Introduction

Diffusion MRI (dMRI) is a primary tool for noninvasive and in vivo investigation of tissue microstructure and white matter (WM) pathways in the human brain^1,2. Making sense of dMRI data often involves intensive processing, fine-grained analysis, and multi-perspective visualization. Despite the availability of many software packages for dMRI processing, few are effective for the entire human lifespan, particularly the first years of life where the diffusion characteristics are markedly different from adults. Here, we introduce a robust and integrative dMRI processing toolkit --- Computational Medical Imaging (COMEDI) --- that has been tested on dMRI data spanning the entire human lifespan from birth to 100 years of age. COMEDI covers recent advances in dMRI processing and analysis, including asymmetric fiber orientation distribution functions, cortico-cortical tractography with explicit consideration of gyral bias, advanced microstructural analysis, consistent bundle identification across the human lifespan, dedicated analysis pipelines for baby dMRI, and subject-specific whole-brain parcellation. COMEDI will be made publicly available as a non-commercial software package after the ISMRM meeting to support lifespan dMRI studies.

Methods

COMEDI is a one-stop platform for dMRI processing, analysis, and visualization. It supports multiple file formats (DICOM, Analyze, NIFTI, NRRD/NHDR, MRtrix, etc.) and interoperates with various off-the-shelf software packages (Dipy³, MRtrix3⁴, FSL⁵, FreeSurfer⁶, DSI-Studio⁷, and Camino⁸) (Fig. 1). Developed in MATLAB, COMEDI can be deployed on multiple operating systems (MS Windows, Unix, and macOS). COMEDI has been evaluated on dMRI data of the entire human lifespan from birth to 100 years of age. It provides specialized tools for several tasks: (i) Image quality control (IQC) for distinguishing between good and poor quality data. (ii) Post-acquisition anatomy-guided correction of eddy-current and susceptibility-induced geometric distortions. (iii) Tissue microstructural analysis, and (iv) tractography. All these algorithms are accessible via the command-line interface (CLI). We also integrated a visualization platform for rendering brain cortical surfaces, diffusion glyphs for intra-voxel architecture and image maps, and fiber streamlines for visualizing specific fiber properties. COMEDI offers snapshots and animation tools for easily exporting key results. Moreover, a D3.js framework is integrated into COMEDI for generating dynamic and interactive data visualizations accessible via web browsers. COMEDI also supports batch processing following the BIDS structure (https://bids.neuroimaging.io/). Along with the processing workflows, we also provide WM tract atlases across the lifespan⁹.

Results

We show the results generated via COMEDI in Figs. 2, 3, and 4.

Conclusion

COMEDI is a MATLAB-based toolkit for computational diffusion MRI. It contains generic methods for signal processing, machine learning, statistical analysis, and visualization of dMRI data. COMEDI is completed with detailed documentation, worked examples, and screenshots to facilitate quick acquaintance with its many features.

Acknowledgements

This work was supported in part by the United States National Institutes of Health (NIH) through grant MH125479.

References

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