-convex shrinkage function to the one or more orthogonal domain signals, and a shift to the orthogonal wavelet transforms. Following the iterative reconstruction process, the computer system generates an image based on the estimate of the sparse signal.
    Type: Grant
    Filed: September 15, 2016
    Date of Patent: January 2, 2018
    Assignee: Siemens Healthcare GmbH
    Inventors: Boris Mailhe, Alexander Ruppel, Qiu Wang, Mariappan S. Nadar
  • Publication number: 20170322793
    Abstract: A method, and associated computer system and computer program product. One or more processors of a computer system receive an upgrade request to upgrade a base operating system (OS) of a virtual machine (VM). In response to receiving the upgrade request, the one or more processors store metadata of the VM into a resource registry. The one or more processors load a new version of the base OS onto the VM. The one or more processors retrieve, from the resource registry, the stored metadata for configuring the VM.
    Type: Application
    Filed: May 5, 2016
    Publication date: November 9, 2017
    Inventors: Chuan Ran, Jian Qiu Wang, Yang Yang
  • Patent number: 9800367
    Abstract: A method improves an anti-eavesdropping (AE)-shelter that emits interference signals that protect communication between legitimate transmitters and legitimate receivers in the AE-shelter. The method includes determining a circular boundary for the AE-shelter; improving the AE-shelter by uniformly placing a number of jammers at the boundary; tuning emitting power of the jammers; and improving a coverage area of the interference signals.
    Type: Grant
    Filed: February 14, 2017
    Date of Patent: October 24, 2017
    Assignee: Macau University of Science and Technology
    Inventors: Hong-Ning Dai, Xuran Li, Qiu Wang, Athanasios V. Vasilakos
  • Patent number: 9746538
    Abstract: Magnetic resonance imaging uses regularized SENSE reconstruction for a reduced field of view, but minimizes folding artifacts. A reference scan is oversampled relative to the reduced field of view. The oversampling provides coil sensitivity information for a region greater than the reduced field of view. The reconstruction of the object for the reduced field of view using the coil sensitivities for the larger region may have fewer folding artifacts.
    Type: Grant
    Filed: August 13, 2014
    Date of Patent: August 29, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Qiu Wang, Derya Gol Gungor, Michael Zenge, Marcel Dominik Nickel, Edgar Müller, Mariappan S. Nadar
  • Publication number: 20170213321
    Abstract: A computer-implemented method for denoising image data includes a computer system receiving an input image comprising noisy image data and denoising the input image using a deep multi-scale network comprising a plurality of multi-scale networks sequentially connected. Each respective multi-scale network performs a denoising process which includes dividing the input image into a plurality of image patches and denoising those image patches over multiple levels of decomposition using a threshold-based denoising process. The threshold-based denoising process denoises each respective image patch using a threshold which is scaled according to an estimation of noise present in the respective image patch. The noising process further comprises the assembly of a denoised image by averaging over the image patches.
    Type: Application
    Filed: June 8, 2016
    Publication date: July 27, 2017
    Inventors: Yevgen Matviychuk, Boris Mailhe, Xiao Chen, Qiu Wang, Mariappan S. Nadar
  • Patent number: 9689947
    Abstract: A computer-implemented method of selecting a Magnetic Resonance Imaging (MRI) sampling strategy includes selecting a base variable-density sampling pattern and determining a scan time associated with the base variable-density sampling pattern. A modified variable-density sampling pattern is created by modifying one or more parameters of the base variable-density sampling pattern to maximize a sampled k-space area without increasing the scan time. Next, a scan is performed on an object of interest using the modified variable-density sampling pattern to obtain a sparse MRI dataset. Then a sparse reconstruction process is applied to the sparse MRI dataset to yield an image of the object of interest.
    Type: Grant
    Filed: October 16, 2014
    Date of Patent: June 27, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Qiu Wang, Michael Zenge, Edgar Mueller, Mariappan S. Nadar
  • Publication number: 20170178318
    Abstract: A method for sparse iterative phase correction for Magnetic Resonance (MR) partial Fourier reconstruction includes acquiring a partial Fourier k-space dataset using an MR scanner and estimating, by a control computer, a coil sensitivity map associated with the MR scanner from fully sampled k-space center. The control computer extracts a symmetrically sampled k-space center dataset from the partial Fourier k-space dataset and determines a low-resolution image based on the symmetrically sampled k-space center dataset and the coil sensitivity map. The control computer also determines phase corresponding to the low-resolution image. An iterative reconstruction process may then be applied to generate an image based on the partial Fourier k-space dataset. This iterative reconstruction process applies a Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with phase correction based on the phase corresponding to the low-resolution image.
    Type: Application
    Filed: December 22, 2015
    Publication date: June 22, 2017
    Inventors: Qiu Wang, Esther Raithel
  • Publication number: 20170168129
    Abstract: A method comprises acquiring navigation data during navigation scans at each of a plurality of points in time. A plurality of magnetic resonance imaging (MM) k-space data corresponding to an imaged object are acquired at the plurality of points in time using Cartesian sampling, the k-space data including at least two spatial dimensions, a time. The respective motion state for each of the k-space data are computed based on the navigation data. At least one image is reconstructed from the plurality MM k-space data using k-space data corresponding to at least two motion states and the same point in time to reconstruct the at least one image.
    Type: Application
    Filed: November 28, 2016
    Publication date: June 15, 2017
    Inventors: Xiao Chen, Mariappan S. Nadar, Marcel Dominik Nickel, Boris Mailhe, Qiu Wang
  • Publication number: 20170160363
    Abstract: A learning-based magnetic resonance fingerprinting (MRF) reconstruction method for reconstructing an MR image of a tissue space in an MR scan subject for a particular MR sequence is disclosed. The method involves using a machine-learning algorithm that has been trained to generate a set of tissue parameters from acquired MR signal evolution without using a dictionary or dictionary matching.
    Type: Application
    Filed: December 8, 2016
    Publication date: June 8, 2017
    Inventors: Xiao Chen, Boris Mailhe, Qiu Wang, Shaohua Kevin Zhou, Yefeng Zheng, Xiaoguang Lu, Puneet Sharma, Benjamin L. Odry, Bogdan Georgescu, Mariappan S. Nadar
  • Publication number: 20170115368
    Abstract: Disclosed herein is a method obtaining a magnetic resonance image of an object, comprising obtaining a first time evolution signal from a magnetic resonance signal from the object; performing a search of a compressed dictionary of magnetic resonance fingerprints to select a magnetic resonance fingerprint representative of the first time evolution signal, wherein the selected magnetic resonance fingerprint is an exact or approximate nearest neighbor match to the first time evolution signal; obtaining a magnetic resonance parameter associated with the selected fingerprint; generating the magnetic resonance image of the object from the obtained magnetic resonance parameter; and performing a second search of the compressed dictionary using the magnetic resonance image.
    Type: Application
    Filed: October 24, 2016
    Publication date: April 27, 2017
    Inventors: Xiao Chen, Mariappan S. Nadar, Christopher Cline, Boris Mailhe, Qiu Wang
  • Patent number: 9633455
    Abstract: A method of generating Magnetic Resonance (MR) parameter maps includes creating one or more parameter maps, each respective parameter map comprising initial parameter values associated with one of a plurality of MR parameters. A dynamical update process is performed over a plurality of time points. The dynamical update process performed at each respective time point includes applying a randomized pulse sequence to subject using an MR scanner to acquire a k-space dataset. This randomized pulse sequence is configured to excite a distinct range of values associated with the plurality of MR parameters. The dynamical update process further includes applying a reconstruction process to the k-space dataset to generate an image and using a tracking process to update the one or more parameter maps based on the randomized pulse sequence and the image.
    Type: Grant
    Filed: December 10, 2015
    Date of Patent: April 25, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Boris Mailhe, Mariappan Nadar, Xiao Chen, Qiu Wang
  • Patent number: 9542761
    Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.
    Type: Grant
    Filed: February 25, 2015
    Date of Patent: January 10, 2017
    Assignee: Siemens Healthcare GmbH
    Inventors: Jin Tan, Boris Mailhe, Qiu Wang, Mariappan S. Nadar
  • Patent number: 9482732
    Abstract: A method of image reconstruction for a magnetic resonance imaging (MRI) system includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, iteratively reconstructing preliminary dynamic images for the undersampled region from the k-space scan data via optimization of a first instance of a minimization problem, the minimization problem including a regularization term weighted by a weighting parameter array, generating a motion determination indicative of an extent to which each location of the undersampled region exhibits motion over time based on the preliminary dynamic images, and iteratively reconstructing motion-compensated dynamic images for the region from the k-space scan data via optimization of a second instance of the minimization problem, the second instance having the weighting parameter array altered as a function of the motion determination.
    Type: Grant
    Filed: October 29, 2013
    Date of Patent: November 1, 2016
    Inventors: Nicolas Chesneau, Nirmal Janardhanan, Jun Liu, Mariappan S. Nadar, Qiu Wang, Zhili Yang
  • Publication number: 20160306019
    Abstract: A method for performing a magnetic resonance image reconstruction with spatially varying coil compression includes using a non-Cartesian acquisition scheme to acquire a multi-coil k-space dataset fully sampled along a fully sampled direction and decoupling the multi-coil k-space dataset along the fully sampled direction to yield a plurality of uncompressed coil data matrices. The plurality of uncompressed coil data matrices are compressed to yield a plurality of virtual coil data matrices which are aligned along the fully sampled direction to yield a plurality of aligned virtual coil data matrices. The aligned virtual coil data matrices are coupled along the fully sampled direction to yield a compressed multi-coil k-space dataset. Intensity values in the plurality of aligned virtual coil data matrices are normalized based on the plurality of uncompressed coil data matrices and an image is reconstructed using the compressed multi-coil k-space dataset.
    Type: Application
    Filed: April 14, 2015
    Publication date: October 20, 2016
    Inventors: Qiu Wang, Marcel Dominik Nickel, Boris Mailhe, Mariappan S. Nadar
  • Patent number: 9453895
    Abstract: A computer-implemented method for learning a tight frame includes acquiring undersampled k-space data over a time period using an interleaved process. An average of the undersampled k-space data is determined and a reference image is generated based on the average of the undersampled k-space data. Next, a tight frame operator is determined based on the reference image. Then, a reconstructed image data is generated from the undersampled k-space data via a sparse reconstruction which utilizes the tight frame operator.
    Type: Grant
    Filed: September 16, 2013
    Date of Patent: September 27, 2016
    Assignee: Siemens Aktiengesellschaft
    Inventors: Jun Liu, Qiu Wang, Mariappan Nadar, Michael Zenge, Edgar Mueller
  • Publication number: 20160247299
    Abstract: A method for reconstructing magnetic resonance imaging data includes acquiring a measurement dataset using a magnetic resonance imaging device and determining an estimated image dataset based on the measurement dataset. An iterative reconstruction process is performed to refine the estimated image dataset. Each iteration of the iterative reconstruction process comprises: updating the measurement dataset and a sparse coefficient dataset based on the estimated image dataset and a plurality of belief propagation terms, incorporating a noise prior dataset into the measurement dataset, incorporating a sparsity prior dataset into the sparse coefficient dataset, updating the plurality of belief propagation terms based on the measurement dataset and the sparsity prior dataset, and updating the estimated image dataset based on the plurality of belief propagation terms. A reconstructed image and confidence map are generated using the estimated image dataset.
    Type: Application
    Filed: February 25, 2015
    Publication date: August 25, 2016
    Inventors: Jin Tan, Boris Mailhe, Qiu Wang, Mariappan S. Nadar
  • Patent number: 9418318
    Abstract: A computer-implemented method of detecting a foreground data in an image sequence using a dual sparse model framework includes creating an image matrix based on a continuous image sequence and initializing three matrices: a background matrix, a foreground matrix, and a coefficient matrix. Next, a subspace recovery process is performed over multiple iterations. This process includes updating the background matrix based on the image matrix and the foreground matrix; minimizing an L?1 norm of the coefficient matrix using a first linearized soft-thresholding process; and minimizing an L?1 norm of the foreground matrix using a second linearized soft-thresholding process. Then, background images and foreground images are generated based on the background and foreground matrices, respectively.
    Type: Grant
    Filed: August 26, 2014
    Date of Patent: August 16, 2016
    Assignees: Siemens Aktiengesellschaft, North Carolina State University
    Inventors: Mariappan S. Nadar, Xiao Bian, Qiu Wang, Hasan Ertan Cetingul, Hamid Krim, Lucas Plaetevoet