Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography

TL;DR

This paper provides comprehensive guidelines and best practices for ex vivo preclinical diffusion MRI, emphasizing data processing, validation with microscopy, and tractography, to improve research reproducibility and accuracy.

Contribution

It offers detailed recommendations for ex vivo diffusion MRI, including image processing, model fitting, and tractography, highlighting gaps and future directions in the field.

Findings

Best practices for ex vivo dMRI data processing

Guidelines for validation with microscopy

Recommendations for data and code sharing

Abstract

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging.