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 ◽  
...  

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.

2021 ◽  
Author(s):  
Fenghua Guo ◽  
Chantal M. W. Tax ◽  
Alberto De Luca ◽  
Max A. Viergever ◽  
Anneriet Heemskerk ◽  
...  

NeuroImage ◽  
2020 ◽  
Vol 222 ◽  
pp. 117197 ◽  
Author(s):  
Qiuyun Fan ◽  
Aapo Nummenmaa ◽  
Thomas Witzel ◽  
Ned Ohringer ◽  
Qiyuan Tian ◽  
...  

2021 ◽  
Author(s):  
Philippe Karan ◽  
Alexis Reymbaut ◽  
Guillaume Gilbert ◽  
Maxime Descoteaux

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.


2018 ◽  
Author(s):  
Kurt G Schilling ◽  
Yurui Gao ◽  
Iwona Stepniewska ◽  
Vaibhav Janve ◽  
Bennett A Landman ◽  
...  

AbstractUnderstanding the relationship between the diffusion-weighted MRI signal and the arrangement of white matter fibers is fundamental for accurate voxel-wise reconstruction of the fiber orientation distribution (FOD) and subsequent fiber tractography. Spherical deconvolution reconstruction techniques model the diffusion signal as the convolution of the FOD with a response function which represents the signal profile of a single fiber orientation. Thus, given the signal and a fiber response function, the FOD can be estimated in every imaging voxel by deconvolution. However, the selection of the appropriate response function remains relatively un-studied, and requires further validation. In this work, using 3D histologically-defined FODs and the corresponding diffusion signal from three ex vivo squirrel monkey brains, we derive the ground truth response functions. We find that the histologically-derived response functions differ from those conventionally used. Next, we find that response functions statistically vary across brain regions, which suggests that the practice of using the same kernel throughout the brain is not optimal. Additionally, response functions vary significantly across subjects. We show that different kernels lead to different FOD reconstructions, which in turn can lead to different tractography results depending on algorithmic parameters, with large variations in the accuracy of resulting reconstructions. Together, this suggests that there is room for improvement in estimating and understanding the relationship between the diffusion signal and the underlying FOD.


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