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A blob-based tomographic reconstruction of 3D coronary trees from rotational x-ray angiography

Abstract : A method is proposed for a 3D reconstruction of coronary networks from rotational projections that departs from motion-compensated approaches. It deals with multiple views extracted from a time-stamped image sequence through ECG gating. This statistics-based vessel reconstruction method relies on a new imaging model by considering both the effect of background tissues and the image representation using spherically-symmetric basis functions, also called 'blobs'. These blobs have a closed analytical expression for the X-ray transform, which makes easier to compute a cone-beam projection than a voxel-based description. A Bayesian maximum a posteriori (MAP) estimation is used with a Poisson distributed projection data instead of the Gaussian approximation often used in tomography reconstruction. A heavy-tailed distribution is proposed as image prior to take into account the sparse nature of the object of interest. The optimization is performed by an expectation-maximization like (EM) block iterative algorithm which offers a fast convergence and a sound introduction of the non-negativity constraint for vessel attenuation coefficients. Simulations are performed using a model of coronary tree extracted from multidetector CT scanner and a performance study is conducted. They point out that, even with severe angular undersampling (6 projections over 110 degrees for instance) and without introducing a prior model of the object, significant results can be achieved.
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Contributor : Lotfi Senhadji <>
Submitted on : Tuesday, October 28, 2008 - 7:24:27 PM
Last modification on : Thursday, January 14, 2021 - 11:25:20 AM
Long-term archiving on: : Tuesday, October 9, 2012 - 2:35:57 PM


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Jian Zhou, Alexandre Bousse, Guanyu Yang, Jean-Jacques Bellanger, Limin Luo, et al.. A blob-based tomographic reconstruction of 3D coronary trees from rotational x-ray angiography. Medical Imaging 2008: Physics of Medical Imaging, Feb 2008, San Diego, CA, United States. pp.69132N-12, ⟨10.1117/12.769478⟩. ⟨inserm-00335244⟩



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