Optimal Diffusion Tensor Imaging with Repeated Measurements
Paper in proceedings, 2013

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.

optimal sampling scheme

diffusion tensor imaging

tensor estimation

Author

Mohammad Alipoor

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Irene Yu-Hua Gu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Andrew Mehnert

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

Y Lilja

University of Gothenburg

Daniel Nilsson

University of Gothenburg

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 8149 Pt 1 687-694

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Information Science

Neurology

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing

DOI

10.1007/978-3-642-40811-3_86

ISBN

978-3-642-40810-6

More information

Created

10/8/2017