Diffusion Capillary Phantom vs. Human Data: Outcomes for Reconstruction Methods Depend on Evaluation Medium.

James H. Bishop,Fang-Cheng Yeh,Sarah D. Lichenstein,Timothy D. Verstynen

doi:10.3389/fnins.2016.00407

Abstract

Purpose: Diffusion MRI provides a non-invasive way of estimating structural connectivity in the brain. Many studies have used diffusion phantoms as benchmarks to assess the performance of different tractography reconstruction algorithms and assumed that the results can be applied to in vivo studies. Here we examined whether quality metrics derived from a common, publically available, diffusion phantom can reliably predict tractography performance in human white matter tissue.Materials and Methods: We compared estimates of fiber length and fiber crossing among a simple tensor model (diffusion tensor imaging), a more complicated model (ball-and-sticks) and model-free (diffusion spectrum imaging, generalized q-sampling imaging) reconstruction methods using a capillary phantom and in vivo human data (N = 14).Results: Our analysis showed that evaluation outcomes differ depending on whether they were obtained from phantom or human data. Specifically, the diffusion phantom favored a more complicated model over a simple tensor model or model-free methods for resolving crossing fibers. On the other hand, the human studies showed the opposite pattern of results, with the model-free methods being more advantageous than model-based methods or simple tensor models. This performance difference was consistent across several metrics, including estimating fiber length and resolving fiber crossings in established white matter pathways.Conclusions: These findings indicate that the construction of current capillary diffusion phantoms tends to favor complicated reconstruction models over a simple tensor model or model-free methods, whereas the in vivo data tends to produce opposite results. This brings into question the previous phantom-based evaluation approaches and suggests that a more realistic phantom or simulation is necessary to accurately predict the relative performance of different tractography reconstruction methods.

Highlights

Diffusion MRI is an increasingly popular imaging approach for visualizing in vivo white matter architecture
Phantom Fiber Length Results Fiber tracking with the diffusion tensor imaging (DTI) reconstruction was unable to detect any streamlines passing through the long leg of the phantom that navigated both crossings
Analysis of variance for the four reconstruction methods that were able to detect streamlines within this arm of the phantom demonstrated that there is a significant group difference in the number of streamlines resolved with different reconstruction methods [F(3,36) = 138362.57, p < 0.001]

Summary

Introduction

Diffusion MRI is an increasingly popular imaging approach for visualizing in vivo white matter architecture. The diffusion signals can reveal such an anisotropy that can be used as a proxy for the general orientation of the fiber, making it possible to draw inferences about the microstructural properties of white matter pathways (Hagmann et al, 2006; Jones et al, 2013). This information has been applied to study normal white matter connectivity as well as to elucidate the neural basis of various forms of clinical pathology (Seizeur et al, 2012; Jones et al, 2013), including multiple sclerosis (Filippi and Rocca, 2011), stroke (Sotak, 2002), Alzheimer’s disease (Amlien and Fjell, 2014), and a variety of neuropsychiatric conditions such as schizophrenia, mood, and anxiety disorders (White et al, 2008). It is important to delineate the relative strengths and weaknesses of different diffusion imaging analysis techniques in order to help researchers select the most appropriate methods when designing studies of white matter architecture

Methods

Results

Discussion

Conclusion