Fluid parameters such as WSS and EL are accurately calculated, using flow velocity information obtained by diffusion tensor imaging. Dispersion of a flow velocity distribution of fluid is calculated, using a diffusion tensor image obtained with respect to an examination target containing fluid, and an estimation model is set for a distribution shape of intra-voxel flow velocity. Using the estimation model and the dispersion of the flow velocity distribution, a differential value of the flow velocity is calculated. Then, a fluid parameter representing a flow characteristic of the fluid is calculated.

In a method and system for acquiring measurement data reference data for a phase correction of the measurement data, a RF excitation pulse is provided to excite spins in the object under examination, one or more RF refocusing pulses are provided to refocus the spins excited by the RF excitation pulse, measurement data is acquired by recording echo signals of refocused spins excited by the RF excitation pulse by switching readout gradients that alternate in their polarity, at least two echo signals are recorded while switching readout gradients with different polarity acquire reference data, chronologically between the providing of the RF excitation pulse and the acquisition of the measurement data, and correction data is determined for phase correction of phase errors contained in the measurement data based on the acquired reference data.

An information processing apparatus according to an embodiment includes a processing circuit. The processing circuit acquires a measurement field corresponding to a spatial distribution of a predetermined physical quantity in a subject of measurement. The processing circuit calculates an unknown quantity in the subject of measurement based on a first equation between the measurement field and the unknown quantity having spatial dependence, and on the acquired measurement field. The first equation is one that is acquired based on a second equation expressing a dual field divergence of which can be expressed using the measurement field, by using the measurement field and the unknown quantity, and on the Helmholtz decomposition of the dual field.

A computing device for diffusion dictionary imaging (DDI) of microstructure and inflammation of a patient is provided. The DDI computing device is connected to other computing devices, such as a magnetic resonance imaging (MRI) scanner. The DDI computing device receives magnetic resonance (MR) signals from the MRI scanner. Once received, the DDI computing device records the one or more MR signals to a memory device. The DDI computing device computationally processes the one or more MR signals to reconstruct a diffusion MRI image using diffusion dictionary data. The MR signals include values that are used as input to algorithms of the DDI data to reconstruct the diffusion MRI image. A database is used to store DDI data, artificial intelligence (AI) data, diffusion dictionary data, and MR data.

A magnetic resonance imaging apparatus according to an embodiment executes a first imaging prior to a second imaging and includes processing circuitry. The processing circuitry receives, on a first image obtained from the first imaging, a setting of a region in which an RF (Radio Frequency) pulse is to be applied to a subject, generates a three-dimensional image based on the first image, determines, based on an imaging purpose of the second imaging, a translucent region to which translucent processing is to be performed in the three-dimensional image, and displays the translucent region, making the translucent region translucent in the three-dimensional image.

A system includes a machine readable storage medium storing instructions and a processor to execute the instructions. The processor executes the instructions to receive radial k-space magnetic resonance imaging (MRI) data of a patient and determine a series of dipole sources via direct dipole decomposition of the radial k-space MRI data. The processor executes the instructions to identify an activation within the patient based on the series of dipole sources.

Magnetic resonance elastography (“MRE”), or other imaging-based elastography techniques, generate estimates of the mechanical properties, such as stiffness and damping ratio, of tissues in a subject. A machine learning approach, such as an artificial neural network, is implemented to perform an inversion of displacement data in order to generate the estimates of the mechanical properties.

Techniques are disclosed for measuring the corpus callosum volume of a fetus using magnetic resonance imaging. A scanogram of a fetus is acquired, and a detection area is determined using the corpus callosum position of the fetus in the scanogram. Magnetic resonance scanning is performed on the detection area to obtain a diffusion weighted image, with a gradient direction that is orthogonal or normal to an extending direction of fiber bundles of the corpus callosum. A fetal head image is cropped in the diffusion weighted image, and a predetermined threshold is applied to obtain an image including pixels having a brightness value that is greater than the threshold. Image processing is performed on the binarized image, with the largest region therein being identified as the corpus callosum, and the sum of voxel dimensions associated with the signal of the largest region being calculated as the corpus callosum volume.

The present disclosure is directed to systems and methods of multiphase pseudo-continuous arterial spin labeling.

An exemplary system, method and computer-accessible medium for generating a denoised magnetic resonance (MR) image(s) of a portion(s) of a patient(s) can be provided, which can include, for example, generating a plurality of MR images of the portion(s), where a number of the MR images can be based on a number of MR coils in a MR apparatus used to generate the MR images, generating MR imaging information by denoising a first one of the MR images based on another one of the MR images, and generating the denoised MR image(s) based on the MR imaging information. The number of the MR coils can be a subset of a total number of the MR coils in the MR apparatus. The number of the MR coils can be a total number of the MR coils in the MR apparatus. The MR information can be generated by denoising each of the MR images based on the other one of the MR images.