Systems and methods for monitoring pulsatile flows are disclosed. One method includes obtaining a plurality of magnetic resonance imaging (MRI) scans of a biological structure, acquired consecutively in time, each of the MRI scans acquired using a modified True FISP sequence. The method also includes assembling the plurality of scans into a video file of the biological structure and identifying pulsatile features within the video file.

Systems and methods for automated segmentation of anatomical structures, such as the human heart. The systems and methods employ convolutional neural networks (CNNs) to autonomously segment various parts of an anatomical structure represented by image data, such as 3D MRI data. The convolutional neural network utilizes two paths, a contracting path which includes convolution/pooling layers, and an expanding path which includes upsampling/convolution layers. The loss function used to validate the CNN model may specifically account for missing data, which allows for use of a larger training set. The CNN model may utilize multi-dimensional kernels (e.g., 2D, 3D, 4D, 6D), and may include various channels which encode spatial data, time data, flow data, etc. The systems and methods of the present disclosure also utilize CNNs to provide automated detection and display of landmarks in images of anatomical structures.

The embodiments of the present application disclose a method and an apparatus for determining flow rates of components of multiphase fluid. The method comprises: performing a first magnetization treatment and a second magnetization treatment on multiphase fluid in a pipeline in a target oil and gas well, respectively, to obtain first magnetized multiphase fluid and second magnetized multiphase fluid; determining a first echo train signal set and a second echo train signal set corresponding to the first magnetized multiphase fluid and the second magnetized multiphase fluid, respectively; determining contents of an oil phase component, a water phase component, and a gas phase component of the multiphase fluid at a specified horizon position, and determining a flow velocity of the multiphase fluid at the specified horizon position; and determining flow rates of the oil phase component, the water phase component and the gas phase component in the multiphase fluid. The technical solutions provided by the embodiments of the present application can improve the accuracy of the determined flow rate of each component of the multiphase fluid.

A method for determining quantitative parameters for dynamic contrast-enhanced MR data includes acquiring a set of contrast-enhanced MR data for a region of interest using a T1-weighted pulse sequence, generating at least one contrast concentration curve based on the set of contrast-enhanced MR data, accessing a comprehensive dictionary of contrast concentration curves and generating a grouped dictionary that has a plurality of groups based on the comprehensive dictionary. Each group includes a plurality of correlated contrast concentration curves and a group representative signal for the group. The method also includes comparing a contrast concentration curve with the group representative signal of each group to select a group, comparing the contrast concentration curve to the plurality of correlated contrast concentration curves in the selected group to identify a set of quantitative parameters for the concentration curve and generating a report including the set of quantitative parameter.

A method for measuring blood flow is provided. A modulated magnetic field is generated by a generator coil. Response signals of the modulated magnetic field are record from an object with an RF reception system. The response signals are measured by at least two RF antennae of the RF reception system. A flow component of the recorded response signals is separated from signal components resulting from movements of the object.

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.

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.

Methods and devices for reconstructing a magnetic resonance image, and a non-transitory machine readable storage medium are provided. In an example, the method includes: obtaining a previous image; for each of channels, collecting k-space data of the channel by a partial sampling technology, generating original k-space data of the channel by mapping the previous image into k-space of the channel, and obtaining residue k-space data of the channel by subtracting the original k-space data of the channel from the k-space data of the channel; reconstructing a residue image with the residue k-space data of each of the channels by taking sparsity of the residue image as a constraint term and a difference between virtual residue k-space data of the channel and the residue k-space data of the channel as a data fidelity term; and obtaining a reconstructed magnetic resonance image by adding the residue image to the previous image.

A method for reconstructing dynamic image data is described. In the method, raw data is acquired in a time-dependent manner from an examination region, wherein at least some of the raw data is assigned various values of movement parameters. First time-dependent image data based on acquired raw data is reconstructed. Furthermore, deformation fields based on the first image data are determined as a function of at least two time-dependent movement parameters. Based on the deformation fields, the raw data and the first image data, corrected image data is then generated. Furthermore, a reconstruction apparatus is described. Moreover, a magnetic resonance imaging system is described.

The invention provides for a magnetic resonance imaging system (100) for acquiring magnetic resonance data (142) from a subject (118) within an imaging zone (108). The magnetic resonance imaging system comprises a memory (134, 136) for storing machine executable instructions (160), and pulse sequence commands (140, 400, 502, 600, 700), wherein the pulse sequence commands are configured to cause the magnetic imaging resonance system to acquire the magnetic resonance data according to a magnetic resonance fingerprinting technique. The pulse sequence commands are further configured to control the magnetic resonance imaging system to perform spatial encoding using a zero echo time magnetic resonance imaging protocol. Execution of the machine executable instructions causes the processor controlling the MRI system to: acquire (200) the magnetic resonance data by controlling the magnetic resonance imaging system with the pulse sequence commands; and calculate (202) a spatial distribution (146) of each of a set of predetermined substances by comparing the magnetic resonance data with a magnetic resonance fingerprinting dictionary (144).