DTI registration in atlas based fiber analysis of infantile Krabbe disease

Abstract

In recent years, diffusion tensor imaging (DTI) has become the modality of choice to investigate white matter pathology in the developing brain. To study neonate Krabbe disease with DTI, we evaluate the performance of linear and non-linear DTI registration algorithms for atlas based fiber tract analysis. The DTI scans of 10 age-matched neonates with infantile Krabbe disease are mapped into an atlas for the analysis of major fiber tracts -the genu and splenium of the corpus callosum, the internal capsules tracts and the uncinate fasciculi. The neonate atlas is based on 377 healthy control subjects, generated using an unbiased diffeomorphic atlas building method. To evaluate the performance of one linear and seven nonlinear commonly used registration algorithms for DTI we propose the use of two novel evaluation metrics: a regional matching quality criterion incorporating the local tensor orientation similarity, and a fiber property profile based metric using normative correlation. Our experimental results indicate that the whole tensor based registration method within the DTI-ToolKit (DTI-TK) shows the best performance for our application.

Improving Registration Using Multi-channel Diffeomorphic Demons Combined with Certainty Maps

“…The same can be found in [1]. In this context it also worth noting that, in a recent evaluation of various registration algorithms on DTI data [15], using both single-variate and multi-variate registration methods, the single-variate Fig. 2.…”

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“…Data was then transformed to use within DTI-TK software (v 2.3.1) (Zhang et al, 2007;Zhang, Yushkevich, Alexander, & Gee, 2006) using the fsl_to_dtitk command. DTI-TK is currently recognized as one of the best available tools for registering dti data (Wang et al, 2011). It takes into account the directional information of the diffusion tensors, which is used to align all subjects' data through rigid, affine, and nonlinear registration steps.…”

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“…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).…”

“…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). …”