2023
DOI: 10.1016/j.media.2023.102767
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Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI

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Cited by 7 publications
(6 citation statements)
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“…Additionally, the presence of axons with diameters in the transition area may explain the variation we observed in the dMRI results when compared with shrinkage-corrected Epon-TEM. Studies have presented in vivo axon diameter estimations in the human brain 27,35,36,64 with robust rescan findings 65 . Due to the hardware and SNR, those dMRI studies also had a diameter lower bound on a scale of several micrometers 27,34 .…”
Section: Discussionmentioning
confidence: 99%
“…Additionally, the presence of axons with diameters in the transition area may explain the variation we observed in the dMRI results when compared with shrinkage-corrected Epon-TEM. Studies have presented in vivo axon diameter estimations in the human brain 27,35,36,64 with robust rescan findings 65 . Due to the hardware and SNR, those dMRI studies also had a diameter lower bound on a scale of several micrometers 27,34 .…”
Section: Discussionmentioning
confidence: 99%
“…We used data from four subjects of the Human Connectome Project (HCP) Adult Diffusion database to compute the eigenvalues and eigenvectors of the diffusion tensor ( 16 ) via a two-step weighted and iterated least-squares method ( 49 ) as implemented in MRtrix3 ( 23 ). Data were denoised as indicated in ( 50 ) using a Rician variance stabilisation transform ( 51 ) in combination with PCA optimal shrinkage ( 52 ), with subsequent application of Gibbs ringing removal ( 48 ) and eddy current distortion correction ( 53 ). For the estimation of the tensor, we selected only the b=0 and the 64 volume-directions corresponding to b=1000s/mm 2 .…”
Section: Methodsmentioning
confidence: 99%
“…The spherical mean diffusion‐weighted signal Strue‾Diff(b,r)$$ {\overline{S}}_{\mathrm{Diff}}\left(b,r\right) $$ from a cylinder with radius r$$ r $$, in Eq. (), is modeled as Strue‾Difffalse(b,rfalse)=π4exp()Dfalse(rfalse)bnormalerf()bDD(r)bDD(r),$$ {\overline{S}}_{\mathrm{Diff}}\left(b,r\right)=\sqrt{\frac{\pi }{4}}\exp \left(-{D}_{\perp }(r)b\right)\frac{\operatorname{erf}\left(\sqrt{b\left({D}_{\parallel }-{D}_{\perp }(r)\right)}\right)}{\sqrt{b\left({D}_{\parallel }-{D}_{\perp }(r)\right)}}, $$ which is the spherical mean signal equation for an axis‐symmetric diffusion tensor, 2,13,36,37,40,41 where normalerf$$ \operatorname{erf} $$ denotes the error function, and the radial diffusivity D$$ {D}_{\perp } $$ depends on r$$ r $$ according to the van Gelderen model, 38 defined in Eq. () in .…”
Section: Theorymentioning
confidence: 99%
“…which is the spherical mean signal equation for an axis-symmetric diffusion tensor, 2,13,36,37,40,41 where erf denotes the error function, and the radial diffusivity D ⊥ depends on r according to the van Gelderen model, 38 defined in Eq. (A1) in Appendix A.…”
Section: Intra-pore Diffusion-relaxation Mri Modelmentioning
confidence: 99%
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