Abstract: A medical system is provided for three-dimensional hemodynamic quantification. Comprehensive three-dimensional (3D) plus time (3D+t) assessment of flow patterns inside the heart are provided by a combination of lumped-parameter modeling and computational flow dynamic modeling. Using medical scanning, the lumped parameter model is personalized to a given patient. The personalized lumped-parameter model provides pressure curves (i.e., pressure as a function of time) for one or more locations. Using geometry of the patients heart segmented from the medical scanning and the pressure curves as boundary conditions, the computational flow dynamics model calculates the absolute pressure for any location (e.g., for a three-dimensional field of locations) in the patient heart at any one or more phases of the cardiac cycle. More accurate absolute pressure may be provided without invasive measurement.

Abstract: A processor acquires image data from a medical imaging system. The processor generates a first model from the image data. The processor generates a computational model which includes cardiac electrophysiology and cardiac mechanics estimated from the first model. The processor performs tests on the computational model to determine outcomes for therapies. The processor overlays the outcome on an interventional image. Using interventional imaging, the first heart model may be updated/overlaid during the therapy to visualize its effect on a patient's heart.

Abstract: A method and system for estimating physiological heart measurements from medical images and clinical data disclosed. A patient-specific anatomical model of the heart is generated from medical image data of the patient. A patient-specific multi-physics computational heart model is generated based on the patient-specific anatomical model by personalizing parameters of a cardiac electrophysiology model, a cardiac biomechanics model, and a cardiac hemodynamics model based on medical image data and clinical measurements of the patient. Cardiac function of the patient is simulated using the patient-specific multi-physics computational heart model. The parameters can be personalized by inverse problem algorithms based on forward model simulations or the parameters can be personalized using a machine-learning based statistical model.

Abstract: A method and system for real-time ultrasound guided prostate needle biopsy based on a biomechanical model of the prostate from 3D planning image data, such as magnetic resonance imaging (MRI) data, is disclosed. The prostate is segmented in the 3D ultrasound image. A reference patient-specific biomechanical model of the prostate extracted from planning image data is fused to a boundary of the segmented prostate in the 3D ultrasound image, resulting in a fused 3D biomechanical prostate model. In response to movement of an ultrasound probe to a new location, a current 2D ultrasound image is received. The fused 3D biomechanical prostate model is deformed based on the current 2D ultrasound image to match a current deformation of the prostate due to the movement of the ultrasound probe to the new location.

Abstract: A method and system for prediction of respiratory motion from 3D thoracic images is disclosed. A patient-specific anatomical model of the respiratory system is generated from 3D thoracic images of a patient. The patient-specific anatomical model of the respiratory system is deformed using a biomechanical model. The biomechanical model is personalized for the patient by estimating a patient-specific thoracic pressure force field to drive the biomechanical model. Respiratory motion of the patient is predicted using the personalized biomechanical model driven by the patient-specific thoracic pressure force field.

Abstract: Robotic navigation is provided for nuclear probe imaging. Using a three-dimensional scanner (19), the surface of a patient is determined (42). A calibrated robotic system positions (48) a nuclear probe about the patient based on the surface. The positioning (48) may be without contacting the patient and the surface may be used in reconstruction to account for spacing of the probe from the patient. By using the robotic system for positioning (48), the speed, resolution and/or quality of the reconstructed image may be predetermined, user settable, and/or improved compared to manual scanning. The reconstruction (52) may be more computationally efficient by providing for regular spacing of radiation detection locations within the volume.

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: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Abstract: A method and system for patient-specific planning and guidance of an ablation procedure for cardiac arrhythmia is disclosed. A patient-specific anatomical heart model is generated based on pre-operative cardiac image data. The patient-specific anatomical heart model is registered to a coordinate system of intra-operative images acquired during the ablation procedure. One or more ablation site guidance maps are generated based on the registered patient-specific anatomical heart model and intra-operative patient-specific measurements acquired during the ablation procedure. The ablation site guidance maps may include myocardium diffusion and action potential duration maps. The ablation site guidance maps are generated using a computational model of cardiac electrophysiology which is personalized by fitting parameters of the cardiac electrophysiology model using the intra-operative patient-specific measurements. The ablation site guidance maps are displayed by a display device during the ablation procedure.

Abstract: Patient specific temperature distribution in organs, due to an ablative device, is simulated. The effects of ablation are modeled. The modeling is patient specific. The vessel structure for a given patient, segmented from medical images, is accounted for as a heat sink in the model of biological heat transfer. A temperature map is generated to show the effects of ablation in a pre-operative analysis. Temperature maps resulting from different ablation currents and ablation device positions may be used to determine a more optimal location of the ablative device for a given patient. Other models may be included, such as accounting for the tissue damage during the ablation.

Abstract: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Abstract: A method and system for interactive patient-specific simulation of liver tumor ablation is disclosed. A patient-specific anatomical model of the liver and circulatory system of the liver is estimated from 3D medical image data of a patient. A computational domain is generated from the patient-specific anatomical model of the liver. Blood flow in the liver and the circulatory system of the liver is simulated based on the patient-specific anatomical model. Heat diffusion due to ablation is simulated based on a virtual ablation probe position and the simulated blood flow in the liver and the circulatory system of the liver by solving a bio-heat equation for each node on the level-set representation using a Lattice-Boltzmann method (LBM) implementation. Cellular necrosis in the liver is computed based on the simulated heat diffusion. Visualizations of a computed necrosis region and temperature maps of the liver are generated.

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: One or more of the present embodiments include systems and methods for utilizing an MRI image and real-time an ultrasound images to guide and/or restrict the movement of an ultrasound probe in position for collecting a biopsy core. A real-time ultrasound image is acquired and fused with pre-operative imaging modalities, such as an MRI image, to provide a three-dimensional model of the prostate. A multi-link robotic arm is provided with an end-effector and an ultrasound probe mounted thereto. Sensor information is used to track the ultrasound probe position with respect to the 3D model. The robotic arm allows for the implementation of a virtual remote center of motion (VRCM) about the transrectal probe tip, an adjustable compliant mode for the physician triggered movement of probe, a restrictive trajectory of joints of the robotic arm and active locking for stationary imaging of the prostate.

Abstract: A method and system for simulating cardiac function of a patient is disclosed. A patient-specific anatomical model of at least a portion of the patient's heart is generated from medical image data of the patient. Cardiac electrophysiology potentials are calculated over a computational domain defined by the patient-specific anatomical model for each of a plurality of time steps using a patient-specific cardiac electrophysiology model. The electrophysiology potentials acting on a plurality of nodes of the computational domain are calculated in parallel for each time step. Biomechanical forces over the computational domain for each of the plurality of time steps using a cardiac biomechanical model coupled to the cardiac electrophysiology model. The biomechanical forces acting on a plurality of nodes of the mesh domain are estimated in parallel for each time step. Blood flow and cardiac movement are computed at each of the plurality of time steps based on the calculated biomechanical forces.

Abstract: A method (10) to compensate for cardiac and respiratory motion in cardiac imaging during minimal invasive (e.g., trans-catheter) AVI procedures by image-based tracking (20, 25) on fluoroscopic images.

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 method and system for image-based patient-specific guidance of cardiac arrhythmia therapies 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 the patient-specific anatomical heart model and electrophysiology measurements of the patient. One or more virtual electrophysiological interventions are performed using the patient-specific cardiac electrophysiology model. One or more pacing targets or ablation targets based on the one or more virtual electrophysiological interventions are displayed.

Abstract: Method and system for computation of advanced heart measurements from medical images and data; and therapy planning using a patient-specific multi-physics fluid-solid heart model is disclosed. A patient-specific anatomical model of the left and right ventricles is generated from medical image patient data. A patient-specific computational heart model is generated based on the patient-specific anatomical model of the left and right ventricles and patient-specific clinical data. The computational model includes biomechanics, electrophysiology and hemodynamics. To generate the patient-specific computational heart model, initial patient-specific parameters of an electrophysiology model, initial patient-specific parameters of a biomechanics model, and initial patient-specific computational fluid dynamics (CFD) boundary conditions are marginally estimated.

Abstract: A method and system for estimating tissue parameters of a computational model of organ function and their uncertainty due to model assumptions, data noise and optimization limitations is disclosed. As applied to a cardiac use-case, a patient-specific anatomical heart model is generated from medical image data of a patient. A patient-specific computational heart model is generated based on the patient-specific anatomical heart model. Patient-specific parameters and corresponding uncertainty values are estimated for at least a subset of parameters of the patient-specific computational heart model. A surrogate model is estimated for a forward model of cardiac function, and the surrogate model is applied within Bayesian inference to estimate the posterior probability density function of the parameter space of the forward model. Cardiac function for the patient is simulated using the patient-specific computational heart model.