Abstract: A method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a plurality of DHM images acquired using a digital holographic microscopy system. One or more connected components are identified in each of the plurality of DHM images and one or more training white blood cell images are generated from the one or more connected components. A classifier is trained to identify a plurality of white blood cell types using the one or more training white blood cell images. The classifier may be applied to a new white blood cell image to determine a plurality of probability values, each respective probability value corresponding to one of the plurality of white blood cell types. The new white blood cell image and the plurality of probability values may then be presented in a graphical user interface.

Abstract: A needle is enhanced in a medical diagnostic ultrasound image. The image intensities associated with a needle in an image are adaptively increased and/or enhanced by compounding from a plurality of ultrasound images. Filtering methods and probabilistic methods are used to locate possible needle locations. In one approach, possible needles are found in component frames that are acquired at the same time but at different beam orientations. The possible needles are associated with each other across the component frames and false detections are removed based on the associations. In one embodiment of needle detection in an ultrasound component frame, lines are found first. The lines are then searched to find possible needle segments. In another embodiment, data from different times may be used to find needle motion and differences from a reference, providing the features in additional to features from a single component frame for needle detection.

Abstract: A method and system for non-invasive assessment of renal artery stenosis is disclosed. A patient-specific anatomical model of at least a portion of the renal arteries and aorta is generated from medical image data of a patient. Patient-specific boundary conditions of a computational model of blood flow in the portion of the renal arteries and aorta are estimated based on the patient-specific anatomical model. Blood flow and pressure are simulated in the portion of the renal arteries and aorta using the computational model based on the patient-specific boundary conditions. At least one hemodynamic quantity characterizing functional severity of a renal stenosis region is calculated based on the simulated blood flow and pressure in the portion of the renal arteries and aorta.

Abstract: A method and system for calculating a volume of resected tissue from a stream of intraoperative images is disclosed. A stream of 2D/2.5D intraoperative images of resected tissue of a patient is received. The 2D/2.5D intraoperative images in the stream are acquired at different angles with respect to the resected tissue. A resected tissue surface is segmented in each of the 2D/2.5D intraoperative images. The segmented resected tissue surfaces are stitched to generate a 3D point cloud representation of the resected tissue surface. A 3D mesh representation of the resected tissue surface is generated from the 3D point cloud representation of the resected tissue surface. The volume of the resected tissue is calculated from the 3D mesh representation of the resected tissue surface.

Abstract: Intraoperative camera data is registered with medical scan data. The same salient features are located in both the medical scan data and the model from the camera data. The features are specifically labeled rather than just being represented by the data. At least an initial rigid registration is performed using the salient features. The coordinate systems of the camera and the medical scan data are aligned without external positions sensors for the intraoperative camera.

Abstract: A method and system for simulating patient-specific cardiac electrophysiology including the effect of the electrical conduction system of the heart is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. The electrical conduction system of the heart of the patient is modeled by determining electrical diffusivity values of cardiac tissue based on a distance of the cardiac tissue from the endocardium. A distance field from the endocardium surface is calculated with sub-grid accuracy using a nested-level set approach. Cardiac electrophysiology for the patient is simulated using a cardiac electrophysiology model with the electrical diffusivity values determined to model the Purkinje network of the patient.

Abstract: A system and method for visualization of cardiac changes under various pacing conditions for intervention planning and guidance is disclosed. A patient-specific anatomical heart model is generated based on medical image data of a patient. A patient-specific computational model of heart function is generated based on patient-specific anatomical heart model. A virtual intervention is performed at each of a plurality of positions on the patient-specific anatomical heart model using the patient-specific computational model of heart function to calculate one or more cardiac parameters resulting from the virtual intervention performed at each of the plurality of positions. One or more outcome maps are generated visualizing, at each of the plurality of positions on the patient-specific anatomical heart model, optimal values for the one or more cardiac parameters resulting from the virtual intervention performed at the that position on the patient-specific anatomical heart model.

Abstract: A mechanical property of anatomy is estimated from a patient in vivo, such as estimating a patient-specific material property of a valve. A morphological model is used to determine anatomy dynamics. A biomechanical model, using the anatomy dynamics, predicts the dynamics, based, at least in part, on one or more material properties. Using an inverse solution based on comparison of dynamics predicted by the biomechanical model and the dynamics determined from the morphological model, values for the material properties are determined.

Abstract: A computer-implemented method for deriving biopsy results in a non-invasive manner includes acquiring a plurality of training data items. Each training data item comprises non-invasive patient data and one or more biopsy derived scores associated with an individual. The method further includes extracting a plurality of features from the non-invasive patient data based on the one or more biopsy derived scores and training a predictive model to generate a predicted biopsy score based on the plurality of features and the one or more biopsy derived scores.

Abstract: A method for guiding electrophysiology (EP) intervention using a patient-specific electrophysiology model includes acquiring a medical image of a patient subject (S201). Sparse EP signals are acquired over an anatomy using the medical image for guidance (S202). The sparse EP signals are interpolated using a patient specific computational electrophysiology model and a three-dimensional model of EP dynamics is generated therefrom (S203). A rendering of the three-dimensional model is displayed. Candidate intervention sites are received, effects on the EP dynamics resulting from intervention at the candidate intervention sites is simulated using the model, and a rendering of the model showing the simulated effects is displayed (S205).

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: In a method for image guided prostate cancer needle biopsy, a first registration is performed to match a first image of a prostate to a second image of the prostate. Third images of the prostate are acquired and compounded into a three-dimensional (3D) image. The prostate in the compounded 3D image is segmented to show its border. A second registration and then a third registration different from the second registration is performed on distance maps generated from the prostate borders of the first image and the compounded 3D image, wherein the first and second registrations are based on a biomechanical property of the prostate. A region of interest in the first image is mapped to the compounded 3D image or a fourth image of the prostate acquired with the second modality.

Abstract: A method of computing physiological measurements resulting from a multi-scale physiological system using a data-driven model includes generating a database of physiological measurements associated with a multi-scale physiological system. A computer uses dimensionality reduction techniques on the database to identify a reduced set of components explaining the multi-scale physiological system. The computer learns a data-driven model of the multi-scale physiological system from the database. Then, new input parameters are received by the computer and used to compute new physiological measurements using the data-driven model. New derived physiological indicators are computed by the computer based on the reduced set of components. Once computed, the new derived physiological indicators may be displayed along with the new physiological measurements.

Abstract: A method and system for automatic non-invasive estimation of shear modulus and viscosity of biological tissue from shear-wave imaging is disclosed. Shear-wave images are acquired to evaluate the mechanical properties of an organ of a patient. Shear-wave propagation in the tissue in the shear-wave images is simulated based on shear modulus and viscosity values for the tissue using a computational model of shear-wave propagation. The simulated shear-wave propagation is compared to observed shear-wave propagation in the shear-wave images of the tissue using a cost function. Patient-specific shear modulus and viscosity values for the tissue are estimated to optimize the cost function comparing the simulated shear-wave propagation to the observed shear-wave propagation.

Abstract: A method and system for integrating radiological and pathological information for cancer diagnosis, therapy selection, and monitoring is disclosed. A radiological image of a patient, such as a magnetic resonance (MR), computed tomography (CT), positron emission tomography (PET), or ultrasound image, is received. A location corresponding to each of one or more biopsy samples is determined in the at least one radiological image. An integrated display is used to display a histological image corresponding to the each biopsy samples, the radiological image, and the location corresponding to each biopsy samples in the radiological image. Pathological information and radiological information are integrated by combining features extracted from the histological images and the features extracted from the corresponding locations in the radiological image for cancer grading, prognosis prediction, and therapy selection.

Abstract: A method and system for estimating arterial compliance and resistance based on medical image data and pressure measurements is disclosed. An arterial inflow estimate over a plurality of time points is determined based on medical image data of a patient. An arterial pressure measurement of the patient is received. At least one cardiac cycle of the arterial pressure measurement is synchronized with at least one cardiac cycle of the arterial inflow measurement. Arterial compliance and resistance of the patient is estimated based on the arterial inflow estimate and the synchronized arterial pressure measurement.

Abstract: A method and system for patient-specific planning and guidance of electrophysiological interventions is disclosed. A patient-specific anatomical heart model is generated from cardiac image data of a patient. A patient-specific cardiac electrophysiology model is generated based on the patient-specific anatomical heart model and patient-specific electrophysiology measurements. Virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. A simulated electrocardiogram (ECG) signal is calculated in response to each virtual electrophysiological intervention.

Abstract: A method and system for blood flow velocity reconstruction from medical image data is disclosed. Flow system geometry of a flow conduit is generated from medical image data. The flow system velocity includes an inlet, walls, and one or more outlets of the flow conduit. A measured velocity field is extracted from the medical image data. Inlet and wall fluxes are estimated based on the measured velocity field or other external measurements. Outlet fluxes are estimated such that mass conservation is constrained based on the inlet and wall fluxes. A reconstructed velocity field is calculated by solving flux-constrained Poisson (FCP) equations that are constrained by the estimated output fluxes.

Abstract: A method of identifying an optimum treatment for a patient suffering from coronary artery disease, comprising: (i) providing patient information selected from: (a) status in the patient of one or more coronary disease associated biomarkers; (b) one or more items of medical history information selected from prior condition history, intervention history and medication history; (c) one or more items of diagnostic history, if the patient has a diagnostic history; and (d) one or more items of demographic data; (ii) aggregating the patient information in: (a) a Bayesian network; (b) a machine learning and neural network; (c) a rule-based system; and (d) a regression-based system; (iii) deriving a predicted probabilistic adverse event outcome for each intervention comprising percutaneous coronary intervention by placement of a bare metal stent, or a drug-coated stent; or by coronary artery bypass grafting; and (iv) determining the intervention having the lowest predicted probabilistic adverse outcome.

Abstract: A method and system for patient-specific simulation of cardiac electrophysiology is disclosed. A patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific cardiac electrophysiology model is generated based on simulated torso potentials and body surface potential measurements of the patient. Cardiac electrophysiology of the patient is simulated over time for the patient-specific anatomical heart model using the patient-specific electrophysiology model. One or more electrophysiology maps are generated based on the cardiac electrophysiology simulated using the patient-specific cardiac electrophysiology model.