Abstract: Systems and methods include conversion of a first frame of k-space data acquired using a first initial readout polarity to first hybrid (kx, y)-space data, conversion of a second frame of k-space data acquired using a second initial readout polarity to second hybrid (kx, y)-space data, determination of a relationship between phase difference and y-position based on phase differences between a plurality of pixels located at kx=a of first hybrid (kx, y)-space data and a plurality of pixels at kx=b of second hybrid (kx, y)-space data, where a and b are constants, modification of the second hybrid (kx, y)-space data based on the relationship, conversion of the modified second hybrid (kx, y)-space data to a modified second frame of k-space data, generation of two single-polarity readout k-space frames based on the first frame of k-space data and the modified second frame of k-space data, and correction of a third frame of EPI image data based on the two single-readout polarity k-space frames.

Abstract: Systems and methods include conversion of a first frame of k-space data acquired using a first initial readout polarity to first hybrid (kx, y)-space data, conversion of a second frame of k-space data acquired using a second initial readout polarity to second hybrid (kx, y)-space data, determination of a relationship between phase difference and y-position based on phase differences between a plurality of pixels located at kx=a of first hybrid (kx, y)-space data and a plurality of pixels at kx=b of second hybrid (kx, y)-space data, where a and b are constants, modification of the second hybrid (kx, y)-space data based on the relationship, conversion of the modified second hybrid (kx, y)-space data to a modified second frame of k-space data, generation of two single-polarity readout k-space frames based on the first frame of k-space data and the modified second frame of k-space data, and correction of a third frame of EPI image data based on the two single-readout polarity k-space frames.

Abstract: A method for accelerated segmented magnetic resonance (MR) image data acquisition includes using a plurality of RF pulses to excite one or more slices of an anatomical area of interest according to a predetermined slice acceleration factor. Next, a collapsed image comprising the slices is acquired using a consecutive segment acquisition process. Then, a parallel image reconstruction method is applied to the collapsed image to separate the collapsed image into a plurality of slice images.

Abstract: A system for accelerated segmented magnetic resonance (MR) image data acquisition includes an RF (Radio Frequency) signal generator and a magnetic field gradient generator. The RF signal generator generates RF excitation pulses in anatomy and enabling subsequent acquisition of associated RF echo data. The magnetic field gradient generator generates magnetic field gradients for anatomical volume selection, phase encoding, and readout RF data acquisition in a three dimensional (3D) anatomical volume. The RF signal generator and the magnetic field gradient generator acquire consecutive segments of k-space line data representative of an individual image slice in a gradient echo method by adaptively varying RF excitation pulse flip angle between acquisition of the consecutive segments.

Abstract: A method for accelerated segmented magnetic resonance (MR) image data acquisition includes using a plurality of RF pulses to excite one or more slices of an anatomical area of interest according to a predetermined slice acceleration factor. Next, a collapsed image comprising the slices is acquired using a consecutive segment acquisition process. Then, a parallel image reconstruction method is applied to the collapsed image to separate the collapsed image into a plurality of slice images.

Abstract: Methods and systems determine a correspondence of two sets of data, each data set represents an object. A weighted graph is created from each data set, and a Laplacian is determined for each weighted graph, from which spectral components are determined. The spectral components determine a coordinate of a node in a weighted graph. Nodes of a weighted graph are weighted with a quantified feature related to anode. A coordinate related to a quantified feature of a node is added to the coordinate based on the spectral components. Spectral components related to a weighted graph are reordered to a common ordering. Reordered spectral components related to the first and second data set are aligned and a correspondence is determined. An object may be a brain and a feature may be a sulcal depth. Other objects for which a correspondence may be determined include an electrical network, an image and a social network.

Abstract: An MR imaging system uses the multiple RF coils for acquiring corresponding multiple image data sets of the slice. An image data processor comprises at least one processing device conditioned for, generating a composite MR image data set representing a single image in a single non-iterative operation by performing a weighted combination of luminance representative data of individual corresponding pixels of the multiple image data sets in providing an individual pixel luminance value of the composite MR image data set. The image data processor reduces noise in the composite MR image data set by generating a reduced set of significant components in a predetermined transform domain representation of data representing the composite image to provide a de-noised composite MR image data set. An image generator comprises at least one processing device conditioned for, generating a composite MR image using the de-noised composite MR image data set.

Abstract: A system for accelerated segmented magnetic resonance (MR) image data acquisition includes an RF (Radio Frequency) signal generator and a magnetic field gradient generator. The RF signal generator generates RF excitation pulses in anatomy and enabling subsequent acquisition of associated RF echo data. The magnetic field gradient generator generates magnetic field gradients for anatomical volume selection, phase encoding, and readout RF data acquisition in a three dimensional (3D) anatomical volume. The RF signal generator and the magnetic field gradient generator acquire consecutive segments of k-space line data representative of an individual image slice in a gradient echo method by adaptively varying RF excitation pulse flip angle between acquisition of the consecutive segments.

Abstract: An MR imaging system uses the multiple RF coils for acquiring corresponding multiple image data sets of the slice. An image data processor comprises at least one processing device conditioned for, generating a composite MR image data set representing a single image in a single non-iterative operation by performing a weighted combination of luminance representative data of individual corresponding pixels of the multiple image data sets in providing an individual pixel luminance value of the composite MR image data set. The image data processor reduces noise in the composite MR image data set by generating a reduced set of significant components in a predetermined transform domain representation of data representing the composite image to provide a de-noised composite MR image data set. An image generator comprises at least one processing device conditioned for, generating a composite MR image using the de-noised composite MR image data set.

Abstract: Methods and systems determine a correspondence of two sets of data, each data set represents an object. A weighted graph is created from each data set, and a Laplacian is determined for each weighted graph, from which spectral components are determined. The spectral components determine a coordinate of a node in a weighted graph. Nodes of a weighted graph are weighted with a quantified feature related to anode. A coordinate related to a quantified feature of a node is added to the coordinate based on the spectral components. Spectral components related to a weighted graph are reordered to a common ordering. Reordered spectral components related to the first and second data set are aligned and a correspondence is determined. An object may be a brain and a feature may be a sulcal depth. Other objects for which a correspondence may be determined include an electrical network, an image and a social network.