Abstract: A system and method for combining k-space data acquired on multiple different receiver channels in a multichannel receiver is provided. One or more convolution kernels are used to combine the k-space data. Each convolution kernel is designed as the combination of one or more channel combination kernels and an alias-suppressing kernel. The channel combination kernels are designed to have a smaller sample spacing than the acquired data, and the alias-suppressing kernel is designed to suppress aliasing artifacts in stopbands defined by the sample spacing of the channel combination kernels.

Abstract: In an embodiment, a method includes processing magnetic resonance (MR) data according to a process including applying a density filter to blades of k-space data rotated about a section of k-space. Each blade may include a first set of encode lines weighted in a first signal weighting and a second set of encode lines weighted in a second signal weighting. The density filter may be configured to preferentially weight each blade in the first signal weighting to produce blades of weighted k-space data.

Abstract: A method includes the acts of acquiring a blade of k-space calibration data; acquiring a set of T1-weighted k-space imaging data, the set of T1-weighted k-space imaging data having blades of undersampled k-space data rotated about a section of k-space. Each blade of undersampled k-space data includes first data points having acquired data and second data points that are missing data. The method also includes generating a set of reconstruction weights for the blades of undersampled k-space data using the blade of k-space calibration data; synthesizing k-space data for at least a portion of the second data points using the set of reconstruction weights; and generating a T1-weighted image using the T1-weighted k-space imaging data and the synthesized k-space data.

Abstract: A system and method for combining k-space data acquired on multiple different receiver channels in a multichannel receiver is provided. One or more convolution kernels are used to combine the k-space data. Each convolution kernel is designed as the combination of one or more channel combination kernels and an alias-suppressing kernel. The channel combination kernels are designed to have a smaller sample spacing than the acquired data, and the alias-suppressing kernel is designed to suppress aliasing artifacts in stopbands defined by the sample spacing of the channel combination kernels.

Abstract: The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.

Abstract: In an embodiment, a method includes processing magnetic resonance (MR) data according to a process including applying a density filter to blades of k-space data rotated about a section of k-space. Each blade may include a first set of encode lines weighted in a first signal weighting and a second set of encode lines weighted in a second signal weighting. The density filter may be configured to preferentially weight each blade in the first signal weighting to produce blades of weighted k-space data.

Abstract: The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.

Abstract: In one embodiment, a method for processing magnetic resonance imaging data is provided. The method includes accessing the magnetic resonance imaging data, the data including a plurality of image data sets defining reconstructable images representative of a subject at different points in time. Each data set includes sampled data for sampled phase encoding points but is missing data for unsampled phase encoding points. An adaptive time window is determined for each image data set, and the missing data of at least one of the image data sets is determined based upon the sampled data for the respective data set and sampled data from at least one other data set within the time window for the respective data set.

Abstract: A method includes the acts of acquiring a blade of k-space calibration data; acquiring a set of T1-weighted k-space imaging data, the set of T1-weighted k-space imaging data having blades of undersampled k-space data rotated about a section of k-space. Each blade of undersampled k-space data includes first data points having acquired data and second data points that are missing data. The method also includes generating a set of reconstruction weights for the blades of undersampled k-space data using the blade of k-space calibration data; synthesizing k-space data for at least a portion of the second data points using the set of reconstruction weights; and generating a T1-weighted image using the T1-weighted k-space imaging data and the synthesized k-space data.

Abstract: A method for determining weights (or coefficients) for synthesizing k-space data for autocalibrated parallel imaging (API) combines training data sets (including k-space data such as autocalibrating signals (ACS)) acquired at multiple successive time points. Combining training data sets from multiple successive time points together to determine a set of weights increases the accuracy of the calculated weights. The weights may be applied to k-space data from a single or multiple time points. The method retains the phase information of the individual time point images and may thus be applied, for example, to phase-sensitive multi-point imaging such as chemical species separation studies.

Abstract: A method for generating a magnetic resonance (MR) image includes acquiring calibration data from each of a plurality of RF source coils. Calibration data for a virtual coil is generated based on the calibration data from the plurality of RF source coils and a set of synthesis weights is generated based on the calibration data from the plurality of RF source coils and the calibration data for the virtual coil. Accelerated MR data is acquired from each of the plurality of RF source coils. An image can be reconstructed based on an application of the set of synthesis weights to the accelerated MR data from the plurality of RF source coils.

Abstract: A system and method for magnetic resonance imaging is disclosed, the MRI apparatus comprising a computer programmed to acquire a plurality of 3D MR data sets, each 3D MR data set acquired using a central transmit frequency and a central receive frequency set to an offset frequency value that is distinct for each 3D MR data set, wherein at least a portion of each 3D MR data set is accelerated k-space data, and wherein at least one of the plurality of 3D MR data sets comprises fully-sampled calibration k-space data lines. The computer is also programmed to determine reconstruction weights from the fully-sampled calibration k-space data lines, reconstruct an image for each 3D MR data set using the reconstruction weights from the fully-sampled calibration k-space data lines to synthesize unacquired data, and generate a composite image from the reconstructed images based on the plurality of 3D MR data sets.

Abstract: In one embodiment, a method for processing magnetic resonance imaging data is provided. The method includes accessing the magnetic resonance imaging data, the data including a plurality of image data sets defining reconstructable images representative of a subject at different points in time. Each data set includes sampled data for sampled phase encoding points but is missing data for unsampled phase encoding points. An adaptive time window is determined for each image data set, and the missing data of at least one of the image data sets is determined based upon the sampled data for the respective data set and sampled data from at least one other data set within the time window for the respective data set.

Abstract: The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.

Abstract: The present disclosure is intended to describe embodiments for improving image data acquisition and processing in accelerated dynamic magnetic resonance imaging sequences. One embodiment is described where a method includes an acquisition sequence configured to acquire an undersampled set of magnetic resonance data. The undersampled set of magnetic resonance data has a pseudo-random sampling pattern within a data space acquired at a first time, the pseudo-random sampling pattern being influenced by other pseudo-random sampling patterns within the data space arising from the acquisition of additional undersampled sets of magnetic resonance data at respective times. In some embodiments, the pseudo-random sampling patterns of the undersampled sets of magnetic resonance data interleave to yield a desired sampling pattern.

Abstract: A method for generating a magnetic resonance (MR) image includes acquiring calibration data from each of a plurality of RF source coils. Calibration data for a virtual coil is generated based on the calibration data from the plurality of RF source coils and a set of synthesis weights is generated based on the calibration data from the plurality of RF source coils and the calibration data for the virtual coil. Accelerated MR data is acquired from each of the plurality of RF source coils. An image can be reconstructed based on an application of the set of synthesis weights to the accelerated MR data from the plurality of RF source coils.

Abstract: A system and method for parallel imaging is disclosed that generates linear combination coefficient weights by solving systems of linear equations formulated with correlation values. An MRI apparatus includes a computer programmed to acquire MR data from an imaging volume for a plurality of encoding locations using an array of RF receiver coils. Correlation values are calculated from the MR data. From these calculated correlation values, synthesis weights are generated. An image is then reconstructed based on an application of the synthesis weights to at least a portion of the MR data acquired from the array of RF receiver coils.

Abstract: The present invention provides a system and method for parallel imaging that performs auto-calibrating reconstructions with a 2D (for 2D imaging) or 3D kernel (for 3D imaging) that exploits the computational efficiencies available when operating in certain data “domains” or “spaces”. The reconstruction process of multi-coil data is separated into a “training phase” and an “application phase” in which reconstruction weights are applied to acquired data to synthesize (replace) missing data. The choice of data space, i.e., k-space, hybrid space, or image space, in which each step occurs is independently optimized to reduce total reconstruction time for a given imaging application. As such, the invention retains the image quality benefits of using a 2D k-space kernel without the computational burden of applying a 2D k-space convolution kernel.

Abstract: A system and method for combining parallel imaging and compressed sensing techniques to reconstruct an MR image includes a computer programmed to acquire undersampled MR data for a plurality of k-space locations that is less than an entirety of a k-space grid. The computer is further programmed to synthesize unacquired MR data by way of a parallel imaging technique for a portion of k-space location at which MR data was not acquired and apply a compressed sensing reconstruction technique to generate a reconstructed image from the acquired undersampled MR data and the synthesized unacquired data.

Abstract: A system and method for MR imaging includes the use of a form of autocalibrated parallel imaging. By combining a segmented, rotated acquisition trajectory with autocalibration parallel imaging (API), the system and method can achieve improved motion insensitivity while maintaining the benefits of accelerated acquisition due to parallel imaging. In various embodiments, calibration values from a set of reference data or from another set of imaging data can be used in determining reconstruction weights for a given k-space data set. Thus, separate calibration data need not necessarily be acquired for each set of imaging data.