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.

Embodiments of the present invention provide systems and methods to detect a moving anatomic feature during a treatment sequence based on a computed and/or a measured shortest distance between the anatomic feature and at least a portion of an imaging system.

Methods and systems are provided for adaptive scan control. In one embodiment, a method includes processing acquired projection data of a monitoring area of a subject to measure a first contrast signal of a contrast agent administered to the subject via a first injection, initializing a contrast scan of the subject according to a fallback scan prescription, determining when each of a plurality of zones of the contrast scan are estimated to occur based on the contrast signal, generating a personalized scan prescription for the contrast scan based on when each of the plurality of zones are estimated to occur, and performing the contrast scan according to the personalized scan prescription after a second injection of the contrast agent.

Described are a system, method, and computer program product for detecting neurodegeneration using differential tractography and treating neurological disorders accordingly. The method includes obtaining a first diffusion magnetic resonance imaging (MRI) scan of the brain of the patient and obtaining a plurality of diffusion MRI scans of a group of other brains. The method also includes generating a control diffusion MRI scan based on the plurality of diffusion MRI scans of the group of other brains. The method further includes determining a first anisotropy of first neural tracks of the first diffusion MRI scan and a second anisotropy of second neural tracks of the control diffusion MRI scan. The method further includes determining a differential by comparing the first anisotropy to the second anisotropy and identifying at least one neurological disorder based on the differential and a location of the first neural tracks in the brain of the patient.

Systems, methods, and computer program products are provided for segmenting a brain tumor from various MRI sequencing techniques. A plurality of MRI sequences of a head of a patient are received. Each MRI sequence includes a T1-weighted with contrast image, a Fluid Attenuated Inversion Recovery (FLAIR) image, a T1-weighted image, and a T2-weighted image. Each image of the plurality of MRI sequences is registered to an anatomical atlas. A plurality of modified MRI sequences are generated by removing a skull from each image in the plurality of MRI sequences. A tumor segmentation map is determined by segmenting a tumor within a brain in each image in the plurality of modified MRI sequences. The tumor segmentation map is applied to each of the plurality of MRI sequences to thereby generate a plurality of labelled MRI sequences.

The invention relates to a method of Dixon-type MR imaging of an object (10) placed in an examination volume of a MR device (1). It is an object of the invention to provide a method that enables an improved Dixon water/fat separation in combination with a dual-acquisition gradient-echo imaging sequence. The method comprises the steps of: subjecting the object (10) to a dual-acquisition gradient-echo imaging sequence comprising a series of temporally equidistant RF excitations, wherein one gradient echo is generated in each repetition time between successive RF excitations with the echo time alternating between a first and a second value (TE1, TE2), and wherein phase-encoding magnetic field gradients (P, S) are switched in each repetition time to sample a pre-defined region of k-space; acquiring echo signals from the object (10), wherein each gradient echo associated with either the first or the second echo time value (TE1, TE2) is sampled as a partial echo, and—reconstructing an MR image from the acquired echo signals, whereby signal contributions from water and fat are separated. Moreover the invention relates to an MR device (1) and to a computer program to be run on an MR device (1).

A magnetic resonance (MR) imaging method of correcting nonuniformity in diffusion-weighted (DW) MR images of a subject is provided. The method includes applying a DW pulse sequence along a plurality of diffusion directions with one or more numbers of excitations (NEX), and acquiring a plurality of DW MR images of the subject along the plurality of diffusion directions with the one or more NEX. The method also includes deriving a reference image and a base image based on the plurality of DW MR images, generating a nonuniformity factor image based on the reference image and the base image, and combining the plurality of DW MR images into a combined image. The method also includes correcting nonuniformity of the combined image using the nonuniformity factor image, and outputting the corrected image.

The present invention is to acquire a multiphase image while avoiding extension of imaging time and excluding an influence of displacement of an image of each multiphase due to a motion. A method for collecting measurement data is to repeat sampling such that low-frequency data and high-frequency data have different densities. At this time, a sampling interval is set shorter than a motion cycle. Motion information is acquired in parallel with imaging, and measurement data obtained in time series is divided into a plurality of time phases based on the motion information so as to obtain a multiphase image. Displacement correction between multiphase images is performed, and then the multiphase images are integrated. Alternatively, measurement data after the displacement correction is used to generate a time-series image.

A method of implementing an artificial intelligence based neuroradiology platform for neurological tumor identification comprises providing a multilayer convolutional network for neurological tumor identification configured for segmenting data sets of full neurologic scans into resolution voxels; supervised learning and validation of the platform by classification of tissue within classification voxels of a specific given training and validation data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of the training and validation data sets having a predetermined ground truth; and implementing the platform by classification of tissue within classification voxels of a specific given patient data sets by the multilayer convolutional network for neurological tumor identification with each classification voxel of each data set assigned a label. The platform may be used for T-cell therapy initiation and tracking. An artificial intelligence based neuroradiology platform implemented according to the method is disclosed.

A method of medical imaging including receiving k-space data that is divided into multiple k-space data groups, selecting one of the multiple k-space data groups as a reference k-space data group, and calculating spatial transform data for each of the multiple k-space data groups by inputting the multiple k-space data groups and the reference k-space data group into a transformation estimation module. The spatial transformation estimation module is configured for outputting spatial transform data descriptive of a spatial transform between a reference k-space data group and multiple k-space data groups in response to receiving the reference k-space data group and the multiple k-space data groups as input. The method further comprises reconstructing a corrected magnetic resonance image according to the magnetic resonance imaging protocol using the multiple k-space data groups and the spatial transform data for each of the multiple k-space data groups.