title: Optimal diffusion tensor imaging with repeated measurements.
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.
authors: Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden. alipoor@chalmers.se
authors: Mohammad
authors: M
authors: Alipoor
authors: Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden.
authors: Irene Yu Hua
authors: IY
authors: Gu
authors: Department of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden.
authors: Andrew J H
authors: AJ
authors: Mehnert
authors: Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
authors: Ylva
authors: Y
authors: Lilja
authors: Institute of Neuroscience and Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
authors: Daniel
authors: D
authors: Nilsson
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D018401:Sample Size; D012680:Sensitivity and Specificity