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 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 tomogram of a body is provided. Projection-image data obtained by a radiation-based projection method is used for providing the tomogram. Initial voxel data are first specified for a plurality of voxels of the body. Synthetic projection-image data are generated based upon a projection rule modeling a course of the projection method. Projection-error data is determined by comparing the synthetic projection-image data with the real projection-image data. The projection-error data are imaged on the basis of a back-projection rule dependent on the projection rule so that voxel-error data are produced. Correction data is generated from the voxel-error data by a gradient-based optimizing algorithm, wherein corrected voxel data are generated using the correction data.

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: 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 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 flow diverter is automatically detected from medical imaging data. The appearance of the flow diverter as represented in the data as well as the geometry or shape of the flow diverter is used in the detection. Using scoring for appearance relative to the centerline and cross-section of the centerline, the flow diverter is detected for increasing visualization.