Abstract: 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? from a singular value decomposition of A corresponding to ? leading singular values, calculating P=V?V?H, calculating a matrix that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ? ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ? ? … ? ? ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .

Abstract: 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 WTW=I, wherein I is an identity matrix, a regularization parameter ??0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz½?z?(pi+zi)?22+??z?1 for 0?i?k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)?yi, updating zi+1=arg minz½?z?(qi+zi)?22+g(z), wherein g ? ( z ) = { 0 , z = WW T ? z , + ? , otherwise ; and updating qi+1=(qi+yi)?zi?l, wherein x=WTz is a solution of min x ? 1 2 ? ? Wx - Wv ? 2 2 + ? ? ? Wx ? 1 that specifies a reconstruction of the MR image.

Abstract: 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 ?.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for reconstructing parallel magnetic resonance images includes providing a set of acquired k-space MR image data y, and finding a target MR image x that minimizes ½?Fv?y?22+??z?1 where v=Sx and z=Wx where S is a diagonal matrix containing sensitivity maps of coil elements in an MR receiver array, F is an FFT matrix, W is a redundant Haar wavelet matrix, and ??0 is a regularization parameter, by updating x k + 1 = ( ? 1 ? I + ? 3 ? S H ? S ) - 1 ? [ ? 1 ? W H ? ( z k - b z k ) + ? 3 ? S H ? ( v k - b v k ) ] , ? z k + 1 = soft ? ( Wx k + 1 + b z k , 1 ? 1 ) ? ? where soft ? ( x , T ) = { x + T if ? ? x ? - T , 0 if ? ? ? x ? ? T , x - T if ? ? x ? T , ? ? and ? ? v k + 1 = ( F H ? F + ? 3 ? I ) - 1 ? [ F H ? y + ? 3 ? ( Sx k + 1 + b v k ) ] , where k is an iteration counter,

Abstract: An exemplary embodiment of the present invention includes a method of registering images. The method includes: for each image, determining an optimum intensity threshold set from a plurality of intensity threshold sets that maximizes a variance between classes of each set, segmenting each image using the corresponding determined optimum intensity threshold set, generating mutual information from a joint histogram of at least two of the segmented images, and registering the at least two images using the mutual information. The joint histogram may be generated using a geometry shader of a graphical processing unit.

Abstract: 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.

Abstract: 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.

Abstract: 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? from a singular value decomposition of A corresponding to ? leading singular values, calculating P=V?V?H, calculating a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M = ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ? ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ? ? … ? ? ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .

Abstract: 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.

Abstract: 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 WTW=I, wherein I is an identity matrix, a regularization parameter ??0, and a counter limit k, initializing a variable z0=Wv, and intermediate quantities p0=q0=0, calculating yi=arg minz 1/2?z?(pi+zi)?22+??z?1 for 0?i?k, wherein z denotes values of an MR image sought to be reconstructed, updating pi+1=(pi+zi)?yi, updating zi+1=arg minz 1/2?z?(qi+zi)?22+g(z), wherein g ? ( z ) = { 0 , z = WW T ? z , + ? , otherwise ; and updating qi+1=(qi+yi)?zi?1, wherein x=WTz is a solution of min x ? 1 2 ? ? Wx - Wv ? 2 2 + ? ? ? Wx ? 1 that specifies a reconstruction of the MR image.

Abstract: A method for estimating a coil sensitivity map for a magnetic resonance (MR) image includes providing (61) a matrix A of sliding blocks of a 2D image of coil calibration data, calculating (62) a left singular matrix V? from a singular value decomposition of A corresponding to ? leading singular values, calculating (63) P=V?V?H, calculating (64) a matrix S that is an inverse Fourier transform of a zero-padded matrix P, and solving (65) MHcr=(Sr)Hcr for cr, where cr is a vector of coil sensitivity maps for all coils at spatial location r, and M ? ( ( 1 1 … 1 0 0 … 0 … … … 0 0 … 0 ) ? ( 0 0 … 0 1 1 … 1 … … … 0 0 … 0 ) ? ? … ? ? ( 0 0 … 0 0 0 … 0 … … … 1 1 … 1 ) ) .

Abstract: A method for reconstructing parallel magnetic resonance images includes providing a set of acquired k-space MR image data y, and finding a target MR image x that minimizes ½?Fv?y?22+??z?1 where v=Sx and z=Wx where S is a diagonal matrix containing sensitivity maps of coil elements in an MR receiver array, F is an FFT matrix, W is a redundant Haar wavelet matrix, and ??0 is a regularization parameter, by updating x k + 1 = ( ? 1 ? I + ? 3 ? S H ? S ) - 1 ? [ ? 1 ? W H ? ( z k - b z k ) + ? 3 ? S H ? ( v k - b v k ) ] , ? z k + 1 = soft ? ( Wx k + 1 ? b z k , 1 ? 1 ) ? ? where soft ? ( x , T ) = { x + T if ? ? x ? - T , 0 if ? ? ? x ? ? T , x - T if ? ? x ? T , ? ? and ? ? v k + 1 = ( F H ? F + ? 3 ? I ) - 1 ? [ F H ? y + ? 3 ? ( Sx k + 1 + b v k ) ] , where k is an iteration counter,

Abstract: 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.

Abstract: 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 ?.

Abstract: An exemplary embodiment of the present invention includes a method of registering images. The method includes: for each image, determining an optimum intensity threshold set from a plurality of intensity threshold sets that maximizes a variance between classes of each set, segmenting each image using the corresponding determined optimum intensity threshold set, generating mutual information from a joint histogram of at least two of the segmented images, and registering the at least two images using the mutual information. The joint histogram may be generated using a geometry shader of a graphical processing unit.