Maryam Afzali1, Markus Nilsson2, Marco Palombo3, and Derek K Jones1
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 2Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden, 3Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
1Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom, 2Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden, 3Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
Soma and Neurite Density Imaging (SANDI) was recently proposed to disentangle neurite and soma compartments. In this work, three main challenges of this model were identified; Rician noise floor, empirical lower bound, and estimation of cylinder and sphere size simultaneously.
Fig. 2 (a) The results of fitting the sphere radius in stick + ball + sphere model for different sphere signal fractions (GT = Ground Truth and E = Estimated). The figure also shows the p-value of the F-test in the presence of Gaussian, Rician, and corrected Rician noise respectively. (b) Estimated sphere and cylinder radii versus the ground truth sphere radius values for cylinder + ball + sphere model without noise (SNR = 200). The third row in (b) shows the reduced chi-square values for two scenarios where the sphere radius is fixed to 5 and 8 μm, blue and red curves respectively.
Fig. 4 Estimated stick (fstick), ball (fball), and sphere (fsphere) signal fractions, intra-axonal parallel diffusivity ($$$D_{\rm{in}}^{\mid\mid} (\mu m^2/ms)$$$), extra-cellular diffusivity ($$$D_{\rm{ec}} (\mu m^2/ms)$$$), sphere radius ($$$R_{\rm{sphere}} (\mu m)$$$), and standard deviation of the noise ($$$\sigma$$$) on axial, sagittal, and coronal views of the smoothed brain image (A 3D Gaussian kernel with standard deviation of 0.5 is used for smoothing).