A system and method comprises execution of a segmented magnetic resonance imaging pulse sequence, the pulse sequence including a plurality of shots, each of the plurality of shots including an inversion recovery preparation pulse and acquiring a respective segment of k-space lines, wherein each shot comprises a different inversion time between a peak of the inversion recovery pulse and a midpoint of the acquisition of the respective segment of k-space lines, and reconstruction of an image based on the acquired respective segments of k-space lines. In some aspects, the k-space lines acquired by each shot are consecutive in a phase encoding direction of k-space and each shot acquires the segments of k-space lines acquired by prior shots in the sequence, and a time delay between the inversion recovery preparation pulse and acquisition of a first segment for each shot is equal. In other aspects, each shot acquires its respective segment using interleaved reordering and the time delay between the inversion recovery preparation pulse and acquisition of the respective segment for each shot is different.

The present disclosure provides a method for optimizing T1-weighted magnetic resonance imaging of infant brains. Firstly, T1 and PD maps of infant brains at 0-12 months old are collected to obtain average T1 and PD values of WM and GM of the infant brains, and infants are classified into three age groups according to the characteristics of WM and GM T1 values of the infant brains. Then, the theoretical signal strength of the WM and GM of the infant brains generated from a 3D T1-weighted imaging sequence is calculated through Bloch simulation, and a theoretical optimal TI scheme of each group is determined according to the simulated WM/GM contrast characteristics under different TIs. Finally, the theoretical optimal TI scheme is applied to a target infant brain according to the designated age group for 3D T1-weighted magnetic resonance imaging.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

The most appropriate image for a diagnostic target among a plurality of images is selected and accurate diagnosis support information is presented regardless of the type of a selected image, a modality, or the like. An image diagnosis support apparatus includes: a diagnostic information generation unit that generates diagnostic information based on a plurality of medical images; a reliability calculation unit that evaluates an image quality and calculates an image reliability for each of the plurality of medical images; and a degree-of-contribution calculation unit that calculates a degree of contribution of each of the plurality of medical images to the diagnostic information using an internal parameter indicating a degree of appropriateness of each medical image for a diagnostic target and the reliability calculated by the reliability calculation unit. An image for detection used by the diagnostic information generation unit is generated based on the degree of contribution.

To provide a technique with which a FLAIR image and a T1-weighted image can be acquired in a short scan time, a magnetic resonance imaging apparatus comprises: an RF driver unit 121 for driving an RF coil unit 103; a gradient coil driver unit 122 for driving a gradient coil unit 102; and a controller unit 124 connected to the RF coil driver unit 121 and gradient coil driver unit 122, for controlling them so that an imaging sequence ISc having a duration of 1TR for generating echoes from a body part being imaged is repetitively executed, where the imaging sequence ISc has a first sequence part SQ1 including an inversion pulse 11 and an FSE sequence 12, and a second sequence part SQ2 including an inversion pulse 21 and a GRE sequence 22.

In one embodiment, a magnetic resonance imaging apparatus includes: a scanner that includes a static magnetic field magnet configured to generate a static magnetic field, a gradient coil configured to generate a gradient magnetic field, and a WB (Whole Body) coil configured to apply an RF pulse to an object; and processing circuitry. The processing circuitry is configured to: set (i) a pulse sequence in which a sequence element is repeated, the sequence element including at least an inversion pulse and (ii) a data acquisition sequence executed after a delay time from the inversion pulse; and cause the scanner to execute the pulse sequence by using virtual gating.

In a method for recording diffusion-weighted measurement data, using a MR system with diffusion weightings with two+ different b-values, diffusion directions and diffusion weightings with the associated b-values to be used for the desired recordings are loaded, a sequence of recordings of measurement data to be recorded consecutively are determined by sorting the diffusion directions and diffusion weightings to be recorded based on their associated b-value, such that the b-value of a recording of measurement data is less than the b-value of the immediately preceding recording of measurement data by no more than a predetermined threshold value, and the recordings are recorded based on the determined sequence. By arranging diffusion encodings for the desired recordings to be used consecutively, abrupt discontinuities in the b-values used chronologically are prevented, thereby eddy current effects from preceding recordings have time to abate in the case of recordings with small b-values.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

A magnetic resonance imaging (MRI) system and method for acquiring magnetic resonance (MR) images using a pulse sequence implementing driven equilibrium and quadratic phase cycling techniques is provided. The method includes, during a pulse repetition period of a pulse sequence and using a quadratic phase cycling scheme, applying a first RF pulse to deflect a net magnetization vector associated with the subject from a longitudinal plane into a transverse plane; after applying the first RF pulse, applying a first sequence of RF pulses each of which flips the net magnetization vector by approximately 180 degrees within the transverse plane; and after applying the first sequence of RF pulses, applying a second RF pulse to deflect the net magnetization vector from the transverse plane to the longitudinal plane.

The disclosed technology relates to a method and device for generating a fat suppression magnetic resonance image. The method includes: inputting, by an imaging device, a magnetic resonance image to an encoder of a neural network to extract features of the magnetic resonance image; and generating, by a generator of the neural network, a T2-weighted fat suppression image based on the features, in which the neural network is trained according to a result of discriminating, by a discriminator of the neural network, a loss due to a generation of a T2-weighted fat suppression image and as a result of reconstructing, by a decoder, the magnetic resonance image input to the encoder using a Bloch equation before the magnetic resonance image is input.