Abstract: A method and system for transcatheter aortic valve implantation (TAVI) planning is disclosed. An anatomical surface model of the aortic valve is estimated from medical image data of a patient. Calcified lesions within the aortic valve are segmented in the medical image data. A combined volumetric model of the aortic valve and calcified lesions is generated. A 3D printed model of the heart valve and calcified lesions is created using a 3D printer. Different implant device types and sizes can be placed into the 3D printed model of the aortic valve and calcified lesions to select an implant device type and size for the patient for a TAVI procedure. The method can be similarly applied to other heart valves for any type of heart valve intervention planning.

Abstract: Systems and methods for computing uncertainty include generating a surface model of a target anatomical object from medical imaging data of a patient. Uncertainty is estimated at each of a plurality of vertices of the surface model. The uncertainty estimated at each of the plurality of vertices is visualized on the surface model.

Abstract: Computed tomography is used during a minimally invasive intervention. A processor detects the inserted device (e.g., catheter) from a CT scan of the patient. The bed and/or gantry of the CT are moved based on the detected location of the inserted device. As the device is moved within the patient, the automatic detection is used to continue to adjust the CT field of view to scan the device. To further assist the intervention, combining scans with different fields of view relative to the patient may generate an extended field of view image.

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: In valve modeling from medical scan data, chordae are modeled as a dense structure. Rather than attempting to provide the same number of chordae (e.g., 25) as found in a human valve, hundreds or thousands of chordae connectors are used. Since solving for lengths of so many chordae may be computationally intensive, the lengths of only a few are solved, and the lengths of the rest of the chordae are derived from the lengths of the few.

Abstract: Different intracardiac echocardiography volumes are stitched together. Different volumes of a patient are scanned with ICE. To stitch the volumes together, creating a larger volume, the volumes are spatially aligned. The alignment is based on feature, surface, or both feature and surface matching of the ICE volumes with a preoperative model of the same patient. The matching with the model indicates a relative position of the ICE volumes with each other. Using machine-trained classifiers may speed performance, allowing for real-time assembling of a volume from ICE data as the catheter is moved within the patient.

Abstract: A method and system for automated intervention planning for transcatheter aortic valve implantations using computed tomography (CT) data is disclosed. A patient-specific aortic valve model is detected in a CT volume of a patient. The patient-specific aortic valve model is detected by detecting a global location of the patient-specific aortic valve model in the CT volume, detecting aortic valve landmarks based on the detected global location, and fitting an aortic root surface model. Angulation parameters of a C-arm imaging device for acquiring intra-operative fluoroscopic images and anatomical measurements of the aortic valve are automatically determined based on the patient-specific aortic valve model.

Abstract: A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step.

Abstract: A method and system for model based fusion pre-operative image data, such as computed tomography (CT), and intra-operative C-arm CT is disclosed. A first pericardium model is segmented in the pre-operative image data and a second pericardium model is segmented in a C-arm CT volume. A deformation field is estimated between the first pericardium model and the second pericardium model. A model of a target cardiac structure, such as a heart chamber model or an aorta model, extracted from the pre-operative image data is fused with the C-arm CT volume based on the estimated deformation field between the first pericardium model and the second pericardium model. An intelligent weighted average may be used improve the model based fusion results using models of the target cardiac structure extracted from pre-operative image data of patients other than a current patient.

Abstract: A method and system for model-based fusion of pre-operative image data and intra-operative fluoroscopic images is disclosed. A fluoroscopic image and an ultrasound image are received. The ultrasound image is mapped to a 3D coordinate system of a fluoroscopic image acquisition device used to acquire the fluoroscopic image. Contours of an anatomical structure are detected in the ultrasound image, and a transformation is calculated between the ultrasound image and a pre-operative CT image based on the contours and a patient-specific physiological model extracted from the pre-operative CT image. A final mapping is determined between the CT image and the fluoroscopic image based on the transformation between the ultrasound image and physiological model and the mapping of the ultrasound image to the 3D coordinate system of the fluoroscopic image acquisition device. The CT image or the physiological model can then be projected into the fluoroscopic image.

Abstract: A method for real-time fusion of a 2D cardiac ultrasound image with a 2D cardiac fluoroscopic image includes acquiring real time synchronized US and fluoroscopic images, detecting a surface contour of an aortic valve in the 2D cardiac ultrasound (US) image relative to an US probe, detecting a pose of the US probe in the 2D cardiac fluoroscopic image, and using pose parameters of the US probe to transform the surface contour of the aortic valve from the 2D cardiac US image to the 2D cardiac fluoroscopic image.

Abstract: A method and system for automatic aortic valve calcification evaluation is disclosed. A patient-specific aortic valve model in a 3D medical image volume, such as a 3D computed tomography (CT) volume. Calcifications in a region of the 3D medical image volume defined based on the aortic valve model. A 2D calcification plot is generated that shows locations of the segmented calcifications relative to aortic valve leaflets of the patient-specific aortic valve model. The 2D calcification plot can be used for assessing the suitability of a patient for a Transcatheter Aortic Valve Replacement (TAVI) procedure, as well as risk assessment, positioning of an aortic valve implant, and selection of a type of aortic valve implant.

Abstract: A method and system for automated intervention planning for transcatheter aortic valve implantations using computed tomography (CT) data is disclosed. A patient-specific aortic valve model is detected in a CT volume of a patient. The patient-specific aortic valve model is detected by detecting a global location of the patient-specific aortic valve model in the CT volume, detecting aortic valve landmarks based on the detected global location, and fitting an aortic root surface model. Angulation parameters of a C-arm imaging device for acquiring intra-operative fluoroscopic images and anatomical measurements of the aortic valve are automatically determined based on the patient-specific aortic valve model.

Abstract: A method and system for model-based fusion of multi-modal volumetric images is disclosed. A first patient-specific model of an anchor anatomical structure is detected in a first medical image acquired using a first imaging modality, and a second patient-specific model of the anchor anatomical structure is detected in a second medical image acquired using a second imaging modality. A weighted mapping function is determined based on the first patient-specific model of the anchor anatomical structure and the second patient-specific model of the anchor anatomical structure using learned weights to minimize mapping error with respect to a target anatomical structure. The target anatomical structure from the first medical image to the second medical image using the weighted mapping function. In an application of this model-based fusion to transcatheter valve therapies, the trachea bifurcation is used as the anchor anatomical structure and the aortic valve is the target anatomical structure.

Abstract: Different intracardiac echocardiography volumes are stitched together. Different volumes of a patient are scanned with ICE. To stitch the volumes together, creating a larger volume, the volumes are spatially aligned. The alignment is based on feature, surface, or both feature and surface matching of the ICE volumes with a preoperative model of the same patient. The matching with the model indicates a relative position of the ICE volumes with each other. Using machine-trained classifiers may speed performance, allowing for real-time assembling of a volume from ICE data as the catheter is moved within the patient.

Abstract: A method and system for fusion of multi-modal volumetric images is disclosed. A first image acquired using a first imaging modality is received. A second image acquired using a second imaging modality is received. A model and of a target anatomical structure and a transformation are jointly estimated from the first and second images. The model represents a model of the target anatomical structure in the first image and the transformation projects a model of the target anatomical structure in the second image to the model in the first image. The first and second images can be fused based on estimated transformation.

Abstract: A method and system for patient-specific modeling of the whole heart anatomy, dynamics, hemodynamics, and fluid structure interaction from 4D medical image data is disclosed. The anatomy and dynamics of the heart are determined by estimating patient-specific parameters of a physiological model of the heart from the 4D medical image data for a patient. The patient-specific anatomy and dynamics are used as input to a 3D Navier-Stokes solver that derives realistic hemodynamics, constrained by the local anatomy, along the entire heart cycle. Fluid structure interactions are determined iteratively over the heart cycle by simulating the blood flow at a given time step and calculating the deformation of the heart structure based on the simulated blood flow, such that the deformation of the heart structure is used in the simulation of the blood flow at the next time step.