Parametric Representation of Multiple White Matter Fascicles from Cube and Sphere Diffusion MRI

Abstract

The characterization of the complex diffusion signal arising from the brain remains an open problem. Many representations focus on characterizing the global shape of the diffusion profile at each voxel and are limited to the assessment of connectivity. In contrast, Multiple Fascicle Models (MFM) seek to represent the contribution from each white matter fascicle and may be useful in the investigation of both white matter connectivity and diffusion properties of each individual fascicle. However, the most appropriate representation of multiple fascicles remains unclear. In particular, a multiple tensor representation of multiple fascicles has frequently been reported to be numerically challenging and unstable. We provide here the first analytical demonstration that when using a diffusion MRI acquisition with only one non-zero b-value, such as in conventional single-shell HARDI acquisition, a co-linearity in model parameters makes the precise model estimation impossible. Motivated by this theoretical result, we propose the novel CUSP (CUbe and SPhere) optimal acquisition scheme to achieve multiple non-zero b-values. It combines the gradients of a single-shell HARDI with gradients in its enclosing cube, in which varying b-values can be acquired by modulation of the gradient strength, without modifying the minimum echo time. Compared to a multi-shell HARDI acquisition, our scheme has significantly increased signal-to-noise ratio. We propose a novel estimation algorithm that enables efficient, robust and accurate estimation of the parameters of a multi-tensor model. In conjunction with a CUSP acquisition, it enables full estimation of the multi-tensor model. We present an evaluation of CUSP-MFM on both synthetic phantoms and invivo data. We report qualitative and quantitative experimental evaluations which demonstrate the ability of CUSP-MFM to characterize multiple fascicles from short duration acquisitions. CUSP-MFM enables rapid and effective investigation of multiple white matter fascicles, in both normal development and in disease and injury, in research and clinical practice.

Quantifying white matter tract diffusion parameters in the presence of increased extra-fiber cellularity and vasogenic edema

“…For instance, diffusion spectrum imaging (DSI) resolves crossing or branching fibers by direct evaluation of diffusion displacement probability density function which is the inverse Fourier transform of the diffusion weighted signals, but typically requires a large number of measurements with extensive diffusion weighting (Wedeen et al, 2005); diffusion kurtosis imaging (DKI) quantifies the non- Gaussian diffusion by estimating apparent diffusion kurtosis of diffusion displacement probability distribution (Jensen et al, 2005); generalized diffusion tensor imaging (gDTI) models the white matter tract via higher order tensors (Liu et al, 2004); composite hindered and restricted model of diffusion (CHARMED) evaluates an extra-cellular compartment (assigned to hindered diffusion resulting from extra-axonal diffusion weighted signal) and intra-cellular compartments (assigned to restricted diffusion in a cylinder representing individual intra-axonal space) employing a comprehensive diffusion weighting scheme (Assaf and Basser, 2005). Recently, Scherrer et al proposed multiple fascicle models (MFM) to model an isotropic compartment (assigned to free water diffusion) and multiple anisotropic compartments (assigned to single fascicle) using a cube and sphere (CUSP) acquisition scheme (Scherrer and Warfield, 2012). Zhang et al proposed neurite orientation dispersion and density imaging (NODDI) to model tissue components.…”

Diffusion MRI abnormalities detection with orientation distribution functions: A multiple sclerosis longitudinal study

“…Such an extensive comparison would bring further insight into our approach. Our comparison framework is also generic and could be applied to multi-compartment models such as multi-tensor (Scherrer and Warfield (2012)) or DDI (Stamm et al (2012)), which may be more adapted to low angular acquisitions and do not consider only the directional component of the diffusion.…”

“…Moreover, it has been demonstrated that the tensor model is not sufficient to capture regions of crossing fibers in the brain, as it allows to code only for one principal direction of diffusion when there may be several. Recently, several papers proposed higher order diffusion models able to capture several diffusion directions (Assaf and Basser (2005); Descoteaux et al (2007); Scherrer and Warfield (2012); Stamm et al (2012); Zhang et al (2012)). Utilizing the full information coming from those diffusion models could potentially improve further the detection power of abnormalities in patients.…”

Optimized magnetic resonance diffusion protocol for ex-vivo whole human brain imaging with a clinical scanner

“…Characterization of both the connectivity and of microstructural properties with diffusion compartment imaging (DCI) requires the acquisition of multiple non-zero b-values to disentangle the signal arising from each compartment in each voxel. 14,18,19 Varying the b-value in a pulse gradient spin echo (PGSE) DW-MRI sequence can be achieved by either modification of the diffusion pulse gradient duration δ, the separation time ∆ between the pulses or the norm of the applied diffusion sensitization gradient ||g||, as described by Le Bihan:…”

“…17 However, DCI requires the acquisition of multiple non-zero b-values to distinguish between the decay curves of each compartment in each voxel. 14,18,19 In this work, we considered the specific requirements of post-mortem imaging and, building upon previous ex-vivo protocols proposed in the literature, 20, 21 designed an optimized, multiple b-value protocol for ex-vivo whole brain DCI using a human clinical 3T scanner. Human clinical 3T scanners are available to a large number of researchers and, unlike most animal scanners, have a bore diameter large enough to image a whole human brain.…”