Abstract: Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

Abstract: Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

Abstract: A method for magnetic resonance imaging may include acquiring first k-space data that is generated by entering acquired magnetic resonance (MR) data into a plurality of first k-space locations. The method may further include synthesizing second k-space data for a plurality of second k-space locations that are not filled with the acquired MR data. The method may further include reconstructing an image from the first k-space data and the second k-space data by applying a reconstruction algorithm. The reconstruction algorithm is based at least in part on a neural network technique.

Abstract: A method for magnetic resonance imaging may include acquiring first k-space data that is generated by entering acquired magnetic resonance (MR) data into a plurality of first k-space locations. The method may further include synthesizing second k-space data for a plurality of second k-space locations that are not filled with the acquired MR data. The method may further include reconstructing an image from the first k-space data and the second k-space data by applying a reconstruction algorithm. The reconstruction algorithm is based at least in part on a neural network technique.

Abstract: A method may include obtaining first image data of a first subject and obtaining a target machine learning model. The first image data may indicate a first intensity distribution of a first magnetic field relative to at least one portion of the first subject. The target machine learning model may provide a mapping relationship between second intensity distributions of second magnetic fields and corresponding second susceptibility distributions associated with second subjects under the second magnetic fields. The method may also include generating a first susceptibility distribution associated with the first subject under the first magnetic field based on the target machine learning model and the first image data. The method may further include determining a target MR image of the at least one portion of the first subject based on the first susceptibility distribution.

Abstract: The present disclosure relates to systems and methods for vehicle management. The systems may perform the methods to obtain a plurality of echo signals representing a subject, the plurality of echo signals being formed at a plurality of different echo times; generate a plurality of phase wrapped images based on the plurality of echo signals, each phase wrapped image comprising one or more first wrapped values; generate a phase difference map based on the plurality of phase wrapped images, the phase difference map comprising one or more second wrapped values; generate an unwrapped phase difference map by unwrapping the one or more second wrapped values in the phase difference map; determine a first field distribution map based on the unwrapped phase difference map; and generate a plurality of unwrapped phase images by unwrapping the one or more first wrapped values in each phase wrapped image based on the first field distribution map.

Abstract: A method may include obtaining first image data of a first subject and obtaining a target machine learning model. The first image data may indicate a first intensity distribution of a first magnetic field relative to at least one portion of the first subject. The target machine learning model may provide a mapping relationship between second intensity distributions of second magnetic fields and corresponding second susceptibility distributions associated with second subjects under the second magnetic fields. The method may also include generating a first susceptibility distribution associated with the first subject under the first magnetic field based on the target machine learning model and the first image data. The method may further include determining a target MR image of the at least one portion of the first subject based on the first susceptibility distribution.

Abstract: A system and method for magnetic resonance imaging is provided. The method includes acquiring a plurality of echo signals relating to a region of interest of a subject at a number of echo times; generating a plurality of phase images based on the plurality of echo signals; generating an unwrapped phase map by performing a phase unwrapping correction to the plurality of phase images; generating a virtual phase map based on the unwrapped phase map; determining a phase mask based on the virtual phase map; obtaining magnitude information of the plurality of echo signals; and generating a susceptibility weighted image based on the phase mask and the magnitude information.

Abstract: Systems and methods for magnetic resonance imaging acceleration. The systems may perform the methods to obtain imaging data of a subject, or a portion thereof, captured by the MRI system according to an undersampling pattern; execute a first iterative procedure; determine that the first iteration number meets a first threshold; execute, in response to the determination that the first iteration number meets the first threshold, a second iterative procedure; determine that a sum of the first iteration number and the second iteration number meets a second threshold; and generate a reconstruction image of the subject, or a portion thereof, according to the processed imaging data, wherein the first threshold is lower than the second threshold.

Abstract: A system and method for magnetic resonance imaging is provided. The method includes acquiring a plurality of echo signals relating to a region of interest of a subject at a number of echo times; generating a plurality of phase images based on the plurality of echo signals; generating an unwrapped phase map by performing a phase unwrapping correction to the plurality of phase images; generating a virtual phase map based on the unwrapped phase map; determining a phase mask based on the virtual phase map; obtaining magnitude information of the plurality of echo signals; and generating a susceptibility weighted image based on the phase mask and the magnitude information.

Abstract: The present disclosure relates to systems and methods for vehicle management. The systems may perform the methods to obtain a plurality of echo signals representing a subject, the plurality of echo signals being formed at a plurality of different echo times; generate a plurality of phase wrapped images based on the plurality of echo signals, each phase wrapped image comprising one or more first wrapped values; generate a phase difference map based on the plurality of phase wrapped images, the phase difference map comprising one or more second wrapped values; generate an unwrapped phase difference map by unwrapping the one or more second wrapped values in the phase difference map; determine a first field distribution map based on the unwrapped phase difference map; and generate a plurality of unwrapped phase images by unwrapping the one or more first wrapped values in each phase wrapped image based on the first field distribution map.