A processor analyzes an image obtained by imaging a subject including a tubular structure in which a fluid flows, thereby deriving fluid information regarding flow of the fluid at each of pixel positions in the tubular structure. The processor derives, within the tubular structure included in the image, a matching degree between the fluid information at a plurality of pixel-of-interest positions set at a first sampling interval and the fluid information at a plurality of pixel positions within a predetermined region based on the pixel-of-interest positions. The processor sets a second sampling interval for displaying the fluid information in accordance with the matching degree. The processor samples the fluid information at the set second sampling interval and causes a display to display the fluid information.

An image acquisition unit acquires a phase contrast image consisting of a plurality of phases for each of three spatial directions, in which a pixel value of each pixel represents a velocity of fluid for each of the three directions, the phase contrast image being acquired by imaging a subject including a structure inside which fluid flows by a three-dimensional cine phase contrast magnetic resonance method. An identification unit identifies a region of the structure in the phase contrast image on the basis of a maximum value of the velocity of the fluid between corresponding pixels in each of the phases of the phase contrast image.

The invention relates to a magnetic resonance system (100) configured for acquiring magnetic resonance data from a GC subject (118). Execution of the machine executable instructions (140) stored in a memory (134) causes a processor (130) to control the magnetic resonance system (100) using a set of waveform and pulse sequence commands (142, 152) to prepare a first state of vibration (211) of the one or more hardware elements and/or the subject (118). The preparing comprises generating the vibration matching gradient (200) inducing the first vibrations (210) of the one or more hardware elements and/or the subject (118), while the net magnetization vector of the subject (118) is aligned along the longitudinal axis of the main magnetic field. The magnetic resonance system (100) is further controlled to acquire the magnetic resonance data (144, 154) according to a magnetic resonance protocol. The acquiring comprises generating in sequence at least two spin manipulating gradients (202, 204) for manipulating phases of nuclear spins within the subject (118), while the net magnetization vector of the subject (118) comprises a non-vanishing component in a transverse plane perpendicular to the longitudinal axis of the main magnetic field. A first one of the at least two spin manipulating gradients (202) is generated during the first state of vibration (211) and a second one of the at least two spin manipulating gradients (204) is generated during a second state of vibration (213) of the one or more hardware elements and/or the subject (118). The vibration matching gradient (200) is used for matching with the first state of vibration (211) the second state of vibration (213).

A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

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.

The invention provides a fluid analysis apparatus, a method for operating a fluid analysis apparatus, and a fluid analysis program that display a flow velocity vector such that the tendency of a fluid flow is easily checked. A representative two-dimensional flow velocity vector representing a plurality of two-dimensional flow velocity vectors obtained by projecting three-dimensional flow velocity vectors of a plurality of voxels that overlap each other in a projection direction of a projection plane to the projection plane is acquired from three-dimensional volume data that has information of the three-dimensional flow velocity vector indicating the flow velocity of a fluid in an anatomical structure for each voxel and is displayed.

A computer-implemented method of correcting phase and reducing noise in magnetic resonance (MR) phase images is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a pair of pristine images and corrupted images, wherein the corrupted images include corrupted phase information, the pristine images are the corrupted images with the corrupted phase information reduced, and target output images of the neural network model are the pristine images. The method further includes receiving MR images including corrupted phase information, and analyzing the received MR images using the neural network model. The method also includes deriving pristine phase images of the received MR images based on the analysis, wherein the derived pristine phase images include reduced corrupted phase information, compared to the received MR images, and outputting MR images based on the derived pristine phase images.

The present invention includes a computerized method of detecting fluid flow in a vessel, the method comprising: obtaining at least one non-contrast enhanced magnetic resonance image from a magnetic resonance imager; performing a phase sensitive reconstruction of the at least one non-contrast enhanced magnetic resonance image using a processor; combining the phase sensitive reconstruction with a velocity selective preparation of the non-contrast enhanced magnetic resonance image, to determine using the processor, in a single acquisition, at least one of: a flow direction of a fluid in the vessel, a reduction or elimination of a background signal, body fat, water/fat separation, or differentiation of a fast moving flow signal from a slow moving flow signal in an opposite direction with suppression of the background signal; and storing or displaying at least one of flow direction or flow strength of the fluid flow in the vessel obtained from the single acquisition.

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.

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.