Abstract: A method and system for automated deformable registration of an organ from medical images includes generating segmentations of the organ by processing a first and second series of images corresponding to different organ states using a first trained CNN. A second trained CNN processes the first and second series of images and the segmentations to deformably register the second series of images to the first series of images. The second trained CNN predicts a displacement field by minimizing a registration loss function, where the displacement field maximizes colocalization of the organ between the different states.

Abstract: A neural network architecture and method for analysis of time series images from an image source employs a 3D-UNet convolutional neural network (CNN) configured to receive the time series images and generate spatiotemporal feature maps therefrom. Multiple sub-convolutional neural network output prongs based on an SRNet architecture receive the feature maps and simultaneously generate inferences for image segmentation, regression of values, and multi-landmark localization.

Abstract: A method and system for automated correction of phase error in MRI-based flow evaluation employs a computer processor programmed to execute a trained convolutional neural network (CNN) to receive and process image data comprising flow velocity data in three directions and magnitude data collected from a region of interest over a scan period from magnetic resonance imaging instrumentation. The image data is processed using the trained CNN to generate three output channels with pixelwise inferred corrections for the flow velocity data which are further smoothed using a regression algorithm. The smoothed corrections are added to the original image data to generate corrected flow data, which may be used for flow visualization and quantization.

Abstract: A method for improving resolution of a lower resolution image includes inputting at least one digital image into a convolutional neural network (CNN) comprising one of a SRNet and a UNet to output a two-dimensional image having a higher resolution. In some embodiments, multiple image frames may be processed using a 3D CNN to generate a two-dimensional output image.

Abstract: A method for identification of an optimal image within a sequence of image frames includes inputting the sequence of images into a computer processor configured for executing a plurality of neural networks and applying a sliding window to the image sequence to identify a plurality of image frame windows. The image frame windows are processed using a first neural network trained to classify the image frames according to identified spatial features. The image frame windows are also processed using a second neural network trained to classify the image frames according to identified serial features. The results of each classification are concatenated to separate each of the image frame windows into one of two classes, one class containing the optimal image. An output is generated to display image frame windows classification as including the optimal image.

Abstract: A method for identification of an optimal image within a sequence of image frames includes inputting the sequence of images into a computer processor configured for executing a plurality of neural networks and applying a sliding window to the image sequence to identify a plurality of image frame windows. The image frame windows are processed using a first neural network trained to classify the image frames according to identified spatial features. The image frame windows are also processed using a second neural network trained to classify the image frames according to identified serial features. The results of each classification are concatenated to separate each of the image frame windows into one of two classes, one class containing the optimal image. An output is generated to display image frame windows classification as including the optimal image.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large medical imaging data sets and metadata.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.

Abstract: 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: 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. An asynchronous command and imaging pipeline allows remote image processing and analysis in a timely and secure manner even with complicated or large 4-D flow MRI data sets.

Abstract: Processing techniques of volumetric anatomic and vector field data from volumetric phase-contrast MRI on a magnetic resonance imaging (MRI) system are provided to evaluate the physiology of the heart and vessels. This method includes the steps of: (1) correcting for phase-error in the source data, (2) visualizing the vector field superimposed on the anatomic data, (3) using this visualization to select and view planes in the volume, and (4) using these planes to delineate the boundaries of the heart and vessels so that measurements of the heart and vessels can be accurately obtained.