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12 March 2014 Efficient feature-based 2D/3D registration of transesophageal echocardiography to x-ray fluoroscopy for cardiac interventions
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We present a novel 2D/ 3D registration algorithm for fusion between transesophageal echocardiography (TEE) and X-ray fluoroscopy (XRF). The TEE probe is modeled as a subset of 3D gradient and intensity point features, which facilitates efficient 3D-to-2D perspective projection. A novel cost-function, based on a combination of intensity and edge features, evaluates the registration cost value without the need for time-consuming generation of digitally reconstructed radiographs (DRRs). Validation experiments were performed with simulations and phantom data. For simulations, in silica XRF images of a TEE probe were generated in a number of different pose configurations using a previously acquired CT image. Random misregistrations were applied and our method was used to recover the TEE probe pose and compare the result to the ground truth. Phantom experiments were performed by attaching fiducial markers externally to a TEE probe, imaging the probe with an interventional cardiac angiographic x-ray system, and comparing the pose estimated from the external markers to that estimated from the TEE probe using our algorithm. Simulations found a 3D target registration error of 1.08(1.92) mm for biplane (monoplane) geometries, while the phantom experiment found a 2D target registration error of 0.69mm. For phantom experiments, we demonstrated a monoplane tracking frame-rate of 1.38 fps. The proposed feature-based registration method is computationally efficient, resulting in near real-time, accurate image based registration between TEE and XRF.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Charles R. Hatt, Michael A. Speidel, and Amish N. Raval "Efficient feature-based 2D/3D registration of transesophageal echocardiography to x-ray fluoroscopy for cardiac interventions", Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90361J (12 March 2014);

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