Abstract: A method for producing a streak-suppressed MR image of a subject includes (i) generating an interference correlation matrix from M coil images, (ii) producing eigenvectors of the interference correlation matrix, and (iii) determining, from the subspace-eigenvectors, a projection matrix of the interference null space. The subspace-eigenvectors include a plurality of subspace-eigenvectors that span an interference subspace and a plurality of null-space-eigenvectors that span an interference null space.

Abstract: A method for producing a streak-suppressed MR image of a subject includes (i) generating an interference correlation matrix from M coil images, (ii) producing eigenvectors of the interference correlation matrix, and (iii) determining, from the subspace-eigenvectors, a projection matrix of the interference null space. The subspace-eigenvectors include a plurality of subspace-eigenvectors that span an interference subspace and a plurality of null-space-eigenvectors that span an interference null space.

Abstract: A method for producing a streak-suppressed magnetic resonance (MR) image of a subject includes generating an interference covariance matrix {circumflex over (?)}R front N coil images Ij (x,y), j={1, 2, . . . , N}, each of the N coil images Ij (x,y) corresponding to MR signals detected by a respective one of a phased array of N coils of an MRI scanner. The MR signals originate in voxels of the subject corresponding to an artifact-corrupted region of a coil image. Coordinates (x,y) correspond to a location within a cross-sectional plane of the subject. The method also includes, for subject-regions of cross-sectional plane centered at a respective location (x,y), determining a coil weight vector W (x,y) from {circumflex over (?)}R. The method also includes generating the streak-suppressed MR image as a weighted sum of the coil images Ij (x,y), each weight of the weighted sum being Wj* (x,y), a jth element of a complex conjugate of coil weight vector W (x,y).

Abstract: A system and method for processing highly undersampled multi-echo spin-echo data by linearizing the slice-resolved extended phase graph model generates highly accurate T2 maps with indirect echo compensation. Principal components are used to linearize the signal model to estimate the T2 decay curves which can be fitted to the slice-resolved model for T2 estimation. In another example of image processing for highly undersampled data, a joint bi-exponential fitting process can compensate for image variations within a voxel and thus provide partial voxel compensation to produce more accurate T2 maps.

Abstract: A system and method for processing highly undersampled multi-echo spin-echo data by linearizing the slice-resolved extended phase graph model generates highly accurate T2 maps with indirect echo compensation. Principal components are used to linearize the signal model to estimate the T2 decay curves which can be fitted to the slice-resolved model for T2 estimation. In another example of image processing for highly undersampled data, a joint bi-exponential fitting process can compensate for image variations within a voxel and thus provide partial voxel compensation to produce more accurate T2 maps.

Abstract: A system and method for processing highly undersampled multi-echo spin-echo data by linearizing the slice-resolved extended phase graph model generates highly accurate T2 maps with indirect echo compensation. Principal components are used to linearize the signal model to estimate the T2 decay curves which can be fitted to the slice-resolved model for T2 estimation. In another example of image processing for highly undersampled data, a joint bi-exponential fitting process can compensate for image variations within a voxel and thus provide partial voxel compensation to produce more accurate T2 maps.

Abstract: Described here are systems and methods for estimating phase measurements obtained using a magnetic resonance imaging (MRI) system such that phase ambiguities in the measurements are significantly mitigated. Echo time spacings are determined by optimizing phase ambiguity functions associated with the echo time spacings. Data is then acquired using a multi-echo pulse sequence that utilizes the determined echo spacings. Phase measurements are then estimated and images are reconstructed using a reconstruction technique that disambiguates the phase ambiguities in the phase measurements.

Abstract: A system and method for determining a magnetic field map when using a magnetic resonance imaging (MRI) system to acquire images from a region of interest (ROI) of a subject. The method includes selecting a pulse sequence to elicit a plurality of echoes from the subject as medical imaging data from the subject. The method also includes optimizing an echo time for a dynamic range of interest during the pulse sequence (SBmax), a minimum signal-to-noise ratio (SNR0) in the medical imaging data, and minimum T2* value in the ROI. The method further includes generating a magnetic field map estimation using the optimized echo times.

Abstract: Described here are systems and methods for estimating phase measurements obtained using a magnetic resonance imaging (MRI) system such that phase ambiguities in the measurements are significantly mitigated. Echo time spacings are determined by optimizing phase ambiguity functions associated with the echo time spacings. Data is then acquired using a multi-echo pulse sequence that utilizes the determined echo spacings. Phase measurements are then estimated and images are reconstructed using a reconstruction technique that disambiguates the phase ambiguities in the phase measurements.

Abstract: A system and method for processing highly undersampled multi-echo spin-echo data by linearizing the slice-resolved extended phase graph model generates highly accurate T2 maps with indirect echo compensation. Principal components are used to linearize the signal model to estimate the T2 decay curves which can be fitted to the slice-resolved model for T2 estimation. In another example of image processing for highly undersampled data, a joint bi-exponential fitting process can compensate for image variations within a voxel and thus provide partial voxel compensation to produce more accurate T2 maps.

Abstract: A system and method for determining a magnetic field map when using a magnetic resonance imaging (MRI) system to acquire images from a region of interest (ROI) of a subject. The method includes selecting a pulse sequence to elicit a plurality of echoes from the subject as medical imaging data from the subject. The method also includes optimizing an echo time for a dynamic range of interest during the pulse sequence (SBmax), a minimum signal-to-noise ratio (SNR0) in the medical imaging data, and minimum T2* value in the ROI. The method further includes generating a magnetic field map estimation using the optimized echo times.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for remotely visualizing an image on a client includes the steps of rendering a 2D image from image data on a server, applying a 2D wavelet transform to the 2D image on the server to generate a plurality of sub-bands, identifying code blocks of the sub-bands that correspond to a region of interest in the 2D image on the server, compressing a number of bit planes of each code block using one of a plurality of coding techniques on the server based on the number of bit planes to generate compressed codes, sending the compressed codes from the server to the client, and visualizing a new 2D image on the client using the received compressed codes.

Abstract: 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.

Abstract: Scalable image compression is exploited to facilitate the creative process in the post-production of motion pictures. Specifically, digital intermediate (DI) processing of motion pictures is enabled by dynamically rendering proxies 126 (steps 122, 124, 128) in response to client requests (steps 116, 118). A DI application is designed to enhance the efficiency of post-processing and the quality of the work product of the editors, colorists and other creative people. The DI application also provides a method for efficiently formatting the product for film, digital cinema, DVD and other video applications.