Optimal real-time estimation in diffusion tensor imaging
Pablo Casaseca-de-la-Higuera, Antonio Tristan-Vega, Santiago Aja-Fernandez, Carlos Alberola-Lopez, C.-F. Westin, R. San Jose Estepar
Magnetic Resonance Imaging
Volume , Number 0, Pages 1-12
2012
DOI: 10.1016/j.mri.2011.12.001
Abstract
Diffusion tensor imaging (DTI) constitutes the most used paradigm among the diffusion-weighted magnetic resonance imaging (DW- MRI) techniques due to its simplicity and application potential. Recently, real-time estimation in DW-MRI has deserved special attention, with several proposals aiming at the estimation of meaningful diffusion parameters during the repetition time of the acquisition sequence. Specifically focusing on DTI, the underlying model of the noise present in the acquired data is not taken into account, leading to a suboptimal estimation of the diffusion tensor. In this paper, we propose an optimal real-time estimation framework for DTI reconstruction in single- coil acquisitions. By including an online estimation of the time-changing noise variance associated to the acquisition process, the proposed method achieves the sequential best linear unbiased estimator. Results on both synthetic and real data show that our method outperforms those so far proposed, reaching the best performance of the existing proposals by processing a substantially lower number of diffusion images.
Acknowledgement
This work was partially supported by the Ministerio de Ciencia e Innovación and the Fondo Europeo de Desarrollo Regional (FEDER) under Research Grant TEC2010-17982 and by the Centro para el Desarrollo Tecnológico Industrial (CDTI) under the cvREMOD (CEN-20091044) project. The work was also funded by the Junta de Castilla y León under Grants VA376A11-2 and VA039A10-2. A. Tristán-Vega was supported by FMECD-2010/71131616E (Ministerio de Educacíon, Spain/Fulbright Committee). C.-F. Westin was supported by NIH grants R01MH074794, R01MH092862, and P41RR013218. R. San José was supported by NIH award number K25HL104085. The authors would also like to acknowledge the company Q Diagnóstica for the research agreement with the University of Valladolid (2011–2013), through which most MRI acquisitions of the LPI are currently obtained.
Keywords
Diffusion tensor imaging; Real-time processing; Optimal sequential estimation; Best linear unbiased estimator (BLUE); Log-Rician distributionReference
Casaseca-de-la-Higuera P, Tristan-Vega A, Aja-Fernandez S, Alberola-Lopez C, Westin CF, Estepar RSJ. Optimal real-time estimation in diffusion tensor imaging. Magnetic Resonance Imaging 2012;(0):1-12.Bibtex entry
@article{CasasecadelaHigueraMRM2012,
title = "Optimal real-time estimation in diffusion tensor imaging",
journal = "Magnetic Resonance Imaging",
volume = "",
number = "0",
pages = "1--12",
year = "2012",
note = "",
issn = "0730-725X",
doi = "10.1016/j.mri.2011.12.001",
url = "http://www.sciencedirect.com/science/article/pii/S0730725X1
1004632",
author = "Pablo {Casaseca-de-la-Higuera} and Antonio Tristan-Vega and
Santiago Aja-Fernandez and Carlos Alberola-Lopez and
Carl-Fredrik Westin and Raul San Jose Estepar",
keywords = "Diffusion tensor imaging",
keywords = "Real-time processing",
keywords = "Optimal sequential estimation",
keywords = "Best linear unbiased estimator (BLUE)",
keywords = "Log-Rician distribution"}
