An imaging method may include obtaining imaging data associated with a region of interest (ROI) of an object. The imaging data may correspond to a plurality of time-series images of the ROI. The imaging method may also include determining, based on the imaging data, a data set including a spatial basis and one or more temporal bases. The spatial basis may include spatial information of the imaging data. The one or more temporal bases may include temporal information of the imaging data. The imaging method may also include storing, in a storage medium, the spatial basis and the one or more temporal bases.

A method is disclosed for phase contrast magnetic resonance imaging (MRI) comprising: acquiring phase contrast 3D spatiotemporal MRI image data; inputing the 3D spatiotemporal MRI image data to a three-dimensional spatiotemporal convolutional neural network to produce a phase unwrapping estimate; generating from the phase unwrapping estimate an integer number of wraps per pixel; and combining the integer number of wraps per pixel with the phase contrast 3D spatiotemporal MRI image data to produce final output.

A method of cardiac strain analysis uses displacement encoded magnetic resonance image (MRI) data of a heart of the subject and includes generating a phase image for each frame of the displacement encoded MRI data. Phase images include potentially phase-wrapped measured phase values corresponding to pixels of the frame. A convolutional neural network CNN computes a wrapping label map for the phase image, and the wrapping label map includes a respective number of phase wrap cycles present at each pixel in the phase image. Computing an unwrapped phase image includes adding a respective phase correction to each of the potentially-wrapped measured phase values of the phase image, and the phase correction is based on the number of phase wrap cycles present at each pixel. Computing myocardial strain follows by using the unwrapped phase image for strain analysis of the subject.

Aspects described herein estimate the pressure difference across a hollow region arising from fluid flow within the hollow region, based on an imaged fluid flow. The method utilises a complete description of fluid mechanical behaviour to derive an estimate of relative pressure or pressure difference over arbitrary flow segments. The method uses the concept of a virtual or arbitrary velocity field in the analysis of the work-energy of the fluid flow. Furthermore, the method uses statistical analysis to derive the acquired flow as a mean field and a related covariance quantity and uses this statistical description in the evaluation of virtual work-energy of the fluid flow. This assessment of virtual work-energy of the fluid flow is then used to derive an estimate of the pressure difference across any two given points (relative pressure) in the hollow region.

According to one embodiment, a medical image processing apparatus. The apparatus obtains MR dynamic images acquired by MR imaging on a subject, in which a contrast agent has been injected, in accordance with an examination-time imaging condition including magnetic field information, contrast agent information, and/or tissue information. The apparatus sets a standard imaging condition. The apparatus calculates a first index value indicating a temporal change of an MR signal value caused by the contrast agent, the index value being standardized by conversion from the examination-time imaging condition to the standard imaging condition based on the MR dynamic images, the examination-time imaging condition, and the standard imaging condition.

Machine readable instructions, a data processing apparatus and a method are provided to determine one or more properties of motor units of skeletal muscle by analyzing a time series of Magnetic Resonance, MR, images representing a slice of a body part to identify signal voids in one or more images of the series. A comparison of at least one characteristic of the identified signal voids in the images is performed with a control data set of MR images produced by applying a controlled stimulus to a motor nerve to establish inherent characteristics of signal voids corresponding to motor units of skeletal muscle. Properties of candidate motor units are analyzed to confirm or reject them as motor units and at least one of: a firing frequency of at least one of the confirmed motor units, a size of at least one of the confirmed motor units, a number of confirmed motor units in a given image area or a shape of at least one of the confirmed motor units is determined.

Various systems and methods for generating images are provided. In some embodiments, the techniques can include acquiring a medical image and an associated motion characterization. The motion characterization can then be used to generate a plurality of gated image data sets, sorted by phase in the motion cycle. A new amplitude-based motion characterization curve is derived from the association of phases with amplitude-based characteristics in the phase gated images. This newly derived amplitude-based motion characterization curve can then be used to re-sort data according to amplitude-based gating techniques known in the field or with data driven optimization techniques.

A system and method is provided for acquiring flow encoded data from a subject using a magnetic resonance imaging (MRI) system. The method includes acquiring flow encoded (FE) data with alternating encoding polarities and along two of three orthogonal directions through the subject over at least two cycles of the flow within the subject; and separating the FE data into directional FE datasets using a temporal filter that separates the FE data based on temporal modulation of the FE directions caused by the alternating encoding polarities extending over the at least two cycles of the flow within the subject that shift the Fourier spectrum of velocity waveforms corresponding to the FE data. The method also includes using the directional FE datasets to generate an image of the subject showing flow within the subject caused by the at least two cycles of flow within the subject.

A system for MRI is provided. The system may obtain a plurality of sets of under-sampled k-space data corresponding to a plurality of frames. Each set of under-sampled k-space data may be acquired simultaneously from a plurality of slice locations of a subject in one of the frames using an MRI scanner. The system may reconstruct a plurality of reference slice images based on the sets of under-sampled k-space data of the plurality of frames. Each of the reference slice images may be representative of one of the slice locations in more than one frame of the frames. The system may further reconstruct a plurality of image series based on the sets of under-sampled k-space data and the reference slice images. Each image series may correspond to one of the slice locations and include a plurality of slice images of the corresponding slice location in the plurality of frames.

An MRI image processing and analysis system may identify instances of structure in MRI flow data, e.g., coherency, derive contours and/or clinical markers based on the identified structures. The system may be remotely located from one or more MRI acquisition systems, and perform: error detection and/or correction on MRI data sets (e.g., phase error correction, phase aliasing, signal unwrapping, and/or on other artifacts); segmentation; visualization of flow (e.g., velocity, arterial versus venous flow, shunts) superimposed on anatomical structure, quantification; verification; and/or generation of patient specific 4-D flow protocols. A protected health information (PHI) service is provided which de-identifies medical study data and allows medical providers to control PHI data, and uploads the de-identified data to an analytics service provider (ASP) system. A web application is provided which merges the PHI data with the de-identified data while keeping control of the PHI data with the medical provider.