Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging

Abstract

Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.

Keywords: acquisition; best practices; diffusion MRI; diffusion tensor; microstructure; open science; preclinical; processing; small animal; tractography.

FIGURE 1

Four areas in which preclinical brain imaging adds value to the field of dMRI. It enables: (A) correlation with histology on the same subject/sample; (B) the acquisition of richer datasets than on clinical systems thanks to more advanced hardware and longer scan times available; (C) the study of tissue changes with disease and treatment in a more controlled setting; and (D) comparative anatomy between species. Figures reused and adapted from (A),, (B),, , , (C), (D).

FIGURE 2

dMRI brain images of small animal models demonstrating different brain sizes, geometric complexity, gyrification, and tissue constituents, ordered by increasing complexity. Different tissue types are estimated using multi‐shell multi‐tissue spherical deconvolution and color coded—CSF (red), gray matter (green), and white matter (blue). In vivo data: mouse, rat, human. Ex vivo data: raven, squirrel monkey, macaque. Data kindly provided by Adam Anderson, Ileana Jelescu, Kurt Schilling, Ben Jeurissen, and Marleen Verhoye.

FIGURE 3

Small animal in vivo protocols require decisions regarding hardware, animal preparation and monitoring, and acquisition (which include encoding, readout, spatial resolution, and q‐t coverage).

FIGURE 4

Diffusion encoding (left) and readout (right). Pulse sequence diagrams are shown for a variety of representative encoding (Section 3.3) and readout (Section 3.4) schemes. Left: RF pulses are represented as hollow waveforms, diffusion gradients as dark color filled shapes (colors represent encoding axes), and the readout module as pale blue. Slice selection gradients are not shown, for simplicity. Right: RF pulses are represented as hollow waveforms, gradients are in dark gray or thick lines. Two axes are shown for phase‐encode and read‐out gradient directions.

FIGURE 5

Illustration of a Brain Imaging Data Structure (BIDS) structured dataset (right) starting from vendor‐specific convention of data organization (left). From Gorgolewski et al.