Visual mapping of muscle MRI fatty replacement patterns in genetic myopathies using dimensionality reduction.
Authors
Affiliations (8)
Affiliations (8)
- Imaging Center, University of Chile Clinical Hospital. Avenida Dr. Carlos Lorca Tobar 999, Independencia. 8380453, Santiago, Chile.
- John Walton Muscular Dystrophy Research Centre, Newcastle University. West Wing International Centre for Life, Central Parkway Biomedicine, Newcastle upon Tyne NE1 3BZ. United Kingdom.
- Pediatric Neurology, Vall d'Hebron Institut de Recerca (VHIR), Hospital Universitari Vall d'Hebron, Vall d'Hebron Barcelona Hospital Campus, Passeig Vall d'Hebron 119-129, 08035 Barcelona, Spain; Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain.
- Imperial College London, Department of Mathematics & I-X, Exhibition Road, South Kensington, London SW7 2AZ, United Kingdom.
- Instituto Milenio iHealth, Pontificia Universidad Católica de Chile, Pablo Burchard 2760, 7940508 Peñalolén, Santiago, Chile; Centro de Imágenes Biomédicas, Pontificia Universidad Católica de Chile, Pablo Burchard 2760, 7940508 Peñalolén, Santiago, Chile.
- John Walton Muscular Dystrophy Research Centre, Newcastle University. West Wing International Centre for Life, Central Parkway Biomedicine, Newcastle upon Tyne NE1 3BZ. United Kingdom; Neuromuscular Disorders Laboratory, Institut de recerca de l'Hospital de la Santa Creu i Sant Pau, Barcelona, Spain; Centro de Investigación Biomédica en Red en Enfermedades Raras (CIBERER), Barcelona, Spain.
- Radiology Department, University of Chile Clinical Hospital, Avenida Dr. Carlos Lorca Tobar 999, Independencia. 8380453, Santiago, Chile.
- Neuromuscular Unit, Neurology and Neurosurgery Department, University of Chile Clinical Hospital, Avenida Dr. Carlos Lorca Tobar 999, Independencia. 8380453, Santiago, Chile; Neurology and Neurosurgery Department, Clínica Dávila, Avenida Recoleta N° 464, 8431657, Recoleta, Santiago, Chile. Electronic address: [email protected].
Abstract
Muscle MRI is a complementary diagnostic tool in genetic myopathies; however, its interpretation remains challenging because numerous muscles must be assessed simultaneously, and disease-specific patterns overlap. This study evaluated whether dimensionality reduction techniques (DRT) can transform complex muscle MRI fatty replacement data into meaningful low-dimensional visual maps of patient similarity. We analyzed a large multicenter dataset comprising 975 patients with genetically confirmed diagnoses across ten myopathies. Mercuri scores of pelvic and lower-limb muscles were used as input. Principal Component Analysis (PCA), ISOMAP, t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) were applied to generate two-dimensional representations of muscle fatty replacement. The quality of these low-dimensional maps was quantitatively assessed by measuring the extent to which they preserved disease-specific organization, using unsupervised clustering as a proxy of consistency. Gaussian Mixture Models (GMM) were subsequently applied to assess whether the low-dimensional maps retained sufficient information to support disease discrimination. UMAP and t-SNE outperformed PCA and ISOMAP. Furthermore, UMAP produced more coherent and better-separated disease groupings than t-SNE, reflected by a higher V-measure metric (0.415 vs 0.403), and achieved superior top-3 diagnostic accuracy when combined with GMM (87%vs 81%). Overall, dimensionality reduction provides a framework for visualizing muscle MRI patterns similarity across neuromuscular diseases supporting pattern recognition.