Abstract
Diffusion MRI (dMRI) has become a crucial imaging technique in the field of neuroscience, with a growing number of clinical applications. Although most studies still focus on the brain, there is a growing interest in utilizing dMRI to investigate the healthy or injured spinal cord. The past decade has also seen the development of biophysical models that link MR-based diffusion measures to underlying microscopic tissue characteristics, which necessitates validation through ex vivo dMRI measurements. Building upon 13 years of research and development, we present an open-source, MATLAB-based academic software toolkit dubbed ACID: A Comprehensive Toolbox for Image Processing and Modeling of Brain, Spinal Cord, and Ex Vivo Diffusion MRI Data. ACID is designed to process and model dMRI data of the brain, spinal cord, and ex vivo specimens by incorporating state-of-the-art artifact correction tools, diffusion and kurtosis tensor imaging, and biophysical models that enable the estimation of microstructural properties in white matter. Additionally, the software includes an array of linear and non-linear fitting algorithms for accurate diffusion parameter estimation. By adhering to the Brain Imaging Data Structure (BIDS) data organization principles, ACID facilitates standardized analysis, ensures compatibility with other BIDS-compliant software, and aligns with the growing availability of large databases utilizing the BIDS format. Furthermore, ACID seamlessly integrates into the popular Statistical Parametric Mapping (SPM) framework, benefitting from a wide range of segmentation, spatial processing, and statistical analysis tools as well as a large and growing number of SPM extensions. As such, this comprehensive toolbox covers the entire processing chain from raw DICOM data to group-level statistics, all within a single software package.
Competing Interest Statement
This work was supported by the German Research Foundation (DFG Priority Program 2041 "Computational Connectomics" (MO 2397/5-1, MO 2397/5-2)), the Emmy Noether Stipend (MO 2397/4-1 and 2397/4-2), and the BMBF (01EW1711A and B) in the framework of ERA-NET NEURON. L.R. is supported in part by NSF awards DMS 1751636 and DMS 2038118. P.F. is funded by an SNF Eccellenza Professorial Fellowship grant (PCEFP3_181362/1).
Footnotes
↵* Shared first authors.
Improved structure and updated figures to present the content more clearly.