摘要:
A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.
摘要:
A method and system for generating a patient specific anatomical heart model is disclosed. A sequence of volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. A multi-component patient specific 4D geometric model of the heart and aorta estimated from the sequence of volumetric cardiac imaging data. A patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model is generated. Patient specific material properties of the aortic wall are estimated using the 4D geometrical model and the 4D computational model. Fluid Structure Interaction (FSI) simulations are performed using the 4D computational model and estimated material properties of the aortic wall, and patient specific clinical parameters are extracted based on the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.
摘要:
A method and system for generating a patient specific anatomical heart model is disclosed. Volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. Individual models for multiple heart components, such as the left ventricle (LV) endocardium, LV epicardium, right ventricle (RV), left atrium (LA), right atrium (RA), mitral valve, aortic valve, aorta, and pulmonary trunk, are estimated in said volumetric cardiac image data. A multi-component patient specific anatomical heart model is generated by integrating the individual models for each of the heart components. Fluid Structure Interaction (FSI) simulations are performed on the patient specific anatomical model, and patient specific clinical parameters are extracted based on the patient specific heart model and the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.
摘要:
A method and system for generating a patient specific anatomical heart model is disclosed. A sequence of volumetric image data, such as computed tomography (CT), echocardiography, or magnetic resonance (MR) image data of a patient's cardiac region is received. A multi-component patient specific 4D geometric model of the heart and aorta estimated from the sequence of volumetric cardiac imaging data. A patient specific 4D computational model based on one or more of personalized geometry, material properties, fluid boundary conditions, and flow velocity measurements in the 4D geometric model is generated. Patient specific material properties of the aortic wall are estimated using the 4D geometrical model and the 4D computational model. Fluid Structure Interaction (FSI) simulations are performed using the 4D computational model and estimated material properties of the aortic wall, and patient specific clinical parameters are extracted based on the FSI simulations. Disease progression modeling and risk stratification are performed based on the patient specific clinical parameters.
摘要:
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.
摘要:
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.
摘要:
A method and system for tumor ablation planning and guidance based on a patient-specific model 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. 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 venous system of the liver. Cellular necrosis in the liver is simulated based on the simulated heat diffusion. A visualization of a simulated necrosis region is generated and displayed to the user for decision making and optimal therapy planning and guidance.
摘要:
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.
摘要:
A method and system for tumor ablation planning and guidance based on a patient-specific model 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. 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 venous system of the liver. Cellular necrosis in the liver is simulated based on the simulated heat diffusion. A visualization of a simulated necrosis region is generated and displayed to the user for decision making and optimal therapy planning and guidance.
摘要:
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.