Volumetric and Intrastructural Characterization of the Substantia Nigra Using Optimized Synthetic MR Images Derived...

Accurate imaging of the substantia nigra (SN) is critical for diagnosing and monitoring neurodegenerative conditions like Parkinson's disease. However, conventional magnetic resonance imaging (MRI) struggles to precisely visualize this small structure within the deep brain due to poor intrinsic tissue contrast. Although quantitative MRI (qMRI) captures objective tissue properties with diagnostic value to assess neurodegenerative conditions of the SN, additionally acquiring multiple MRI contrasts inherently requires impractically long scan times. To overcome this, qMRI-derived synthetic MRI uses mathematical signal models to retrospectively generate various synthetic contrast weightings from a single map of quantitative T1-relaxation times (qT1). This study investigated which mathematical transformations of qT1 maps, including an approximation for pseudo-proton spin density (pPSD), are best suited to derive optimized synthetic MRI contrasts for accurate SN analysis. By evaluating six dedicated qT1-derived contrast weightings in healthy subjects, this work demonstrates that a nonlinear, signal-increased pPSD-based synthetic MRI contrast (si⁴_PSD) yields a favorable balance by significantly enhancing optical contrast while maintaining a sufficient signal-to-noise ratio and preserving contrast-to-noise ratio. The si⁴_PSD images enabled highly consistent, automated atlas-based segmentation of the SN that significantly outperformed traditional manual delineation, overcoming rater variability and bias from age-dependent contrast changes. Automated segmentation revealed significant sex- and hemisphere-dependent differences in SN volume, whereas qT1 data simultaneously uncovered distinct subregional and asymmetric intrastructural aging patterns. These findings highlight that macrostructural volume and intrastructural tissue properties must be assessed as independent variables. In conclusion, this qT1-based, automated framework offers a highly consistent, scalable approach to SN mapping without extending patient scan times. By clearly separating physiological intrastructural changes affecting MRI contrasts from mistakenly perceived general atrophy, this methodology provides a powerful foundation for developing more precise and objective imaging biomarkers for SN-related neurodegenerative diseases in both research and clinical settings.