Siemens Healthcare Gmbh

Siemens Aktiengesellschaft

Other

North Carolina State University

Qiu Wang

Mariappan S Nadar

Boris Mailhe

Michael Zenge

Xiao Chen

Marcel Dominik Nickel

Edgar Mueller

Jun Liu

Nirmal Janardhanan

Zhili Yang

Alban Lefebvre

Johannes Flake

Bogdan Georgescu

Puneet Sharma

Shaohua Kevin Zhou

Yefeng Zheng

Benjamin L Odry

Xiaoguang Lu

Ti-chiun Chang

Michaela Schmidt

Hui Xue

Hasan Ertan Cetingul

Esther Raithel

Edgar Müller

Axel Loewe

Laszlo Lazar

Aurélien Stalder

Hamid Krim

Xiao Bian

Yuanhsi Chen

Yevgen Matviychuk

Julia Traechtler

Christopher Cline

Alexander Ruppel

Derya Gol Gungor

Jin Tan

Nicolas Chesneau

Lucas Plaetevoet

Ozgur Balkan

A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing a matrix A of sliding blocks of a 3D image of coil calibration data, calculating a left singular matrix Vfrom a singular value decomposition of A corresponding to &#x3c4; leading singular values, calculating P=VV, calculating a matrix that is an inverse Fourier transform of a zero-padded matrix P, and solving Mc=(S)cfor c, where cis a vector of coil sensitivity maps for all coils at spatial location r, and 

Eigen-vector approach for coil sensitivity maps estimation

A method for acquiring a three-dimensional image volume using a magnetic resonance imaging device includes performing a multi-slice or multi-slab acquisition process to acquire a plurality of slices or three-dimensional slabs corresponding to an imaged object. Each respective slice or three-dimensional slab included in the plurality of slices or three-dimensional slabs comprises k-space data. An iterative compressed-sensing reconstruction process is applied to jointly reconstruct the plurality of three-dimensional slabs as a single consistent volume. The iterative compressed-sensing reconstruction process solves a cost function comprising a summation of individual data fidelity terms corresponding to the plurality of three-dimensional slabs.

Compressed sensing reconstruction for multi-slice and multi-slab acquisitions

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.

System and method for learning based magnetic resonance fingerprinting

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 stochastic gradient descent.

Fast sparse computed tomography image reconstruction from few views

A method comprises acquiring navigation data during navigation scans at each of a plurality of points in time. A plurality of magnetic resonance imaging (MRI) 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 MRI 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.

System and method for motion resolved MRI

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.

Iterative magnetic resonance fingerprinting reconstruction

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.

Magnetic resonance imaging with undersampling

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.

Sparse iterative phase correction for MR partial fourier reconstruction

A computer-implemented method for reconstructing magnetic resonance images using a parallel computing platform comprising a host unit and a graphical processing device includes receiving a plurality of coil data sets from a magnetic resonance imaging system, each respective coil data set comprising scanner data and a coil sensitivity map associated with a distinct coil included in the magnetic resonance imaging system. An iterative compressed-sensing reconstruction process is applied to reconstruct an image based on the plurality of coil data sets. Each iteration of the iterative compressed-sensing reconstruction process comprises: individually transferring the plurality of coil data sets from the host unit to the graphical processing device using a plurality of asynchronous data streams, overlapping with transfer of the plurality of coil data sets, optimizing a plurality of data fidelity values on the graphical processing device, each respective data fidelity value corresponding to a distinct coil data set, and computing an estimated image on the graphical processing device based on the plurality of data fidelity values.

Memory efficiency of parallel magnetic resonance imaging reconstruction

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.

Deep unfolding algorithm for efficient image denoising under varying noise conditions

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.

Spatially varying coil compression in magnetic resonance image reconstruction

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.

Fast and memory efficient redundant wavelet regularization with sequential cycle spinning

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.

Reference oversampling in sense-type magnetic resonance reconstruction

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.

Sampling strategies for sparse magnetic resonance image reconstruction

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.

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Princeton, NJ, United States of America

Qiu Wang