Paper
29 March 2007 Computer-aided differential diagnosis in movement disorders using MRI morphometry
S. Duchesne, Y. Roland M.D., M. Verin M.D., C. Barillot
Author Affiliations +
Abstract
Background: Reported error rates for initial clinical diagnosis in parkinsonian disorders can reach up to 35%. Reducing this initial error rate is an important research goal. The objective of this work is to evaluate the ability of an automated MR-based classification technique in the differential diagnosis of Parkinson's disease (PD), multiple systems atrophy (MSA) and progressive supranuclear palsy (PSP). Methods: A total of 172 subjects were included in this study: 152 healthy subjects, 10 probable PD patients and 10 age-matched patients with diagnostic of either probable MSA or PSP. T1-weighted (T1w) MR images were acquired and subsequently corrected, scaled, resampled and aligned within a common referential space. Tissue transformation and deformation features were then automatically extracted. Classification of patients was performed using forward, stepwise linear discriminant analysis within a multidimensional transformation/deformation feature space built from healthy subjects data. Leave-one-out classification was used to avoid over-determination. Findings: There were no age difference between groups. Highest accuracy (agreement with long-term clinical follow-up) of 85% was achieved using a single MR-based deformation feature. Interpretation: These preliminary results demonstrate that a classification approach based on quantitative parameters of 3D brainstem morphology extracted automatically from T1w MRI has the potential to perform differential diagnosis of PD versus MSA/PSP with high accuracy.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. Duchesne, Y. Roland M.D., M. Verin M.D., and C. Barillot "Computer-aided differential diagnosis in movement disorders using MRI morphometry", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65140X (29 March 2007); https://doi.org/10.1117/12.710841
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Cited by 2 scholarly publications.
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KEYWORDS
Tissues

Magnetic resonance imaging

Computer aided diagnosis and therapy

Diagnostics

Data modeling

Feature extraction

Brain

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