LONI MiND: Metadata in NIfTI for DWI

A wide range of computational methods have been developed for reconstructing white matter geometry from a set of diffusion-weighted images (DWIs), and many clinical studies rely on publicly-available implementations of these methods for analyzing DWI datasets. Unfortunately, the poor interoperability between DWI analysis tools often effectively restricts users to the algorithms provided by a single software suite, which may be suboptimal relative to those in other packages, or outdated given recent developments in the field. A major barrier to data portability and the interoperability between DWI analysis tools is the lack of a standard format for representing and communicating essential DWI-related metadata at various stages of post-processing. In this report, we address this issue by developing a framework for storing metadata in NIfTI for DWI (MiND). We utilize the standard NIfTI format extension mechanism to store essential DWI metadata in an extended header for multiple commonly-encountered DWI data structures. We demonstrate the utility of this approach by implementing a full suite of tools for DWI analysis workflows which communicate solely through the MiND mechanism. We also show that the MiND framework allows for simple, direct DWI data visualization, and we illustrate its effectiveness by constructing a group atlas for 330 subjects using solely MiND-centric tools for DWI processing. Our results indicate that the MiND framework provides a practical solution to the problem of interoperability between DWI analysis tools, and it effectively expands the analysis options available to end users.