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.

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.

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 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.

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: Certain exemplary embodiments can comprise a method that can comprise automatically causing a representation of body tissue to be rendered. The representation of the body tissue can comprise a plurality of voxels located in an interior region of the body tissue. Each of the plurality of voxels can have a negative value of an energy change function.

Abstract: The present invention discloses methods for automatically generating regions of seeding points for fiber tracking in diffusion tensor images. These methods are based on connected region grow. Seeding point selection criteria involving Fractional Anisotropy thresholding and Dominant Eigen Vector similarity are also disclosed.

Abstract: A method of volume rendering two digital images includes providing a volume-rendering computing sub-system, loading a first image volume into a memory of the volume-rendering sub-system, rendering the first image volume, wherein a 2-dimensional image is output into an image buffer and a set of depth values are output into a depth buffer, loading a second image volume into a memory of the volume-rendering sub-system, rendering the second image volume up to the depth values output from the first image volume, wherein values from the rendering of the second image volume are merged with non-zero values of the image buffer, and rendering the remainder of the second image volume to include second image volume points beyond the depth values output from the rendering of the first image volume, wherein values from the rendering of the remainder of the second image volume are merged with non-zero values of the image buffer.

Abstract: Certain exemplary embodiments comprise a method, which can comprise automatically causing a representation of body tissue to be rendered. The body tissue can be tracked via clusters. The clusters each can comprise a predetermined number of particles. Each particle of a particular cluster can be representative of a discreet path associated with the particular cluster in a tensor field.

Abstract: A system and method for prioritized transmission of scalable compressed data are provided, the system including a database server for receiving an interactive prioritization request from a client and prioritizing transmission of the compressed data relative to a bin optimization in response to the interactive prioritization request; and the method including receiving an interactive prioritization request from a client, prioritizing transmission of the compressed data relative to the bin optimization in response to the interactive prioritization request and transmitting the prioritized compressed data to the client.