Optimal Diffusion Tensor Imaging with Repeated Measurements
Paper i proceeding, 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

tensor estimation

diffusion tensor imaging

Författare

Mohammad Alipoor

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Irene Yu-Hua Gu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Andrew Mehnert

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik

Y Lilja

Göteborgs universitet

Daniel Nilsson

Göteborgs universitet

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
978-3-642-40810-6 (ISBN)

Styrkeområden

Livsvetenskaper och teknik (2010-2018)

Ämneskategorier

Systemvetenskap

Neurologi

Datorseende och robotik (autonoma system)

Medicinsk bildbehandling

DOI

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

Mer information

Senast uppdaterat

2021-10-05