A Computer-implemented method of reconstructing a dynamic series of motion-compensated magnetic resonance images of a patient is provided. Images of a patient are acquired over time, at least partially in free-breathing, at a first image resolution and on a frame-by-frame basis. Each frame of the k-space data includes a first subset of data points having a first sample density and a second subset of data points having a second sample density. For each frame, a sub-group of the first subset and the second subset of the k-space data is selected, and an image is reconstructed at a second image resolution. The motion between the second image resolution images is estimated in the form of motion fields. The motion information is incorporated into a final reconstruction of a dynamic series of motion-compensated magnetic resonance images of the patient at a third image resolution.

Systems and methods are provided for performing automated reconstruction of a dynamic MRI dataset that is acquired without a fixed temporal resolution. On one or more image quality metrics (IQMs) are obtained by processing a subset of the acquired dataset. In one example implementation, at each stage of an iterative process, one or more IQMs of the image subset is computed, and the parameters controlling the reconstruction and/or the strategy for data combination are adjusted to provide an improved or optimal image reconstruction. Once the IQM of the image subset satisfies acceptance criteria based on an estimate of the overall temporal fidelity of the reconstruction, the full reconstruction can be performed, and the estimate of the overall temporal fidelity can be reported based on the IQM at the final iteration.

The invention pertains to advances in real-time methods in nuclear magnetic resonance by offering a new dual-frequency dynamic nuclear polarization (DNP) method that uses a microwave beam to polarize the spins of electrons and concomitantly act as a NMR transmitter.

A method for performing real-time magnetic resonance (MR) imaging on a subject is disclosed. A prep pulse sequence is applied to the subject to obtain a high-quality special subspace, and a direct linear mapping from k-space training data to subspace coordinates. A live pulse sequence is then applied to the subject. During the live pulse sequence, real-time images are constructed using a fast matrix multiplication procedure on a single instance of the k-space training readout (e.g., a single k-space line or trajectory), which can be acquired at a high temporal rate.

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.

A method may include obtaining a plurality of imaging signals collected by applying a wave encoding gradient to a region of interest (ROI) of a subject. The method may also include obtaining a plurality of auxiliary signals associated with the ROI. The method may also include obtaining a point spread function corresponding to the wave encoding gradient. The method may also include determining, based on the plurality of auxiliary signals, temporal information relating to at least one temporal dimension of the ROI. The method may also include determining, based on the plurality of auxiliary signals, the plurality of imaging signals, and the point spread function, spatial information relating to at least one spatial dimension of the ROI. The method may also include generating at least one target image of the ROI based on the temporal information and the spatial information.

In a method for diffusion-weighted MR-imaging of an object, which undergoes a cyclic motion, a first sub-period type of the cyclic motion is predicted for a first acquisition timeframe, where the first sub-period type corresponds to one of two or more predefined characteristic types of sub-periods of the cyclic motion. A first amount of diffusion-weighting may be selected based on the first sub-period type. A first MR-acquisition may be carried out during the first acquisition timeframe, where a diffusion-weighting according to the first amount of diffusion-weighting is applied. An MR-image of the object is generated based on MR-data including a first MR-dataset obtained as a result of the first MR-acquisition.

The disclosure relates to techniques for determining a motion parameter of a heart. A subset of a sequence of cardiac MR images is applied as a first input to a first trained convolutional neural network configured to determine, as a first output, a probability distribution of at least 2 anatomical landmarks. The sequence of cardiac MR images is cropped and realigned based on the at least 2 anatomical landmarks to determine a reframed and aligned sequence of new cardiac MR images showing the same orientation of the heart. The reframed and aligned sequence of new cardiac MR images is applied to a second trained convolutional neural network configured to determine, as a second output, a further probability distribution of the at least 2 anatomical landmarks in each new MR image of the reframed and aligned sequence, the motion parameter of the heart is determined based on the second output.

Techniques for determining a functional magnetic resonance data set of an imaging region of a brain of a patient are disclosed in which blood oxygenation level dependent functional magnetic resonance imaging is used. The techniques include using a plurality of reception coils, and acquiring magnetic resonance signals using parallel imaging and a magnetic resonance sequence defining a k-space trajectory, wherein undersampling in at least two k-space directions is performed. The techniques further include reconstructing the functional magnetic resonance data set from the magnetic resonance signals and sensitivity information regarding the plurality of reception coils using a reconstruction technique for undersampled magnetic resonance data, wherein the k-space trajectory is chosen to allow controlled aliasing in all three spatial dimensions including the readout direction.

The present disclosure may provide imaging methods, systems and storage media. The imaging methods may include: obtaining first imaging data acquired by an imaging device, wherein the first imaging data includes data corresponding to a plurality of cardiac cycles; and performing image reconstruction on data corresponding to the plurality of cardiac cycles in the first imaging data to acquire one or more cardiac cines. Each cardiac cine of the one or more cardiac cines may include cardiac images of a plurality of phases in at least one cardiac cycle.