In one embodiment, an MRI apparatus includes: processing circuitry configured to: set a first pulse sequence and a second pulse sequence, wherein, in the first pulse sequence, a first gradient pulse is applied between two adjacent refocusing pulses, and, in the second pulse sequence, a second gradient pulse being different in pulse shape from the first gradient pulse is applied between two adjacent refocusing pulses, wherein: the scanner is configured to acquire first signals and second signals; and the processing circuitry is configured to generate at least one first image and at least one second image; and calculate a T2 value of a body fluid of the object from the at least one first image and the at least one second image in such a manner that influence of movement including diffusion of the body fluid is removed.

In one embodiment, an MRI apparatus includes: processing circuitry configured to: set a first pulse sequence and a second pulse sequence, wherein, in the first pulse sequence, a first gradient pulse is applied between two adjacent refocusing pulses, and, in the second pulse sequence, a second gradient pulse being different in pulse shape from the first gradient pulse is applied between two adjacent refocusing pulses, wherein: the scanner is configured to acquire first signals and second signals; and the processing circuitry is configured to generate at least one first image and at least one second image; and calculate a T2 value of a body fluid of the object from the at least one first image and the at least one second image in such a manner that influence of movement including diffusion of the body fluid is removed.

A computer-implemented method for generating an artifact corrected reconstructed contrast image from magnetic resonance imaging (MRI) data is provided. The method includes inputting into a trained deep neural network both a synthesized contrast image derived from multi-delay multi-echo (MDME) scan data or the MDME scan data acquired during a first scan of an object of interest utilizing a MDME sequence and a composite image, wherein the composite image is derived from both the MDME scan data and contrast scan data acquired during a second scan of the object of interest utilizing a contrast MRI sequence. The method also includes utilizing the trained deep neural network to generate the artifact corrected reconstructed contrast image based on both the synthesized contrast image or the MDME scan data and the composite image. The method further includes outputting from the trained deep neural network the artifact corrected reconstructed contrast image.

The present disclosure relates to magnetic resonance imaging technology for simultaneously measuring a plurality of magnetic resonance imaging parameters. According to one embodiment of the present disclosure, a magnetic resonance imaging apparatus includes a data collector for alternately collecting a steady-state-free-precession (SSFP)-FID signal and an SSFP-ECHO signal within a time of repetition to obtain AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data; a data processor for reconstructing a magnitude image and a phase image for each of the SSFP-FID signal and the SSFP-ECHO signal in the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data and processing the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data using the reconstructed magnitude images and phase images; and a parameter measuring device for measuring a plurality of magnetic resonance imaging parameters using a plurality of echo data based on the processed AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data.

The present disclosure relates to magnetic resonance imaging technology for simultaneously measuring a plurality of magnetic resonance imaging parameters. According to one embodiment of the present disclosure, a magnetic resonance imaging apparatus includes a data collector for alternately collecting a steady-state-free-precession (SSFP)-FID signal and an SSFP-ECHO signal within a time of repetition to obtain AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data; a data processor for reconstructing a magnitude image and a phase image for each of the SSFP-FID signal and the SSFP-ECHO signal in the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data and processing the AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data using the reconstructed magnitude images and phase images; and a parameter measuring device for measuring a plurality of magnetic resonance imaging parameters using a plurality of echo data based on the processed AUSFIDE (alternating unbalanced SSFP-FID & SSFP-ECHO) image data.

Disclosed is a method for generating a time-dependent magnetic field gradient in diffusion weighted magnetic resonance imaging G(t)=[G.sub.x(t)G.sub.y(t)G.sub.z(t)].sup.T, which is asymmetric in time with respect to a refocusing pulse, by meeting one or more of the requirements: A=∫.sub.0.sup.TEh(t)G(t)G(t).sup.Tdt is zero, where TE is an echo time and h(t) is a function of time which is positive during an interval prior to the refocusing pulse and negative during a time interval after the refocusing pulse); minimize A or m=(Tr[AA]).sup.1/2 where A=∫.sub.P1G(t)G(t).sup.Tdt−∫.sub.P2G(t)G(t).sup.Tdt where P1 and P2 represent time intervals prior to and subsequent to the refocusing pulse; m is smaller than a threshold value. an attenuation factor ${\mathrm{AF}}_p=\exp \left(-\frac{t}{T2*}\right)$
due to T2* relaxation is one. Signal attenuation due to concomitant field gradients, regardless of the shape or orientation of the diffusion encoding b-tensor and the location of signal is hereby minimized.

Disclosed is a method for generating a time-dependent magnetic field gradient in diffusion weighted magnetic resonance imaging G(t)=[G.sub.x(t)G.sub.y(t)G.sub.z(t)].sup.T, which is asymmetric in time with respect to a refocusing pulse, by meeting one or more of the requirements: A=∫.sub.0.sup.TEh(t)G(t)G(t).sup.Tdt is zero, where TE is an echo time and h(t) is a function of time which is positive during an interval prior to the refocusing pulse and negative during a time interval after the refocusing pulse); minimize A or m=(Tr[AA]).sup.1/2 where A=∫.sub.P1G(t)G(t).sup.Tdt−∫.sub.P2G(t)G(t).sup.Tdt where P1 and P2 represent time intervals prior to and subsequent to the refocusing pulse; m is smaller than a threshold value. an attenuation factor ${\mathrm{AF}}_p=\exp \left(-\frac{t}{T2*}\right)$
due to T2* relaxation is one. Signal attenuation due to concomitant field gradients, regardless of the shape or orientation of the diffusion encoding b-tensor and the location of signal is hereby minimized.

A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.

A method for temperature quantification using magnetic resonance fingerprinting (MRF) includes acquiring MRF data from a region of interest in a subject using an MRF pulse sequence with smoothly varying RF phase for MR resonant frequencies that is played out continuously. For each of a plurality of time intervals during acquisition of the MRF data the method further includes comparing a set of the MRF data associated with the time interval to an MRF dictionary to determine at least one quantitative parameter of the acquired MRF data, determining a temperature change based on the at least one quantitative parameter and generating a quantitative map of the temperature change in the region of interest. The region of interest can include aqueous and adipose tissue.

The present disclosure provides a system and method for magnetic resonance imaging. The method may include obtaining a first set of imaging data, the first set of imaging data being sampled in multiple shots, each shot of the multiple shots corresponding to a plurality of echo times, the first set of imaging data including partially sampled data in a first k space; obtaining a second set of imaging data, the second set of imaging data including fully sampled data in a central region of a second k space; determining fitting data in the first k space based on the first set of imaging data and the second set of imaging data; and/or generating a target image based on the fitting data in the first k space and the first set of imaging data in the first k space.