Acquisition Guidelines and Quality Assessment Tools for Analyzing Neonatal Diffusion Tensor MRI Data

Abstract

SUMMARY:Diffusion tensor imaging is a valuable measure in clinical settings to assess diagnosis and prognosis of neonatal brain development. However, obtaining reliable images is not straightforward because of the tissue characteristics of the neonatal brain and the high likelihood of motion artifacts. In this review, we present guidelines on how to acquire DTI data of the neonatal brain and recommend high-quality data acquisition and processing as an essential means to obtain accurate and robust parametric maps. Sudden head movements are problematic for DTI in neonates, and these may lead to incorrect values. We describe strategies to minimize the corrupting effects both in terms of acquisition (eg, more gradient directions) and postprocessing (eg, tensor estimation methods). In addition, tools are described that can help assess whether a dataset is of sufficient quality for further assessment. ABBREVIATIONS:MD ϭ mean diffusivity; FA ϭ fractional anisotropy; RESTORE ϭ robust estimation of tensors by outlier rejection; TBSS ϭ tract-based spatial

Choice of Diffusion Tensor Estimation Approach Affects Fiber Tractography of the Fornix in Preterm Brain

“…24 Moreover, preterm infants are vulnerable to respiratory and hemodynamic instability and movement artifacts. 7,9 Therefore, acquisition and processing of DTI data must be handled with dedicated care, which is essential to avoid misinterpretation. This is appropriately described in technical DTI reports, [5][6][7]25,26 but, paradoxically, such work receives little attention in clinical research.…”

“…7,9 Therefore, acquisition and processing of DTI data must be handled with dedicated care, which is essential to avoid misinterpretation. This is appropriately described in technical DTI reports, [5][6][7]25,26 but, paradoxically, such work receives little attention in clinical research. This could be because of their emphasis on clinical results, or, more importantly, because of the lack of awareness that the choice of diffusion tensor estimation approach may affect the subsequent reconstruction of fiber pathways.…”

“…Therefore, before associations between tractography results and neurodevelopmental outcome can be established, it is of paramount importance that acquisition and processing of DTI data are performed with the highest standards possible. 7,8 For example, different algorithms to estimate the diffusion tensor have been developed. These methods differ considerably in processing speed and dealing with data outliers.…”

A Robust Post-Processing Workflow for Datasets with Motion Artifacts in Diffusion Kurtosis Imaging

“…During the acquisition of DKI datasets, the head motion is common, especially in the pediatric subjects and patients with involuntary movements. The inter-volume misalignment caused by the slight head motion may be adjusted by using registration methods [4], [5]. However, the severe signal loss caused by the sudden tissue displacement with a large amplitude during the diffusion MRI scanning cannot be recovered [1], [4].…”

“…The inter-volume misalignment caused by the slight head motion may be adjusted by using registration methods [4], [5]. However, the severe signal loss caused by the sudden tissue displacement with a large amplitude during the diffusion MRI scanning cannot be recovered [1], [4]. Therefore, a post-processing workflow to correct motion-corrupted datasets is of great importance for the clinical application of DKI, also the same for DTI and high angular resolution diffusion imaging (HARDI), etc.…”