Abstract: A system and method for cardiac magnetic resonance imaging (MRI) is disclosed that facilitates the phase sensitive reconstruction of inversion recovery magnetization prepared data with minimal scan time penalty by acquiring the phase reference data with low spatial resolution. The technique can be applied for the investigation of myocardial tissue characterization by acquiring 2D and/or 3D late Gadolinium enhancement (LGE) scans after the injection of a Gadolinium contrast agent. Regional areas of contrast accumulation in scarred myocardial tissue appear bright on these T1-weighted images. As disclosed here the proposed technique for phase sensitive inversion recovery acquisition with low resolution phase reference is robust against changes in inversion time, change in T1 due to Gadolinium contrast washout, high signal-to-noise ratio, and low scan time penalty compared to magnitude LGE.

Abstract: A system and method of self-calibrated correction for residual phase in phase-contrast magnetic resonance (PCMR) imaging data. The method includes receiving PCMR image data from an MR scanner system, segmenting static tissue from non-static cardiovascular elements of the image data, calculating a non-linear fitted-phase basis function, the non-linear fitted-phase basis function based on system artifacts of the PCMR system, adding the non-linear fitted-phase basis function to linear fit terms, and subtracting the result of the adding step from the PCMR imaging data. The system includes a PCMR scanning apparatus configured to provide PCMR image data, a scanner control circuit configured to control the scanning apparatus during image acquisition, the scanner control circuitry in communication with a control processor, the control processor configured to execute computer-readable instructions that cause the control processor to perform the method. A non-transitory computer-readable medium is also disclosed.

Abstract: A system and method for cardiac magnetic resonance imaging (MRI) is disclosed that facilitates the phase sensitive reconstruction of inversion recovery magnetization prepared data with minimal scan time penalty by acquiring the phase reference data with low spatial resolution. The technique can be applied for the investigation of myocardial tissue characterization by acquiring 2D and/or 3D late Gadolinium enhancement (LGE) scans after the injection of a Gadolinium contrast agent. Regional areas of contrast accumulation in scarred myocardial tissue appear bright on these T1-weighted images. As disclosed here the proposed technique for phase sensitive inversion recovery acquisition with low resolution phase reference is robust against changes in inversion time, change in T1 due to Gadolinium contrast washout, high signal-to-noise ratio, and low scan time penalty compared to magnitude LGE.

Abstract: A system and method of self-calibrated correction for residual phase in phase-contrast magnetic resonance (PCMR) imaging data. The method includes receiving PCMR image data from an MR scanner system, segmenting static tissue from non-static cardiovascular elements of the image data, calculating a non-linear fitted-phase basis function, the non-linear fitted-phase basis function based on system artifacts of the PCMR system, adding the non-linear fitted-phase basis function to linear fit terms, and subtracting the result of the adding step from the PCMR imaging data. The system includes a PCMR scanning apparatus configured to provide PCMR image data, a scanner control circuit configured to control the scanning apparatus during image acquisition, the scanner control circuitry in communication with a control processor, the control processor configured to execute computer-readable instructions that cause the control processor to perform the method. A non-transitory computer-readable medium is also disclosed.

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 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: 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 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.