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Báo cáo hóa học: " A full bi-tensor neural tractography algorithm using the unscented Kalman filter"

Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : A full bi-tensor neural tractography algorithm using the unscented Kalman filter | Lienhard et al. EURASIP Journal on Advances in Signal Processing 2011 2011 77 http asp.eurasipjournals.eom content 2011 1 77 o EURASIP Journal on Advances in Signal Processing a SpringerOpen Journal RESEARCH Open Access A full bi-tensor neural fractography algorithm using the unscented Kalman filter Stefan Lienhard1 James G Malcolm2 Carl-Frederik Westin3 and Yogesh Rathi2 Abstract We describe a technique that uses tractography to visualize neural pathways in human brains by extending an existing framework that uses overlapping Gaussian tensors to model the signal. At each point on the fiber an unscented Kalman filter is used to find the most consistent direction as a mixture of previous estimates and of the local model. In our previous framework the diffusion ellipsoid had a cylindrical shape i.e. the diffusion tensor s second and third eigenvalues were identical. In this paper we extend the tensor representation so that the diffusion tensor is represented by an arbitrary ellipsoid. Experiments on synthetic data show a reduction in the angular error at fiber crossings and branchings. Tests on in vivo data demonstrate the ability to trace fibers in areas containing crossings or branchings and the tests also confirm the superiority of using a full tensor representation over the simplified model. 1 Introduction Diffusion-weighted magnetic resonance imaging has provided the opportunity for non-invasive investigation of neural architecture of the brain. Neuroscientists use this imaging technique to find out how neurons originating from one region in the brain connect to other regions and how well-defined those connections are. The quality of the results of such studies relies heavily on the chosen fiber representation and the reconstruction method to trace neural pathways. For studying the microstructure of fibers we need a model to interpret the diffusion-weighted signal. There are two main categories for such models parametric and non-parametric models. The simplest .