Optimization of a free water elimination two-compartment model for diffusion tensor imaging

Abstract

Diffusion tensor imaging is used to measure the diffusion of water in tissue. The diffusion properties carry information about the relative organization and structure of the underlying tissue. In the case of a single voxel containing both tissue and a fast diffusing component such as free water, a single diffusion tensor is no longer appropriate. A two-tensor free water elimination model has previously been proposed to correct for the case of volume mixing. Here, this model was implemented in a straightforward but novel manner without the use of spatial constraints. The optimal acquisition parameters were investigated through Monte Carlo simulations and human brain imaging studies. At a signal-to-noise ratio of 40 with 64 diffusion-weighted encoding images, the most accurate estimates of fast diffusion signal were obtained with two diffusion-weighted shells (b-value in s/mm^2 x number of directions) of 500×32 and 1500×32. The potential bias in fractional anisotropy induced by this two-compartment model was more than an order of magnitude less than the error of using the single diffusion tensor model in the presence of partial volume effects with free water. This strategy may be useful for characterizing the diffusion of tissues adjacent to cerebral spinal fluid (CSF), tissues affected by edema, and removing artifacts from blurring and ghosting of the CSF signal.

Design and Validation of Diffusion MRI Models of White Matter

“…single shell) is understandable, the ill-posed nature of the mathematical problem and the issue of constraints has been recognized by the authors, who proposed incorporation of an additional low b -value shell for accuracy and stability (Pasternak et al, 2012). The initial, more conventional, multi-shell approach proposed by Pierpaoli and Jones has also been reintroduced in parallel by (Hoy et al, 2014). The latter work included simulations showing that in the absence of a CSF compartment, fitting the FWE model indeed overestimated tissue FA.…”

In vivo imaging of neuroinflammation in schizophrenia

“…The free-water method requires the same data that a DTI acquisition requires (Pasternak et al, 2009), yet other emerging techniques such as multi-shell based free-water (Hoy et al, 2014; Pasternak et al, 2012a), diffusion basis spectrum imaging (DBSI) (Wang et al, 2011), and neurite orientation dispersion and density imaging (NODDI) (Zhang et al, 2012) may provide an even better evaluation of the extracellular space, providing that more elaborate acquisitions are available. These methods could potentially increase both sensitivity and specificity to extracellular changes.…”

Free water elimination improves test–retest reproducibility of diffusion tensor imaging indices in the brain: A longitudinal multisite study of healthy elderly subjects

“…To address this limitation, free water elimination (FWE) methods that include an explicit compartment modeling free-water, have been proposed (Pasternak et al, 2009; Metzler-Baddeley et al, 2012; Hoy et al, 2014; Pasternak et al, 2012a; Baron and Beaulieu, 2014; Zhang et al, 2012). Free water contributions are not only limited to CSF partial volume effects at the border of the ventricles and brain parenchyma, but also found within deep white matter structures, potentially providing additional structural information (Pasternak et al, 2009).…”