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 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 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 Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

2021 ◽  
Author(s):  
Philippe Karan ◽  
Alexis Reymbaut ◽  
Guillaume Gilbert ◽  
Maxime Descoteaux
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Simulated Data ◽  
Orientation Distribution ◽  
Variance Decomposition ◽  
Distribution Functions ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of HARDI data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). However, tensor-valued diffusion MRI data is not compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

scholarly journals Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs)

2019 ◽  
Author(s):  
Alberto De Luca ◽  
Fenghua Guo ◽  
Martijn Froeling ◽  
Alexander Leemans
Keyword(s):  
Fiber Orientation ◽  
Diffusion Mri ◽  
Grey Matter ◽  
State Of The Art ◽  
List Type ◽  
Response Functions ◽  
Fiber Tractography ◽  
Tissue Specific ◽  
Spherical Deconvolution ◽  
Orientation Distributions

AbstractIn diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions. The applicability of spherical deconvolution to grey matter (GM), however, is still limited, despite its critical role as start/endpoint of WM fiber pathways. The advent of multi-shell diffusion MRI data offers additional contrast to model the GM signal but, to date, only isotropic models have been applied to GM. Evidence from both histology and high-resolution diffusion MRI studies suggests a marked anisotropic character of the diffusion process in GM, which could be exploited to improve the description of the cortical organization. In this study, we investigated whether performing spherical deconvolution with tissue specific models of both WM and GM can improve the characterization of the latter while retaining state-of-the-art performances in WM. To this end, we developed a framework able to simultaneously accommodate multiple anisotropic response functions to estimate multiple, tissue-specific, fiber orientation distributions (mFODs). As proof of principle, we used the diffusion kurtosis imaging model to represent the WM signal, and the neurite orientation dispersion and density imaging (NODDI) model to represent the GM signal. The feasibility of the proposed approach is shown with numerical simulations and with data from the Human Connectome Project (HCP). The performance of our method is compared to the current state of the art, multi-shell constrained spherical deconvolution (MSCSD). The simulations show that with our new method we can accurately estimate a mixture of two FODs at SNR≥50. With HCP data, the proposed method was able to reconstruct both tangentially and radially oriented FODs in GM, and performed comparably well to MSCSD in computing FODs in WM. When performing fiber tractography, the trajectories reconstructed with mFODs reached the cortex with more spatial continuity and for a longer distance as compared to MSCSD and allowed to reconstruct short trajectories tangential to the cortical folding. In conclusion, we demonstrated that our proposed method allows to perform spherical deconvolution of multiple anisotropic response functions, specifically improving the performances of spherical deconvolution in GM tissue.HighlightsWe introduce a novel framework to perform spherical deconvolution with multiple anisotropic response functions (mFOD)We show that the proposed framework can be used to improve the FOD estimation in the cortical grey matterFiber tractography performed with mFOD reaches the cortical GM with more coverage and contiguity than with previous methodsThe proposed framework is a first step towards GM to GM fiber tractography

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