Apparent Intravoxel Fibre Population Dispersion (FPD) Using Spherical Harmonics

Assemlal, Haz-Edine; Campbell, Jennifer S. W.; Pike, Bruce; Siddiqi, Kaleem

doi:10.1007/978-3-642-23629-7_20

Cited by 7 publications

(7 citation statements)

References 13 publications

(21 reference statements)

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“…A different model that is based on the generation of subvoxel fanning/bending grids is presented in (Nedjati-Gilani and Alexander, 2009) and preliminary results on inferring a single fanning population are shown. An approach to quantify intravoxel fibre dispersion, caused by either crossing or fanning, has been recently presented, that is based on the autocorrelation function of the spherical signal or features of it, such as the ODF (Assemlal et al, 2011). …”

Section: Introductionmentioning

confidence: 99%

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI

Sotiropoulos

Behrens²,

Jbabdi³

2012

NeuroImage

141

156

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A number of methods have been proposed for resolving crossing fibres from diffusion-weighted (DW) MRI. However, other complex fibre geometries have drawn minimal attention. In this study, we focus on fibre orientation dispersion induced by within-voxel fanning. We use a multi- compartment, model-based approach to estimate fibre dispersion. Bingham distributions are employed to represent continuous distributions of fibre orientations, centred around a main orientation, and capturing anisotropic dispersion. We evaluate the accuracy of the model for different simulated fanning geometries, under different acquisition protocols and we illustrate the high SNR and angular resolution needs. We also perform a qualitative comparison between our parametric approach and five popular non-parametric techniques that are based on orientation distribution functions (ODFs). This comparison illustrates how the same underlying geometry can be depicted by different methods. We apply the proposed model on high-quality, post-mortem macaque data and present whole-brain maps of fibre dispersion, as well as exquisite details on the local anatomy of fibre distributions in various white matter regions.

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Section: Introductionmentioning

confidence: 99%

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI

Sotiropoulos

Behrens²,

Jbabdi³

2012

NeuroImage

141

156

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“…Recently, the authors in (Assemlal et al, 2011) used the SPFI signal basis to compute the novel fiber population dispersion (FPD), an index which assess the presence of crossing fibers within a voxel. The FPD, however, is a relatively new measure that has not yet been computed for an actual human brain.…”

Section: Introductionmentioning

confidence: 99%

Bessel Fourier Orientation Reconstruction (BFOR): An analytical diffusion propagator reconstruction for hybrid diffusion imaging and computation of q-space indices

Hosseinbor

Chung

et al. 2013

NeuroImage

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The ensemble average propagator (EAP) describes the 3D average diffusion process of water molecules, capturing both its radial and angular contents. The EAP can thus provide richer information about complex tissue microstructure properties than the orientation distribution function (ODF), an angular feature of the EAP. Recently, several analytical EAP reconstruction schemes for multiple q-shell acquisitions have been proposed, such as diffusion propagator imaging (DPI) and spherical polar Fourier imaging (SPFI). In this study, a new analytical EAP reconstruction method is proposed, called Bessel Fourier orientation reconstruction (BFOR), whose solution is based on heat equation estimation of the diffusion signal for each shell acquisition, and is validated on both synthetic and real datasets. A significant portion of the paper is dedicated to comparing BFOR, SPFI, and DPI using hybrid, non-Cartesian sampling for multiple b-value acquisitions. Ways to mitigate the effects of Gibbs ringing on EAP reconstruction are also explored. In addition to analytical EAP reconstruction, the aforementioned modeling bases can be used to obtain rotationally invariant q-space indices of potential clinical value, an avenue which has not yet been thoroughly explored. Three such measures are computed: zero-displacement probability (Po), mean squared displacement (MSD), and generalized fractional anisotropy (GFA).

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“…This relationship is useful as SPF (Assemlal et al, 2009) is a now a state-of-the-art method in diffusion MRI. We can therefore reconstruct the Ensemble Average Propagator (EAP) following Cheng et al (2010b), the Orientation Distribution Function (ODF) following Cheng et al (2010a), or the apparent fiber population dispersion following Assemlal et al (2011). The SPF basis is built on Laguerre polynomials L 1=2 n while we use L 5=2 n in this work.…”

Section: Link With the Spf Basismentioning

confidence: 99%

Diffusion MRI signal reconstruction with continuity constraint and optimal regularization

Caruyer

Deriche

2012

Medical Image Analysis

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In diffusion MRI, the reconstruction of the full Ensemble Average Propagator (EAP) provides new insights in the diffusion process and the underlying microstructure. The reconstruction of the signal in the whole Q-space is still extremely challenging however. It requires very long acquisition protocols, and robust reconstruction to cope with the very low SNR at large b-values. Several reconstruction methods were proposed recently, among which the Spherical Polar Fourier (SPF) expansion, a promising basis for signal reconstruction. Yet the reconstruction in SPF is still subject to noise and discontinuity of the reconstruction. In this work, we present a method for the reconstruction of the diffusion attenuation in the whole Q-space, with a special focus on continuity and optimal regularization. We derive a modified Spherical Polar Fourier (mSPF) basis, orthonormal and compatible with SPF, for the reconstruction of a signal with continuity constraint. We also derive the expression of a Laplace regularization operator in the basis, together with a method based on generalized cross validation for the optimal choice of the parameter. Our method results in a noticeable dimension reduction as compared with SPF. Tested on synthetic and real data, the reconstruction with this method is more robust to noise and better preserves fiber directions and crossings.

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Apparent Intravoxel Fibre Population Dispersion (FPD) Using Spherical Harmonics

Cited by 7 publications

References 13 publications

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI

Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI

Bessel Fourier Orientation Reconstruction (BFOR): An analytical diffusion propagator reconstruction for hybrid diffusion imaging and computation of q-space indices

Diffusion MRI signal reconstruction with continuity constraint and optimal regularization

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