Iman Aganj, PhDIman Aganj, PhD, an associate investigator at the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital and an assistant professor of Radiology at Harvard Medical School, is the lead author of a recent study in Frontiers in Neuroscience, Automatic Geometry-Based Estimation of the Locus Coeruleus Region on T1-Weighted Magnetic Resonance Images.

What Question Were You Investigating?

The locus coeruleus is a key brain structure implicated in cognitive function and neurodegenerative disease. Automatic segmentation of the locus coeruleus is a crucial step in its quantitative non-invasive analysis in large MRI cohorts.

Most publicly available imaging databases for training automatic locus coeruleus segmentation models take advantage of specialized contrast-enhancing (e.g., neuromelanin-sensitive) MRI.

Segmentation models developed with such image contrasts, however, are not readily applicable to existing datasets with conventional MRI sequences.

In this work, we evaluated the feasibility of using non-contrast neuroanatomical information to geometrically approximate the locus coeruleus region from standard 3-Tesla T1-weighted images.

What Were the Results?

This report provides an evaluation of computational methods estimating the neural structure of locus coeruleus, when they are trained on geometry-based manual labels.

We achieved internal cross-validation results comparable to those in the literature. External validation Dice scores were expectedly lower, where including the phase-only image as an input, however, enhanced the results.

We also found significant correlations with the mean fractional anisotropy inside the locus coeruleus region, mainly negative correlations with the body weight and a positive correlation with working memory.

What are the Clinical Implications and Next Steps?

Quantitative MRI studies of the locus coeruleus – requiring automatic region estimation – have the potential to generate imaging biomarkers for early diagnosis of neurodegenerative diseases and patient stratification.

Paper Cited:

Aganj, I., Mora, J., Fischl, B., & Augustinack, J. C. (2024). Automatic geometry-based estimation of the locus coeruleus region on T1-weighted magnetic resonance images. Frontiers in Neuroscience, 18, 1375530. PMCID: PMC11106368