Free-water elimination allows one to reduce the bias in DTI metrics induced by partial-volume effects. Unfortunately the fitting problem for this model is ill-conditioned. However, it has been recently demonstrated that the introduction of a second dimension determined by the echo-time, leads to a well-conditioned fitting problem. In this work we investigate the experimental design and data analysis pipeline of such experiments in vivo.
The model we adopt here assumes that the diffusion MRI signal originates from two compartments with different transverse relaxation rates and diffusivities, in the slow-exchange limit1,7 S(TE,b,g)=S0[fwe−TET2,0e−bD0+(1−fw)e−TET2,te−bgTDg],(1) where S0 is the proton density, fw, T2,0 and D0 are the fraction, transverse relaxation time and diffusion coefficient of the free-water compartment and T2,t and D are the transverse relaxation time diffusion tensor for the tissue compartment. The experimentally controlled parameters are the strength and direction of the diffusion weighting gradient, b and g, and TE (Fig.1).
Experiments were performed on a healthy volunteer, in a 3T Siemens Trio scanner (Siemens, Erlangen, Germany). Prior written, informed consent was obtained from the volunteer. A Stejskal-Tanner pulse sequence with monopolar pulse field gradients and EPI readout was implemented for this purpose. The sequence was designed to independently control the gradient pulse duration, δ, and separation, Δ, as well as TE. Thus, the diffusion-dimension and the relaxation-dimension are fully decoupled. Experimental parameters included (Fig. 1): 4 experiments with TE = 70, 100, 130, 170 ms; b-values = 0 (6 repetitions), 500 (20 directions), 1000 (40 directions) s/mm2; Δ = 34 ms and δ = 19 ms. Additionally, we acquired 8 more echo-times, in the range TE ∈ [70, 210] ms. The experiment with TE = 70 ms was acquired twice with opposite phase-encoding (anterior-posterior, posterior-anterior) in order to correct for EPI distortions. Other parameters were voxel-size = 23 mm3; matrix-size = 100×100×60; repetition-time, TR = 15 s and GRAPPA acceleration factor = 2.
Eddy current and EPI distortions were corrected using the EDDY toolkit available in FSL.8 FWET2 DTI parameter estimation was performed in three steps:
[1] Pasternak O, et al. Free Water Elimination and Mapping from Diffusion MRI. Magn Reson Med 2009;62:717-730.
[2] Pasternak O, et al. Estimation of Extracellular Volume from Regularized Multi-Shell Diffusion MRI. Med Image Comput Comput Assist Interv 2012;15:305-312.
[3] Ofori E, et al. Increased free-water in the substantia nigra of Parkinson’s disease: a single-site and multi-site study. Neurobiol Aging 2015;36(2):1097-1104.
[4] Hoy AR, et al. Microstructural white matter alterations in preclinical Alzheimer's disease detected using free water elimination diffusion tensor imaging. PLoS One 2017;14;12(3):e0173982.
[5] Hoy AR, et al. Optimization of a free water elimination two-compartment model for diffusion tensor imaging. Neuroimage 2014;103:323-333.
[6] Neto Henriques R, et al. Exploring the potentials and limitations of improved free-water elimination DTI techniques. Proc Intl Soc Magn Reson Med 2017;1787.
[7] Collier Q, et al. Solving the free water elimination estimation problem by incorporating T2 relaxation properties. Proc Intl Soc Magn Reson Med 2017;1783.
[8] Jesper L, et al. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 2016;125:1063-1078.
[9] Miller AJ, Joseph PM. The use of power images to perform quantitative analysis on low SNR MR images. Magn Reson Imaging 1993;11(7):1051-6.
[10] Piechniket SK, Functional Changes in CSF Volume Estimated Using Measurement of Water T2 Relaxation. Magn Reson Med 2009;61:579-586.
[11] Gras V, et al. Diffusion-Weighted DESS Protocol Optimization for Simultaneous Mapping of the Mean Diffusivity, Proton Density and Relaxation Times at 3 Tesla. Magn Reson Med 2017;78:130-141.