High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis

Abstract

Spatial normalization of diffusion tensor images plays a key role in voxel-based analysis of white matter (WM) group differences. Currently, it has been achieved using low-dimensional registration methods in the large majority of clinical studies. This paper aims to motivate the use of high-dimensional normalization approaches by generating evidence of their impact on the findings of such studies. Using an ongoing amyotrophic lateral sclerosis (ALS) study, we evaluated three normalization methods representing the current range of available approaches: low-dimensional normalization using the fractional anisotropy (FA), high-dimensional normalization using the FA, and high-dimensional normalization using full tensor information. Each method was assessed in terms of its ability to detect significant differences between ALS patients and controls. Our findings suggest that inadequate normalization with low-dimensional approaches can result in insufficient removal of shape differences which in turn can confound FA differences in a complex manner, and that utilizing high-dimensional normalization can both significantly minimize the confounding effect of shape differences to FA differences and provide a more complete description of WM differences in terms of both size and tissue architecture differences. We also found that high-dimensional approaches, by leveraging full tensor features instead of tensor-derived indices, can further improve the alignment of WM tracts.

Complex regional pain syndrome: The matter of white matter?

“…Compared with FA‐intensity‐based registrations, the tensor‐based DTI‐TK registration technique, as used in the present study (see Section 2), improves the alignment of different brains and thereby the detection of group differences (Van Hecke et al., 2007; Wang et al., 2011; Zhang et al., 2007). The differences of these registration methods can affect the detection of group differences with TBSS e.g., in the cingulum bundle (Bach et al., 2014).…”

“…Tensors were estimated on the corrected data within the brain mask using FSL's dtifit and the resulting images were converted into DTI‐TK (http://www.nitrc.org/projects/dtitk) format for tensor‐based spatial normalization (Zhang et al., 2007). First, population‐specific tensor template was bootstrapped using the IXI aging DTI template (Zhang, Yushkevich, Rueckert, & Gee, 2010).…”

“…Finally, aligned individual tensor images were wrapped to the population‐specific template in standard space. This tensor‐based normalization has been shown to be superior in detecting white‐matter differences compared with low‐dimensional registration using scalar values, such as FA (Wang et al., 2011; Zhang et al., 2007). …”

Tensor-Based Morphometry of Fibrous Structures with Application to Human Brain White Matter

“…We selected a total of 35 subjects (16 males and 19 females) with the following criteria: 65 years or older, scanned at the same site, and with available DTI data of sufficient quality. To spatially normalize the data, we applied the approach described in [8] which simultaneously constructs a populationspecific DTI template from the subject data and normalizes the subjects to the resulting template. The approach is based on high-dimensional tensor-based image registration and has been shown to outperform scalar-based registration.…”

Joint Segmentation and Groupwise Registration of Cardiac Perfusion Images Using Temporal Information

“…This approach is more faithful to the term "groupwise registration." The goal is to simultaneously warp all subjects in a population towards a hidden common space [11,12]. The groupwise registration problem is formulated as one of optimization, with a global cost function defined on all aligned images [13,14].…”