Abstract: For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Abstract: Systems and methods for determining a quantity of interest of a patient comprise receiving patient data of the patient at a first physiological state. A value of a quantity of interest of the patient at the first physiological state is determined based on the patient data. The quantity of interest represents a medical characteristic of the patient. Features are extracted from the patient data, wherein the features which are extracted are based on the quantity of interest to be determined for the patient at a second physiological state. The value of the quantity of interest of the patient at the first physiological state is mapped to a value of the quantity of interest of the patient at the second physiological state based on the extracted features.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: Imaging from sequential scans is aligned based on patient information. A three-dimensional distribution of a patient-related object or objects, such as an outer surface of the patient or an organ in the patient, is stored with any results (e.g., images and/or measurements). Rather than the entire scan volume, the three-dimensional distributions from the different scans are used to align between the scans. The alignment allows diagnostically useful comparison between the scans, such as guiding an imaging technician to more rapidly determine the location of a same lesion for size comparison.

Abstract: Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: For cardiac flow detection in echocardiography, by detecting one or more valves, sampling planes or flow regions spaced from the valve and/or based on multiple valves are identified. A confidence of the detection may be used to indicate confidence of calculated quantities and/or to place the sampling planes.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method and system for dose-optimized acquisition of a computed tomography (CT) scan of a target organ is disclosed. A localizer spiral CT scan is started at a beginning of a confidence range before a target organ. Real-time localizer scan images are automatically analyzed to predict a beginning location of the target organ based on the real-time localizer scan images. A diagnostic spiral CT scan is automatically started at the predicted beginning location of the target organ. Real-time diagnostic scan images are automatically analyzed to predict an end location of the target organ where full coverage of the target organ will be reached. The diagnostic spiral CT scan is automatically stopped in response to reaching the predicted end location of the target organ. A 3D profile can be acquired using a 3D camera and used to determine the confidence range before the target organ.

Abstract: A method and system for prediction of post-stenting hemodynamic metrics for treatment planning of arterial stenosis is disclosed. A pre-stenting patient-specific anatomical model of the coronary arteries is extracted from medical image data of a patient Blood flow is simulated in the pre-stenting patient-specific anatomical model of the coronary arteries with a modified pressure-drop model that simulates an effect of stenting on a target stenosis region used to compute a pressure drop over the target stenosis region. Parameter values for the modified pressure-drop model are set without modifying the pre-stenting patient-specific anatomical model of the coronary arteries. A predicted post-stenting hemodynamic metric for the target stenosis region, such as fractional flow reserve (FFR), is calculated based on the pressure-drop over the target stenosis region computed using the modified pressure-drop model.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method and system for dose-optimized acquisition of a computed tomography (CT) scan of a target organ is disclosed. A localizer spiral CT scan is started at a beginning of a confidence range before a target organ. Real-time localizer scan images are automatically analyzed to predict a beginning location of the target organ based on the real-time localizer scan images. A diagnostic spiral CT scan is automatically started at the predicted beginning location of the target organ. Real-time diagnostic scan images are automatically analyzed to predict an end location of the target organ where full coverage of the target organ will be reached. The diagnostic spiral CT scan is automatically stopped in response to reaching the predicted end location of the target organ. A 3D profile can be acquired using a 3D camera and used to determine the confidence range before the target organ.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method and system for automated decision support for treatment planning of arterial stenoses is disclosed. A set of stenotic lesions is identified in a patient's coronary arteries from medical image data of the patient. A plurality of treatment options are generated for the set of stenotic lesions, wherein each of the plurality of treatment options corresponds to a stenting configuration in which one or more of the stenotic lesions are stented. For each of the plurality of treatment options, predicted hemodynamic metrics for the set of stenotic lesions resulting from the stenting configuration corresponding to that treatment option are calculated.

Abstract: Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: Systems and methods for non-invasive assessment of an arterial stenosis, comprising include segmenting a plurality of mesh candidates for an anatomical model of an artery including a stenosis region of a patient from medical imaging data. A hemodynamic index for the stenosis region is computed in each of the plurality of mesh candidates. It is determined whether a variation among values of the hemodynamic index for the stenosis region in each of the plurality of mesh candidates is significant with respect to a threshold associated with a clinical decision regarding the stenosis region.

Abstract: A method and system for determining fractional flow reserve (FFR) for a coronary artery stenosis of a patient is disclosed. In one embodiment, medical image data of the patient including the stenosis is received, a set of features for the stenosis is extracted from the medical image data of the patient, and an FFR value for the stenosis is determined based on the extracted set of features using a trained machine-learning based mapping. In another embodiment, a medical image of the patient including the stenosis of interest is received, image patches corresponding to the stenosis of interest and a coronary tree of the patient are detected, an FFR value for the stenosis of interest is determined using a trained deep neural network regressor applied directly to the detected image patches.

Abstract: A method of magnetic resonance (MR) imaging of a volume undergoing repetitive motion includes obtaining source slice data indicative of a plurality of source slices during the repetitive motion, and obtaining anchor slice data indicative of an anchor slice during the repetitive motion. The anchor slice intersects the plurality of source slices. The source slice data and the anchor slice data are reconstructed. A three-dimensional image assembly procedure is implemented to generate, for each phase of the repetitive motion, volume data based on a respective subset of the reconstructed source slice data. For each phase of the repetitive motion, the respective subset of slices is selected based on a correlation of the source slice data and the anchor slice data along an intersection between each source slice and the anchor slice. The source slice data of the selected subset is corrected for misalignment with the anchor slice data.

Abstract: A system and method for instrument placement using an image based navigation system is disclosed. A target of interest is identified in a medical image of a patient. An image plane is displayed that goes through a center of the target. The image plane has a configurable orientation. The image plane is used to select a path for an instrument from a position on the patient's skin to the center of the target. A trajectory plane is viewed from a tip of the instrument to the center of the target. The trajectory plane reflects an orientation of the instrument. A particular trajectory plane is selected that is representative of a desired orientation of the instrument. An image of the particular trajectory plane is frozen. The instrument can then be inserted using a virtual guide and is navigated toward the target.