Henrik Sjöström1, Tobias Granberg2, Farouk Hashim2, Eric Westman3, Per Svenningsson4. 1. Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden; Center for Neurology, Academic Specialist Center, 113 65, Stockholm, Sweden. Electronic address: henrik.sjostrom@ki.se. 2. Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, 141 86, Stockholm, Sweden. 3. Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 171 77, Huddinge, Sweden. 4. Department of Clinical Neuroscience, Karolinska Institutet, 171 77, Stockholm, Sweden; Center for Neurology, Academic Specialist Center, 113 65, Stockholm, Sweden; Department of Neurology, Karolinska University Hospital, 141 86, Stockholm, Sweden.
Abstract
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.
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.
Authors: W J Scotton; M Bocchetta; E Todd; D M Cash; N Oxtoby; L VandeVrede; H Heuer; D C Alexander; J B Rowe; H R Morris; A Boxer; J D Rohrer; P A Wijeratne Journal: Brain Commun Date: 2022-04-14
Authors: Andrea Quattrone; Maurizio Morelli; Maria G Bianco; Jolanda Buonocore; Alessia Sarica; Maria Eugenia Caligiuri; Federica Aracri; Camilla Calomino; Marida De Maria; Maria Grazia Vaccaro; Vera Gramigna; Antonio Augimeri; Basilio Vescio; Aldo Quattrone Journal: Brain Sci Date: 2022-07-20