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.

Abstract: A deployment specification for implementing a requested cloud service is received by a server. A resource pool is queried by the server for available resources required by the deployment specifications. The resource pool includes a plurality of pre-configured resources for implementing one or more cloud services. A first resource required by the deployment specification is determined to be available within the resource pool. First resource metadata associated with the first resource is requested from a database. The resource metadata includes a resource identifier and a resource type of the first resource. The resource metadata associated with the first resource is received from the database. The first resource is deployed from the resource pool according to the deployment specification to implement the requested cloud service.

Abstract: A deployment specification for implementing a requested cloud service is received by a server. A resource pool is queried by the server for available resources required by the deployment specifications. The resource pool includes a plurality of pre-configured resources for implementing one or more cloud services. A first resource required by the deployment specification is determined to be available within the resource pool. First resource metadata associated with the first resource is requested from a database. The resource metadata includes a resource identifier and a resource type of the first resource. The resource metadata associated with the first resource is received from the database. The first resource is deployed from the resource pool according to the deployment specification to implement the requested cloud service.

Abstract: This disclosure relates to hyperpolarized probes for use in magnetic resonance studies of biological systems.

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 stochastic gradient descent.

Abstract: A deployment specification for implementing a requested cloud service is received by a server. A resource pool is queried by the server for available resources required by the deployment specifications. The resource pool includes a plurality of pre-configured resources for implementing one or more cloud services. A first resource required by the deployment specification is determined to be available within the resource pool. First resource metadata associated with the first resource is requested from a database. The resource metadata includes a resource identifier and a resource type of the first resource. The resource metadata associated with the first resource is received from the database. The first resource is deployed from the resource pool according to the deployment specification to implement the requested cloud service.

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 (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.

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.

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.

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.

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.

Abstract: 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.

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.

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.

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.

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.

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.

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.

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.

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.