Abstract
ABSTRACTBackground and PurposeDiffusion 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. Spherical deconvolution requires to define––or derive from the data––a response function, which is used to compute the fiber orientation distribution (FOD). Different characteristics of the response function are expected to affect the FOD computation and the subsequent fiber tracking.MethodsIn this work, we explored the effects of inaccuracies in the shape factors of the response function on the FOD characteristics.ResultsWith simulations, we show that the apparent fiber density could be doubled in the presence of underestimated shape factors in the response functions, whereas the overestimation of the shape factor will cause more spurious peaks in the FOD, especially when the signal‐to‐noise ratio is below 15. Moreover, crossing fiber populations with a separation angle smaller than 60° were more sensitive to inaccuracies in the response function than fiber populations with more orthogonal separation angles. Results with in vivo data demonstrate angular deviations in the FODs and spurious peaks as a result of modified shape factors of the response function, while the reconstruction of the main parts of fiber bundles is well preserved.ConclusionsThis work sheds light on how specific aspects of the response function shape can affect the estimated FODs, and highlights the importance of a proper calibration/definition of the response function.
Highlights
Diffusion MRI allows to characterize tissue microstructure in vivo and noninvasively by measuring the anisotropic diffusion of water molecules.[1,2] Diffusion tensor imaging (DTI)[3] is the most widely used model in clinical studies to relate the diffusion MRI signals to the diffusion characteristics of the underlying tissue
The shape factor has an signal to noise ratio (SNR)-dependent effect, as apparent fiber density (AFD) estimated at low SNR deviate from those estimated at high SNR in the presence of shape factor changes (Figure 2D)
The relation between the scaling factor K and the fiber orientation distribution (FOD) characteristics aligns with the theoretical expectations, for the rest of the study, we only show shape factorrelated results
Summary
Diffusion MRI allows to characterize tissue microstructure in vivo and noninvasively by measuring the anisotropic diffusion of water molecules.[1,2] Diffusion tensor imaging (DTI)[3] is the most widely used model in clinical studies to relate the diffusion MRI signals to the diffusion characteristics of the underlying tissue. EFFECTS OF RF CALIBRATION ON FOD CHARACTERISTICS population-specific microstructural measures derived from the magnitudes of the fiber orientation distribution (FOD) functions, such as apparent fiber density (AFD)[12] and hindrance-modulated orientational anisotropy (HMOA).[13]. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Spherical deconvolution requires to define––or derive from the data––a response function, which is used to compute the fiber orientation distribution (FOD). Methods: In this work, we explored the effects of inaccuracies in the shape factors of the response function on the FOD characteristics. Results with in vivo data demonstrate angular deviations in the FODs and spurious peaks as a result of modified shape factors of the response function, while the reconstruction of the main parts of fiber bundles is well preserved. Conclusions: This work sheds light on how specific aspects of the response function shape can affect the estimated FODs, and highlights the importance of a proper calibration/definition of the response function
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