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Optimal Diffusion Tensor Imaging with Repeated Measurements

Conference paper
Authors Mohammad Alipoor
Irene Y.H. Gu
Andrew Mehnert
Y Lilja
Daniel Nilsson
Published in Lecture Notes in Computer Science: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013. 16th International Conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part I
Volume 8149
Pages 687-694
ISBN 978-3-642-40810-6
ISSN 0302-9743
Publication year 2013
Published at Institute of Neuroscience and Physiology, Department of Clinical Neuroscience and Rehabilitation
Pages 687-694
Language en
Links dx.doi.org/10.1007/978-3-642-40811-...
Keywords diffusion tensor imaging, optimal sampling scheme, tensor estimation
Subject categories Information processing, Computer Vision and Robotics (Autonomous Systems), Image analysis, Medical Image Processing, Neurology

Abstract

Several data acquisition schemes for diffusion MRI have been proposed and explored to date for the reconstruction of the 2nd order tensor. Our main contributions in this paper are: (i) the definition of a new class of sampling schemes based on repeated measurements in every sampling point; (ii) two novel schemes belonging to this class; and (iii) a new reconstruction framework for the second scheme. We also present an evaluation, based on Monte Carlo computer simulations, of the performances of these schemes relative to known optimal sampling schemes for both 2nd and 4th order tensors. The results demonstrate that tensor estimation by the proposed sampling schemes and estimation framework is more accurate and robust.

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