Abstract: Methods and systems in a parallel magnetic resonance imaging (MRI) system utilize sensitivity-encoded MRI data acquired from multiple receiver coils together with spatially dependent receiver coil sensitivities to generate MRI images. The acquired MRI data forms a reduced MRI data set that is undersampled in at least a phase-encoding direction in a frequency domain. The acquired MRI data and auto-calibration signal data are used to determine reconstruction coefficients for each receiver coil using a weighted or a robust least squares method. The reconstruction coefficients vary spatially with respect to at least the spatial coordinate that is orthogonal to the undersampled, phase-encoding direction(s) (e.g., a frequency encoding direction).

Abstract: Methods are provided for recovering component signals or estimates of component signals from combined signals of multiple tracers in the context of imaging multiple PET tracers, a single tracer injected repeatedly, or a combination of tracers using multiple-timepoint or dynamic scanning, where the tracer administrations are simultaneous or staggered in time such that some or all of the PET timeframes, images, data, and/or datasets contain overlapping signals from more than one of the tracer administrations.

Abstract: A method, system, and computer-readable medium are provided which perform reconstruction of an image from undersampled, multi-image k-space data. A first undersampled image dataset and a second undersampled image dataset defined in a first dimension are received. The first undersampled image dataset and the second undersampled image dataset define a multi-image dimension. An ordering for a plurality of pixels of the first dimension in the multi-image dimension is defined. The first undersampled image dataset and the second undersampled image dataset in the multi-image dimension are sorted based on the defined ordering. A first constraint is defined in the first dimension using the unsorted first and second undersampled image datasets. A second constraint is defined in the multi-image dimension using the sorted first and second undersampled image datasets.

Abstract: Methods and systems in a parallel magnetic resonance imaging (MRI) system utilize sensitivity-encoded MRI data acquired from multiple receiver coils together with spatially dependent receiver coil sensitivities to generate MRI images. The acquired MRI data forms a reduced MRI data set that is undersampled in at least a phase-encoding direction in a frequency domain. The acquired MRI data and auto-calibration signal data are used to determine reconstruction coefficients for each receiver coil using a weighted or a robust least squares method. The reconstruction coefficients vary spatially with respect to at least the spatial coordinate that is orthogonal to the undersampled, phase-encoding direction(s) (e.g., a frequency encoding direction).

Abstract: A method, system, and computer-readable medium are provided which perform reconstruction of an image from undersampled k-space data. Imaging data of an image area is received. The imaging data is thermal magnetic resonance imaging data in k-space generated at less than the Nyquist rate. A cost function is minimized based on an image estimate and the received imaging data. An image of the image area is defined based on the minimized cost function. The received imaging data may include current k-space data and a summation of k-space data from previous time frames. Additionally, the image may be defined before imaging data is received for a next timeframe.

Abstract: A method, system, and computer-readable medium are provided which perform reconstruction of an image from undersampled, multi-image k-space data. A first undersampled image dataset and a second undersampled image dataset defined in a first dimension are received. The first undersampled image dataset and the second undersampled image dataset define a multi-image dimension. An ordering for a plurality of pixels of the first dimension in the multi-image dimension is defined. The first undersampled image dataset and the second undersampled image dataset in the multi-image dimension are sorted based on the defined ordering. A first constraint is defined in the first dimension using the unsorted first and second undersampled image datasets. A second constraint is defined in the multi-image dimension using the sorted first and second undersampled image datasets.

Abstract: Methods are provided for recovering component signals or estimates of component signals from combined signals of multiple tracers in the context of imaging multiple PET tracers, a single tracer injected repeatedly, or a combination of tracers using multiple-timepoint or dynamic scanning, where the tracer administrations are simultaneous or staggered in time such that some or all of the PET timeframes, images, data, and/or datasets contain overlapping signals from more than one of the tracer administrations.