| Patent application number | Description | Published |
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| 20090129690 | LIFTING-BASED VIEW COMPENSATED COMPRESSION AND REMOTE VISUALIZATION OF VOLUME RENDERED IMAGES - A method for compressing 2D images includes determining a depth map for each of a plurality of sequential 2D images of a 3D volumetric image, determining coordinate transformations the 2D images based on the depth maps and a geometric relationship between the 3D volumetric image and each of the 2D image, performing a lifting-based view compensated wavelet transform on the 2D images using the coordinate transformations to generate a plurality of wavelet coefficients and compressing the wavelet coefficients and depth maps to generate a compressed representation of the 2D images. | 05-21-2009 |
| 20110116724 | Method for Exploiting Structure in Sparse Domain for Magnetic Resonance Image Reconstruction - A method for constructing an image includes acquiring image data in a first domain. The acquired image data is transformed from the first domain into a second domain in which the acquired image data exhibits a high degree of sparsity. An initial set of transform coefficients is approximated for transforming the image data from the second domain into a third domain in which the image may be displayed. The approximated initial set of transform coefficients is updated based on a weighing of where substantial transform coefficients are likely to be located relative to the initial set of transform coefficients. An image is constructed in the third domain based on the updated set of transform coefficients. The constructed image is displayed. | 05-19-2011 |
| 20120008843 | Method for reconstruction of magnetic resonance images - A method for constructing an image includes acquiring image data in a sensing domain, transforming the acquired image data into a sparse domain, approximating sparse coefficients based on the transformed acquired image data, performing a Bayes Least Squares estimation on the sparse coefficients based on Gaussian Scale Mixtures Model to generate weights, approximating updated sparse coefficients by using the weights and acquired image, constructing an image based on the updated sparse coefficients, and displaying the constructed image. | 01-12-2012 |
| 20120008844 | SYSTEM AND METHOD FOR MOTION-COMPENSATED COMPRESSED SENSING FOR DYNAMIC IMAGING - A method for reconstructing a digital image from a set of measurements includes providing a previous image frame in a time series of measurements of an image signal and a current image frame in the time series, calculating an estimated motion vector for a spatial point and current time point between the previous and current image frames, calculating a motion compensated current image frame from the previous image frame, estimating a known support set of a sparse signal estimate of the motion compensated current image frame where the support set comprises indices of non-zero elements of the sparse signal estimate, calculating a sparse signal corresponding to the current image frame whose support contains a smallest number of new additions to the known support set while satisfying a data consistency constraint, and correcting the motion compensated current image frame image frame from the sparse signal. | 01-12-2012 |
| 20120148128 | UNIFYING RECONSTRUCTION AND MOTION ESTIMATION IN FIRST PASS CARDIAC PERFUSION IMAGING - Methods and a system to unify reconstruction and motion estimation steps in first pass cardiac perfusion MRI include a global objective function that meets data consistency, spatial smoothness, motion and contrast dynamics constraints. The global objective decomposed into simpler sub-problems which include low pass filtering of a deformed object, TV shrinkage, analytical Fourier replacement and an l | 06-14-2012 |
| 20120148129 | Prior Enhanced Compressed Sensing (PRINCE-CS) Reconstruction for Dynamic 2D-Radial Cardiac MRI - A reconstructed image is rendered from a set of MRI data by first estimating an image with an area which does not contain artifacts or has an artifact with a relative small magnitude. Corresponding data elements in the estimated image and a trial image are processed, for instance by multiplication, to generate an intermediate data set. The intermediate data set is transformed and minimized iteratively to generate a reconstructed image that is free or substantially free of artifacts. In one embodiment a Karhunen-Loeve Transform (KLT) is used. A sparsifying transformation may be applied to generate the reconstructed image. The sparsifying transformation may be also not be applied. | 06-14-2012 |
| 20130113816 | VISUALIZING BRAIN NETWORK CONNECTIVITY - A method for visualizing brain connectivity includes receiving image data including molecular diffusion of brain tissue, constructing a tree data structure from the image data, wherein the tree data structure comprises a plurality of network nodes, wherein each network node is connected to a root of the tree data structure, rendering a ring of a radial layout depicting the tree data structure, wherein a plurality of vertices may be traversed from the top to the bottom, duplicating at least one control point for spline edges sharing a common ancestor, and bundling spline edges by applying a global strength parameter β. | 05-09-2013 |
| 20130121550 | Non-Contrast-Enhanced 4D MRA Using Compressed Sensing Reconstruction - A reconstructed image is rendered of a patient by a processor from a set of undersampled MRI data by first subtracting two repetitions of the acquired data in k-space to create a third dataset. The processor reconstructs the image by minimizing an objective function under a constraint related to the third dataset, wherein the objective function includes applying a Karhunen-Loeve Transform (KLT) to a temporal dimension of data. The objective function under the constraint is expressed as arg min | 05-16-2013 |
| 20130121554 | IMAGE RECONSTRUCTION USING REDUNDANT HAAR WAVELETS - A method for image reconstruction includes receiving under-sampled k-space data, determining a data fidelity term of a first image of the under-sampled k-space data in view of a second image of the under-sampled k-space data, wherein a time component separated the first image and the second image, determining a spatial penalization on redundant Haar wavelet coefficients of the first image in view of the second image, and optimizing the first image according the data fidelity term and the spatial penalization, wherein the spatial penalization selectively penalizes temporal coefficients and an optimized image of the first image is output. | 05-16-2013 |
| 20130289912 | EIGEN-VECTOR APPROACH FOR COIL SENSITIVITY MAPS ESTIMATION - A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing ( | 10-31-2013 |
| 20130320974 | EFFICIENT REDUNDANT HAAR MINIMIZATION FOR PARALLEL MRI RECONSTRUCTION - A method for parallel magnetic resonance imaging (MRI) reconstruction of digital images includes providing a set of acquired k-space MR image data v, a redundant Haar wavelet matrix W satisfying W | 12-05-2013 |
| 20140037228 | ZERO COMMUNICATION BLOCK PARTITIONING - A computer-implemented method for calculating a multi-dimensional wavelet transform in an image processing system comprising a plurality of computation units includes receiving multi-dimensional image data. An overlap value corresponding to a number of non-zero filter coefficients associated with the multi-dimensional wavelet transform is identified. Then the multi-dimensional image data is divided into a plurality of multi-dimensional arrays, wherein the multi-dimensional arrays overlap in each dimension by a number of pixels equal to the overlap value. A multi-dimensional wavelet transform is calculated for each multi-dimensional array, in parallel, across the plurality of computation units. | 02-06-2014 |
| 20140085318 | Multi-GPU FISTA Implementation for MR Reconstruction with Non-Uniform K-Space Sampling - A system for performing image reconstruction in a multi-threaded computing environment includes one or more central processing units executing a plurality of k-space components and a plurality of graphic processing units executing a reconstruction component. The k-space components executing on the central processing units include a k-space sample data component operating in a first thread and configured to receive k-space sample data from a first file interface; a k-space sample coordinate data component operating in a second thread and configured to receive k-space sample coordinate data from a second file interface; and a k-space sample weight data component operating in a third thread and configured to retrieve k-space sample weight data from a third file interface. The reconstruction component is configured to receive one or more k-space input data buffers comprising the k-space sample data, the k-space sample coordinate data, and the k-space sample weight data from the one or more central processing units, and reconstruct an image based on the input data buffers using an iterative reconstruction algorithm. | 03-27-2014 |
| 20140086469 | MRI RECONSTRUCTION WITH INCOHERENT SAMPLING AND REDUNDANT HAAR WAVELETS - A method of image reconstruction for a magnetic resonance imaging (MRI) system having a plurality of coils includes obtaining k-space scan data captured by the MRI system, the k-space scan data being representative of an undersampled region over time, determining a respective coil sensitivity profile for the region for each coil of the plurality of coils, and iteratively reconstructing dynamic images for the region from the k-space scan data via an optimization of a minimization problem. The minimization problem is based on the determined coil sensitivity profiles and redundant Haar wavelet transforms of the dynamic images. | 03-27-2014 |
| 20140088899 | EIGEN-VECTOR APPROACH FOR COIL SENSITIVITY MAPS ESTIMATION - 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 V | 03-27-2014 |
| 20140126796 | MRI RECONSTRUCTION WITH MOTION-DEPENDENT REGULARIZATION - 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. | 05-08-2014 |
| 20140133724 | MULTI-STAGE MAGNETIC RESONANCE RECONSTRUCTION FOR PARALLEL IMAGING APPLICATIONS - A computer-implemented method for reconstruction of a magnetic resonance image includes acquiring a first incomplete k-space data set comprising a plurality of first k-space lines spaced according to an acceleration factor and one or more calibration lines. A parallel imaging reconstruction technique is applied to the first incomplete k-space data to determine a plurality of second k-space lines not included in the first incomplete k-space data set, thereby yielding a second incomplete k-space data set. Then, the parallel imaging reconstruction technique is applied to the second incomplete k-space data to determine a plurality of third k-space lines not included in the second incomplete k-space data, thereby yielding a complete k-space data set. | 05-15-2014 |
| 20150054505 | REFERENCE OVERSAMPLING IN SENSE-TYPE MAGNETIC RESONANCE RECONSTRUCTION - 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. | 02-26-2015 |
| 20150063687 | ROBUST SUBSPACE RECOVERY VIA DUAL SPARSITY PURSUIT - 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. | 03-05-2015 |
| 20150086131 | SINGLE-IMAGE SUPER RESOLUTION AND DENOISING USING MULTIPLE WAVELET DOMAIN SPARSITY - A computer-implemented method of enhancing images includes receiving one or more observed images, identifying wavelet bases, and determining a downsampling operator. A noise variance value is estimated and used to select a tuning parameter. A blurring kernel is estimated based on one or more system calibration parameter and used to determine a low-pass blurring filter operator. A cost function is created which generates one or more denoised super-resolution images based on the observed images and the plurality of wavelet bases. The cost function may include, for example, a sparsity inducing norm applied to the plurality of wavelet bases (with the tuning parameter applied to the sparsity inducing norm) and a constraint requiring the one or more denoised super-resolution images to be equal to a result of applying the low-pass blurring filter operator and the downsampling operator to the one or more denoised super-resolution images. The one or more denoised super-resolution images are generated by minimizing this cost function. | 03-26-2015 |
| 20150091563 | MRI 3D CINE IMAGING BASED ON INTERSECTING SOURCE AND ANCHOR SLICE DATA - A method of magnetic resonance (MR) imaging of a volume undergoing repetitive motion includes obtaining source slice data indicative of a plurality of source slices during the repetitive motion, and obtaining anchor slice data indicative of an anchor slice during the repetitive motion. The anchor slice intersects the plurality of source slices. The source slice data and the anchor slice data are reconstructed. A three-dimensional image assembly procedure is implemented to generate, for each phase of the repetitive motion, volume data based on a respective subset of the reconstructed source slice data. For each phase of the repetitive motion, the respective subset of slices is selected based on a correlation of the source slice data and the anchor slice data along an intersection between each source slice and the anchor slice. The source slice data of the selected subset is corrected for misalignment with the anchor slice data. | 04-02-2015 |
