Abstract: The disclosure provides a system for noise analysis. The system may acquire a plurality of signals of a subject, and determine a signal representation of the subject based on the plurality of signals. The system may determine an updated signal representation of the subject by adding a signal disturbance into the plurality of signals. The system may further determine a value of a noise parameter indicative of a signal level of the plurality of signals relative to noise of the plurality of signals based on the signal representation and the updated signal representation of the subject.

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 method for determining a flow sensitive gradient block includes determine a gradient parameter for one or more echoes in an imaging sequence according to a scanning protocol. The method also includes determining one or more time origins for gradient moment calculation based on the gradient parameter and obtaining one or more target zeroth-order gradient moments and one or more target first-order gradient moments corresponding to the one or more time origins. The method further includes determining one or more parameters of the one or more flow sensitive gradient blocks with respect to the one or more target zeroth-order gradient moments and the one or more target first-order gradient moment and updating the gradient parameter according to the one or more parameters of the one or more flow sensitive gradient blocks.

Abstract: Systems and methods for determining a distribution map of susceptibility property of an object are provided. The method may include one or more of the following operations. A phase diagram corresponding to a magnetic resonance (MR) signal of the object may be obtained. A preliminary field map may be determined based on the phase diagram. Preliminary error limiting information associated with the preliminary field map may be obtained. A preliminary distribution map of susceptibility property of the object may be determined based on the preliminary field map and the preliminary error limiting information. An iteration process including at least one iteration may be performed to determine a target distribution map of susceptibility property of the object.

Abstract: A system and method for magnetic resonance imaging is provided. The method may include: determining a scanning parameter, wherein the scanning parameter includes one or more predetermined values; obtaining one or more MR signal sets based on the one or more predetermined values; generating one or more original images based on the one or more MR signal sets, wherein an original image corresponds to an MR signal set; determining one or more virtual values associated with the scanning parameter; and generating one or more virtual images based on the one or more virtual values and the one or more original images, wherein a virtual image corresponds to a virtual value.

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 a system and method for MRI with respect to vessels and bleedings. The method may include exciting a region of interest by applying an RF pulse, wherein the region of interest includes a vessel region and a bleeding region. The method may further include acquiring a plurality of echo signals related to the region of interest. The method may further include generating one or more magnitude images based on the plurality of echo signals, generating a first image with respect to the vessel region based on the one or more magnitude images, generating one or more phase images based on the plurality of echo signals, and generating a second image with respect to a distribution of susceptibility of the bleeding region based on the one or more phase images.

Abstract: A method for determining a flow sensitive gradient block includes determine a gradient parameter for one or more echoes in an imaging sequence according to a scanning protocol. The method also includes determining one or more time origins for gradient moment calculation based on the gradient parameter and obtaining one or more target zeroth-order gradient moments and one or more target first-order gradient moments corresponding to the one or more time origins. The method further includes determining one or more parameters of the one or more flow sensitive gradient blocks with respect to the one or more target zeroth-order gradient moments and the one or more target first-order gradient moment and updating the gradient parameter according to the one or more parameters of the one or more flow sensitive gradient blocks.

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.

Abstract: A system and method for magnetic resonance imaging is provided. The method may include: determining a scanning parameter, wherein the scanning parameter includes one or more predetermined values; obtaining one or more MR signal sets based on the one or more predetermined values; generating one or more original images based on the one or more MR signal sets, wherein an original image corresponds to an MR signal set; determining one or more virtual values associated with the scanning parameter; and generating one or more virtual images based on the one or more virtual values and the one or more original images, wherein a virtual image corresponds to a virtual value.