scholarly journals Axon diameter index estimation independent of fiber orientation distribution using high-gradient diffusion MRI

NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117197 ◽  
Author(s):  
Qiuyun Fan ◽  
Aapo Nummenmaa ◽  
Thomas Witzel ◽  
Ned Ohringer ◽  
Qiyuan Tian ◽  
...  
Keyword(s):  
Fiber Orientation ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Axon Diameter ◽  
High Gradient ◽  
Gradient Diffusion ◽  
Fiber Orientation Distribution ◽  
Index Estimation

scholarly journals Fiber orientation distribution from diffusion MRI: Effects of inaccurate response function calibration

2021 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M. W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  
Keyword(s):  
Response Function ◽  
Fiber Orientation ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Fiber Orientation Distribution

scholarly journals High-gradient diffusion MRI reveals distinct estimates of axon diameter index within different white matter tracts in the in vivo human brain

2019 ◽  
Vol 225 (4) ◽  
pp. 1277-1291 ◽  
Author(s):  
Susie Y. Huang ◽  
Qiyuan Tian ◽  
Qiuyun Fan ◽  
Thomas Witzel ◽  
Barbara Wichtmann ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Axon Diameter ◽  
White Matter Tracts ◽  
High Gradient ◽  
Gradient Diffusion

scholarly journals Effects of Inaccurate Response Function Calibration on Characteristics of the Fiber Orientation Distribution in Diffusion MRI

2019 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M.W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  
Keyword(s):  
Response Function ◽  
Fiber Orientation ◽  
Shape Factor ◽  
Diffusion Mri ◽  
Orientation Distribution ◽  
Fiber Tracking ◽  
Fiber Tractography ◽  
Scaling Factors ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution

AbstractDiffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Constrained spherical deconvolution requires to define – or derive from the data – a response function, which is used to compute the fiber orientation distribution (FOD). This definition or derivation is not unequivocal and can thus result in different characteristics of the response function which are expected to affect the FOD computation and the subsequent fiber tracking. In this work, we explored the effects of inaccuracies in the shape and scaling factors of the response function on the FOD characteristics. With simulations, we show that underestimation of the shape factor in the response functions has a larger effect on the FOD peaks than overestimation of the shape factor, whereas the latter will cause more spurious peaks. Moreover, crossing fiber populations with a smaller separation angle were more sensitive to the response function inaccuracy than fiber populations with more orthogonal separation angles. Furthermore, the FOD characteristics show deviations as a result of modified shape and scaling factors of the response function. Results with the in vivo data demonstrate that the deviations of the FODs and spurious peaks can further deviate the termination of propagation in fiber tracking. This work highlights the importance of proper definition of the response function and how specific calibration factors can affect the FOD and fiber tractography results.

scholarly journals Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors

2021 ◽  
Vol 7 (11) ◽  
pp. 226
Author(s):  
Marica Pesce ◽  
Audrey Repetti ◽  
Anna Auría ◽  
Alessandro Daducci ◽  
Jean-Philippe Thiran ◽  
...  
Keyword(s):  
Fiber Orientation ◽  
Diffusion Mri ◽  
Angular Resolution ◽  
Real Data ◽  
Orientation Distribution ◽  
Accurate Identification ◽  
Fiber Orientation Distribution ◽  
Under Sampling ◽  
The Cost ◽  
Anatomical Priors

High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide accurate identification of complex neuronal fiber configurations, albeit, at the cost of long acquisition times. We propose a method to recover intra-voxel fiber configurations at high spatio-angular resolution relying on a 3D kq-space under-sampling scheme to enable accelerated acquisitions. The inverse problem for the reconstruction of the fiber orientation distribution (FOD) is regularized by a structured sparsity prior promoting simultaneously voxel-wise sparsity and spatial smoothness of fiber orientation. Prior knowledge of the spatial distribution of white matter, gray matter, and cerebrospinal fluid is also leveraged. A minimization problem is formulated and solved via a stochastic forward–backward algorithm. Simulations and real data analysis suggest that accurate FOD mapping can be achieved from severe kq-space under-sampling regimes potentially enabling high spatio-angular resolution dMRI in the clinical setting.

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