Automated brainstem volumetry can aid in the diagnostics of parkinsonian disorders.

INTRODUCTION

Separating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) and multiple system atrophy (MSA) is often challenging in early disease but is important for appropriate management. Magnetic resonance imaging (MRI) can aid the diagnostics and manual 2D measurements are often used. However, new fully automatic brainstem volumetry could potentially be more accurate and increase availability of brainstem metrics.

METHODS

Clinical 3D T1-weighted MRI were obtained from 196 consecutive patients; 29 PSP, 27 MSA, 140 PD. Midbrain-pons ratio and magnetic resonance parkinsonism index (MRPI) 1.0 and 2.0 were manually calculated, and intra-rater and inter-rater reliability was assessed. FreeSurfer was used to automatically segment brainstem substructures, normalized to the intracranial volume. The robustness of the automated analysis was evaluated in 3 healthy controls. The diagnostic accuracy of the brainstem biomarkers was assessed using receiver operating characteristic curves.

RESULTS

Automatic brainstem volumetry had good repeatability/reproducibility with intra-scanner coefficient of variation 0.3-5.5% and inter-scanner coefficient of variation 0.9-8.4% in the different brainstem regions. Midbrain volume performs better than planimetric measurements in separating PSP from PD (Area under the curve (AUC) 0.90 compared with 0.81 for midbrain-pons ratio (p = 0.019), 0.77 for MRPI 1.0 (p = 0.007) and 0.81 for MRPI 2.0 (p = 0.021)). Midbrain volume performed on par with planimetry for separation between PSP and MSA.

CONCLUSION

Automatic brainstem segmentation is robust and shows promising diagnostic performance in separating PSP from PD and MSA. If further developed, it could play a role in diagnosing PSP and could potentially be used as an outcome in clinical trials.