Abstract:
In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.
Abstract:
In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.
Abstract:
In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.
Abstract:
In one aspect the disclosed technology relates to embodiments of a method which, includes acquiring magnetic resonance imaging data, for a plurality of images, of the heart of a subject. The method also includes segmenting, using cascaded convolutional neural networks (CNN), respective portions of the images corresponding to respective epicardium layers and endocardium layers for a left ventricle (LV) and a right ventricle (RV) of the heart. The segmenting is used for extracting biomarker data from segmented portions of the images and, in one embodiment, assessing hypertrophic cardiomyopathy from the biomarker data. The method further includes segmenting processes for T1 MRI data and LGE MRI data.
Abstract:
Some aspects of the present disclosure relate to systems and methods for three-dimensional spiral perfusion imaging. In one embodiment, a method for perfusion imaging of a subject includes acquiring perfusion imaging data associated with the heart of a subject. The acquiring includes applying an imaging pulse sequence with a three-dimensional stack-of-spirals trajectory. The method also includes reconstructing perfusion images from the acquired perfusion imaging data. The reconstructing includes parallel imaging and motion-guided compressed sensing. The method also includes determining, from the reconstructed perfusion images, absolute perfusion values based on time-intensity relationships to quantify myocardial blood flow of the heart of the subject, and generating a quantitative volumetric perfusion flow map based on the determined absolute perfusion values.
Abstract:
Systems and methods for Cartesian dynamic imaging are disclosed. In one aspect, in accordance with one example embodiment, a method includes acquiring magnetic resonance data for an area of interest of a subject that is associated with one or more physiological activities of the subject and performing image reconstruction comprising Kalman filtering or smoothing on Cartesian images associated with the acquired magnetic resonance data. Performing the image reconstruction includes increasing at least one of spatial and temporal resolution of the Cartesian images.
Abstract:
Methods, systems, and computer readable media for utilizing a therapeutic ultrasound device to perform mitral valve decalcification are disclosed. One method includes acquiring, via an ultrasound imaging component, imaging data of a mitral valve in real-time, defining a therapeutic region of interest corresponding to the mitral valve, and utilizing, by a system controller engine, imaging data from the ultrasound imaging component to determine an interval period of minimal mitral annular movement. The method further includes defining a sequence of therapeutic targets within the region of interest of the mitral valve, utilizing the imaging data acquired in real-time by the ultrasound imaging component to provide a therapeutic ultrasound transducer array with a location and depth of an intra-annular focal zone within the mitral valve, and emitting a high intensity focused ultrasound (HIFU) pulse wave from the therapeutic ultrasound transducer array to each of the therapeutic targets of the mitral valve during the determined interval period and in accordance with the defined sequence.
Abstract:
Some aspects of the present disclosure relate to systems and methods for three-dimensional spiral perfusion imaging. In one embodiment, a method for perfusion imaging of a subject includes acquiring perfusion imaging data associated with the heart of a subject. The acquiring includes applying an imaging pulse sequence with a three-dimensional stack-of-spirals trajectory. The method also includes reconstructing perfusion images from the acquired perfusion imaging data. The reconstructing includes parallel imaging and motion-guided compressed sensing. The method also includes determining, from the reconstructed perfusion images, absolute perfusion values based on time-intensity relationships to quantify myocardial blood flow of the heart of the subject, and generating a quantitative volumetric perfusion flow map based on the determined absolute perfusion values.