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Data acquisition modeling and hybrid coronary tree 3D reconstruction in C-arm CBCT imaging

Abstract : The rotational angiography RX of the coronaries is a standard modality to determine the degree and the number of the coronaries stenosis. The objective of this dissertation aims at improving the 3D reconstruction of the coronary arteries, which can improve the diagnosis, the security and the precision of the minimal invasive interventions.For the first part, the major contribution is improving the calibration procedure of the rotational R-X imaging system. First, we propose a new calibration algorithm based on the classical helical phantom on the Artis-Zeego system. Second, we transfer the geometries to the C-arm coordinate system. Last, we propose the movement models of the projection geometries objectively and systematically at 3 representative work positions. The movement models simplify the clinical procedures. The experiment results indicate that the proposed movement models have an acceptable precision to estimate the acquisition parameters.For the second part work, the major contribution is proposing a new reconstruction method by motion compensation. The steps of the reconstruction method include: the forward projection, the segmentation of the acquired projection, registration, the initial and motion compensated reconstruction. We adopt the advanced Simplified Distance Driven projector to generate the forward projection. We use the mutual information (MI) and rigidity penalty (RP) to be the similarity measure. We adopt the advanced Adaptive Stochastic Gradient Descent (ASGD) to realize the optimization. The initial and the compensated reconstruction are based on the MAP iterative reconstruction. The experiment results indicate that the proposed method improves the quality of the 3D reconstruction. The contrast and the details of the coronary arteries are improved by the proposed motion compensation reconstruction method.
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Submitted on : Thursday, June 14, 2018 - 4:38:17 PM
Last modification on : Wednesday, September 14, 2022 - 10:20:04 AM
Long-term archiving on: : Monday, September 17, 2018 - 10:58:30 AM


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  • HAL Id : tel-01816039, version 1


Si Li. Data acquisition modeling and hybrid coronary tree 3D reconstruction in C-arm CBCT imaging. Medical Imaging. Université Rennes 1, 2017. English. ⟨NNT : 2017REN1S133⟩. ⟨tel-01816039⟩



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