Histological validation of diffusion MRI fiber orientation distributions and dispersion

Abstract

Diffusion magnetic resonance imaging (dMRI) is widely used to probe tissue microstructure, and is currently the only non-invasive way to measure the brain's fiber architecture. While a large number of approaches to recover the intra-voxel fiber structure have been utilized in the scientific community, a direct, 3D, quantitative validation of these methods against relevant histological fiber geometries is lacking. In this study, we investigate how well different high angular resolution diffusion imaging (HARDI) models and reconstruction methods predict the ground-truth histologically defined fiber orientation distribution (FOD), as well as investigate their behavior over a range of physical and experimental conditions. The dMRI methods tested include constrained spherical deconvolution (CSD), Q-ball imaging (QBI), diffusion orientation transform (DOT), persistent angular structure (PAS), and neurite orientation dispersion and density imaging (NODDI) methods. Evaluation criteria focus on overall agreement in FOD shape, correct assessment of the number of fiber populations, and angular accuracy in orientation. In addition, we make comparisons of the histological orientation dispersion with the fiber spread determined from the dMRI methods. As a general result, no HARDI method outperformed others in all quality criteria, with many showing tradeoffs in reconstruction accuracy. All reconstruction techniques describe the overall continuous angular structure of the histological FOD quite well, with good to moderate correlation (median angular correlation coefficient > 0.70) in both single- and multiple-fiber voxels. However, no method is consistently successful at extracting discrete measures of the number and orientations of FOD peaks. The major inaccuracies of all techniques tend to be in extracting local maxima of the FOD, resulting in either false positive or false negative peaks. Median angular errors are ∼10° for the primary fiber direction and ∼20° for the secondary fiber, if present. For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value). Regardless of acquisition parameters, all methods show improved successes at resolving multiple fiber compartments in a voxel when fiber populations cross at near-orthogonal angles, with no method adequately capturing low to moderate angle (<60°) crossing fibers. Finally, most methods are limited in their ability to capture orientation dispersion, resulting in low to moderate, yet statistically significant, correlation with histologically-derived dispersion with both HARDI and NODDI methodologies. Together, these results provide quantitative measures of the reliability and limitations of dMRI reconstruction methods and can be used to identify relative advantages of competing approaches as well as potential strategies for improving accuracy.

Histologically derived fiber response functions for diffusion MRI vary across white matter fibers—An ex vivo validation study in the squirrel monkey brain

“…This study focused on single fiber voxels in the white matter, in order to avoid contamination or partial volume effects from other compartments—including gray matter or multiple intra‐voxel fiber populations. However, the dataset collected in Reference additionally contains a number of z stacks that contain crossing fibers (defined as voxels having multiple local maxima in the FOD). Figure shows example z stacks with crossing fibers (which were not used in the current study), highlighting location, shape of the FOD shown in two views (to highlight crossing and show both in‐plane and through‐plane fiber orientations), and the derived response function.…”

“…The FOD is defined as the distribution of these orientation estimates. This procedure has been validated against manually traced fibers, and has been used to assess the accuracy of diffusion MRI reconstruction techniques …”

Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances

“…Although bias from anisotropic depth sampling and resolution in confocal microscopy must be carefully avoided, the resulting true 3D orientation information permits even more detailed validation of dMRI‐derived FODs, especially in the out‐of‐plane direction. These techniques have recently been leveraged to compare a broad range of diffusion‐based models, demonstrating the strengths and weaknesses of different approaches, as well as the current shortcomings of all current models (for example, robust and accurate estimation of multiple peaks in FOD) . This brings the information gained from STA closer to techniques that derive 3D orientation from the optical birefringence of myelin, such as optical coherence tomography (OCT) and polarized light imaging (PLI) .…”

“…These techniques have recently been leveraged to compare a broad range of diffusion-based models, demonstrating the strengths and weaknesses of different approaches, as well as the current shortcomings of all current models (for example, robust and accurate estimation of multiple peaks in FOD). 62 This brings the information gained from STA closer to techniques that derive 3D orientation from the optical birefringence of myelin, such as optical coherence tomography (OCT) [63][64][65] and polarized light imaging (PLI). 66 Wang et al 65 employed a serial optical coherence scanner (SOCS), a tissue slicer integrated with multi-contrast OCT, and reported that fiber orientations showed good agreement between the DTI and SOCS measures; however, DTI did not capture complex (multi-orientation) patterns as well as SOCS.…”

Time‐dependent diffusion in undulating thin fibers: Impact on axon diameter estimation

“…(A) Images of axons, which inspired our undulating thin fiber model. Axons in the corpus callosum, optic nerve, and phrenic nerve exhibit ubiquitously sinusoidal undulation patterns. (B) Underlying sine wave parameters used in the simulations (amplitude a , wavelength λ , and discretization into segments with variable angle θ).…”