A method for reconstructing a set of one or more MRI images from one or more segmented acquisitions of MRI raw data includes, for each respective shot capturing a part of the MRI raw data of the respective images, capturing and reconstructing a low-resolution navigation image of a region of interest, either from the part of the MRI raw data or from an additional MRI raw data acquired adjacent to the part of the MRI raw data in time. Each segmented acquisition consists of a number of shots.

The present disclosure relates to systems and methods for magnetic resonance imaging. The method may include obtaining primary imaging data associated with a region of interest (ROI) of a subject and obtaining secondary data associated with the ROI. The method may also include determining secondary imaging data based on the secondary data by using a trained model. The method may further include reconstructing a magnetic resonance image based on the primary imaging data and the secondary imaging data.

The present invention relates to the locally resolved examination of objects by means of magnetic resonance (MR) and relates specifically to a less motion-artifact prone method for navigated multi-shot acquisition of diffusion-weighted image data using moment-nulled magnetic field gradients for diffusion encoding. The invention further relates to an apparatus for performing the method.

Reference data relating to a portion of a patient anatomy during patient motion can be acquired from a magnetic resonance imaging system (MRI) to develop a patient motion library. During a time of interest, tracking data is acquired that can be related to the reference data. Partial volumetric data is acquired during the time of interest and at approximately the same time as the acquisition of the tracking data. A volumetric image of patient anatomy that represents a particular motion state can be constructed from the acquired partial volumetric data and acquired tracking data.

Described herein is a system and method of controlling real-time image-guided adaptive radiation treatment of at least a portion of a region of a patient. The computer-implemented method comprises obtaining a plurality of real-time image data corresponding to 2-dimensional (2D) magnetic resonance imaging (MRI) images including at least a portion of the region, performing 2D motion field estimation on the plurality of image data, approximating a 3-dimensional (3D) motion field estimation, including applying a conversion model to the 2D motion field estimation, determining at least one real-time change of at least a portion of the region based on the approximated 3D motion field estimation, and controlling the treatment of at least a portion of the region using the determined at least one change.

A method of controlling and processing data from a hybrid PET-MR imaging system includes acquiring a positron emission tomographic (PET) dataset over a time period, wherein the PET dataset is affected by a quasi-periodic motion of the patient, and acquiring magnetic resonance (MR) data during the time period such that the acquisition time of the MR data relative to the PET dataset is known. A characteristic of the patient motion is then determined based on the PET dataset and the MR data is processed based on the characteristic of patient motion.

The present invention relates to the locally resolved examination of objects by means of magnetic resonance (MR) and relates specifically to a less motion-artifact prone method for navigated multi-shot acquisition of diffusion-weighted image data using moment-nulled magnetic field gradients for diffusion encoding. The invention further relates to an apparatus for performing the method.

A method for performing magnetic resonance imaging on a subject comprises obtaining undersampled imaging data, extracting one or more temporal basis functions from the imaging data, extracting one or more preliminary spatial weighting functions from the imaging data, inputting the one or more preliminary spatial weighting functions into a neural network to produce one or more final spatial weighting functions, and multiplying the one or more final spatial weighting functions by the one or more temporal basis functions to generate an image sequence. Each of the temporal basis functions corresponds to at least one time-varying dimension of the subject. Each of the preliminary spatial weighting functions corresponds to a spatially-varying dimension of the subject. Each of the final spatial weighting functions is an artifact-free estimation of the one of the one or more preliminary spatial weighting functions.

Disclosed herein is a medical system (100, 300) comprising a memory (110) storing machine executable instructions (120) and an image generating neural network (122). The image generating neural network is configured for outputting synthetic magnetic resonance image data (128) in response to receiving reference magnetic resonance image data (126) as input. The synthetic magnetic resonance image data is a simulation of magnetic resonance image data acquired according to a first configuration of a magnetic resonance imaging system when the reference magnetic resonance image data is acquired according to a second configuration of the magnetic resonance imaging system. Execution of the machine executable instructions causes a computational system (106) to: receive (200) measured k-space data (124) acquired according to the first configuration of the magnetic resonance imaging system; receive (202) the reference magnetic resonance image data; receive (204) the synthetic magnetic resonance image data by inputting the reference magnetic resonance image data into the image generating neural network; and reconstruct (206) corrected magnetic resonance image data (132) from the measured k-space data and the synthetic magnetic resonance image data.

A magnetic resonance imaging (MRI) system and methods are provided for producing images of a subject. In some aspects, a method includes identifying a point in the cardiac cycle, performing an inversion recovery (IR) pulse at a selected time point from the pre-determined point, and sampling a k-space segment at an inversion time from the IR pulse that is substantially coincident with the pre-determined point. The method also includes repeating the IR pulse and k-space sampling for multiple inversion times, and multiple segments of k-space, in an interleaved manner, to generate datasets having T1-weighted contrasts determined by their respective inversion times. The method further includes reconstructing three-dimensional (3D) spatially-aligned images using the datasets, and generating a T1 recovery map by combining the 3D images. In some aspects, a prospective/retrospective scheme may be used to obtain data fully sampled in the center of k-space and randomly undersampled in the outer regions.