The present disclosure provides a system for MRI. The system may obtain MRI scan data of a subject by directing an MRI scanner to perform an MRI scan on the subject according to a first gradient waveform. The system may also determine a second gradient waveform based on the first gradient waveform and a gradient waveform determination model. The gradient waveform determination model may have been trained according to a machine learning algorithm. The system may further generate a target reconstruction image of the subject based on the second gradient waveform and the MRI scan data.

The present disclosure is directed to MRI techniques. The techniques include occupying a central region of a first k-space with full sampling along a Cartesian trajectory, occupying a peripheral region of the first k-space with undersampling along a non-Cartesian trajectory; acquiring sensitivity distribution information of receiving coils; based on a sensitivity distribution chart, merging the Cartesian data of the central region according to multiple channels to obtain a third k-space; based on the sensitivity distribution chart, applying parallel imaging and compressed sensing to the undersampled non-Cartesian trajectory to reconstruct an image, obtaining a second k-space by transformation, and when the second k-space and third k-space are synthesized, using a central region of the second k-space to replace the third k-space of a corresponding region to obtain a k-space suitable for image reconstruction.

A method to compensate for chemical shift displacement includes, prior to applying an imaging pulse sequence to acquire MRI data of a subject, applying a first saturation pulse within a slice location of an imaging volume of the subject in which the MRI data is to be acquired, wherein the first saturation pulse results in a first chemical shift displacement between water and fat in a first spatial saturation band. The method also includes, prior to applying the imaging pulse sequence, subsequently applying a second saturation pulse within the slice location, wherein the second saturation pulse results in a second chemical displacement between the water and the fat in a second spatial saturation band that results in a final spatial saturation band being free of chemical shift displacement after application of the second saturation pulse, the second chemical shift displacement being different from the first chemical shift displacement.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Embodiments facilitate generation of a prediction of long-term survival (LTS) or short-term survival (STS) of Glioblastoma (GBM) patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for LTS or STS based on a radiographic-deformation and textural heterogeneity (r-DepTH) descriptor generated based on radiographic images of tissue demonstrating GBM. A second set of embodiments discussed herein relates to determination of a prediction of disease outcome for a GBM patient of LTS or STS based on an r-DepTH descriptor generated based on radiographic imagery of the patient.

A system for displaying and interacting with magnetic resonance imaging (MRI) data acquired using an MRI system includes an image reconstruction module configured to receive the MRI data and to reconstruct a plurality of images using the MRI data, an image rendering module coupled to the image reconstruction module and configured to generate at least one multidimensional image based on the plurality of images and a user interface device coupled to the image rendering module and located proximate to a workstation of the MRI system. The user interface device is configured to display the at least one multidimensional image in real-time and to facilitate interaction by a user with the multidimensional image in a virtual reality or augmented reality environment.

The present disclosure relates to quantitative magnetic resonance imaging. A time series of magnetic resonance images of an examination region are assigned to different time points following an excitation is acquired by means of a magnetic resonance device, a signal evolution varying with respect to time is determined from the magnetic resonance images for each pixel from the magnetic resonance data of all of the magnetic resonance images and, by comparison of the signal evolution with comparison evolutions stored in a database, at least one quantitative result value on which the comparison evolution exhibiting the greatest agreement is based is assigned to a respective pixel.

A resonance circuit includes: an inductor formed along a surface of a first cylindrical form having a central axis; and a capacitor formed along a surface of a second cylindrical form having the central axis, wherein the inductor and the capacitor are electrically connected to each other to form a closed loop.

For reconstruction of an image in MRI, unsupervised training (i.e., data-driven) based on a scan of a given patient is used to reconstruct model parameters, such as estimating values of a contrast model and a motion model based on fit of images generated by the models for different readouts and times. The models and the estimated values from the scan-specific unsupervised training are then used to generate the patient image for that scan. This may avoid artifacts from binning different readouts together while allowing for scan sequences using multiple readouts.

A magnetic resonance imaging apparatus according to an aspect of the present disclosure includes a sequence controlling circuit and a processing circuit. The sequence controlling circuit is configured to acquire undersampled frequency domain scan data by executing a pulse sequence while carrying out an undersampling process. The processing circuit is configured: to generate image domain corrected data of the frequency domain scan data, by correcting the frequency domain scan data in an image domain; to generate frequency domain corrected data of the frequency domain scan data by correcting the frequency domain scan data in a frequency domain; to optimize the frequency domain scan data based on the image domain corrected data and the frequency domain corrected data; and to reconstruct image data by using the optimized frequency domain scan data.