A hitchhiker's guide to diffusion tensor imaging

TLDR

DTI is increasingly used to map brain connectivity, but optimizing its use requires careful decisions about acquisition, artifact handling, quality control, reconstruction, visualization, quantitative analysis, and software tools. The authors aim to provide a concise guide that covers all major stages of the DTI analysis workflow. The guide systematically outlines each stage of the DTI pipeline, from acquisition to analysis, offering practical recommendations. The guide assists newcomers in overcoming critical roadblocks and promotes broader adoption of DTI.

Abstract

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.