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
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.
Cite
@article{arxiv.2411.05021,
title = {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},
author = {Kurt G Schilling and Amy FD Howard and Francesco Grussu and Andrada Ianus and Brian Hansen and Rachel L C Barrett and Manisha Aggarwal and Stijn Michielse and Fatima Nasrallah and Warda Syeda and Nian Wang and Jelle Veraart and Alard Roebroeck and Andrew F Bagdasarian and Cornelius Eichner and Farshid Sepehrband and Jan Zimmermann and Lucas Soustelle and Christien Bowman and Benjamin C Tendler and Andreea Hertanu and Ben Jeurissen and Marleen Verhoye and Lucio Frydman and Yohan van de Looij and David Hike and Jeff F Dunn and Karla Miller and Bennett A Landman and Noam Shemesh and Adam Anderson and Emilie McKinnon and Shawna Farquharson and Flavio Dell' Acqua and Carlo Pierpaoli and Ivana Drobnjak and Alexander Leemans and Kevin D Harkins and Maxime Descoteaux and Duan Xu and Hao Huang and Mathieu D Santin and Samuel C. Grant and Andre Obenaus and Gene S Kim and Dan Wu and Denis Le Bihan and Stephen J Blackband and Luisa Ciobanu and Els Fieremans and Ruiliang Bai and Trygve B Leergaard and Jiangyang Zhang and Tim B Dyrby and G Allan Johnson and Julien Cohen-Adad and Matthew D Budde and Ileana O Jelescu},
journal= {arXiv preprint arXiv:2411.05021},
year = {2024}
}
Comments
Part 3 of 3 in "Considerations and recommendations for preclinical diffusion MRI"