ANALYSIS OF MUCOPOLYSACCHARIDOSIS TYPE 1 PROGRESSION BASED ON MRI DATA

Background. Mucopolysaccharidosis Type 1 (MPS 1) is a rare inherited lysosomal metabolism disorder that leads to the accumulation of glycosaminoglycans in body tissues. Due to neurodegenerative changes, patients with MPS1 experience cognitive and motor impairments that require continuous clinical monitoring. Magnetic resonance imaging (MRI) is the primary tool for visualizing structural changes in the brain; however, traditional MRI data analysis methods have several limitations related to subjective image interpretation and the high complexity of data processing. Materials and methods. This study proposes an algorithm for automated MRI data analysis aimed at assessing the dynamics of brain structure changes in patients with MPS 1. The algorithm includes several key stages: image preprocessing, segmentation of brain structures, quantitative analysis of gray and white matter volumes, as well as ventricular structures. The calculation of brain structure volumes is performed based on segmented data, followed by comparison with age– and sex-specific normative values. A particular focus of the algorithm is the calculation of deviations in the volumes of gray and white matter, as well as brain ventricles, from reference values, which allows for a quantitative assessment of the degree of neurodegenerative changes. Based on this data, disease progression is evaluated by comparing parameters at successive time points. The introduced criteria for changes in brain structure volumes help determine the degree of pathological process progression and forecast further disease development through extrapolation of current trends. Results and conclusions. The algorithm has been implemented in the MATLAB programming environment, enabling automation of image processing, increased calculation accuracy, and ease of result interpretation. The developed approach was tested on clinical data from patients diagnosed with MPS 1, which confirmed its effectiveness in assessing dynamic changes in brain structures.