Kişiselleştirilmiş Derin Beyin Stimülasyonu: Öngörülebilir Elektrot Alan Modellemesi için Çok Modlu Görüntüleme ve Sonlu Eleman Analizinin AI-Driven Fusionu

Araştırma özeti

Aşağıdaki metin kaynak makalenin lisansı doğrulanmış orijinal özetidir. cc by

Abstract Precise targeting in deep brain stimulation (DBS) is challenged by individual neuroanatomical variability and postoperative brain shift, often compromising therapeutic efficacy in movement disorders like Parkinson's disease. Conventional atlas-based approaches lack patient-specific models to predict stimulation field interactions with target nuclei (e.g., STN, GPi). Here we present an integrative computational pipeline combining multi-modal imaging with biophysical simulation to enable personalized DBS planning. Our framework leverages: 1) multi-modal registration (advanced normalization tools, ANTs; or statistical parametric mapping, SPM) with subcortical brain shift correction, significantly reducing electrode placement error; 2) AI-driven electrode reconstruction (PaCER) achieving 0.4 ± 0.1 mm contact localization accuracy; and 3) patient-specific finite element modelling (iso2mesh/TetGen) predicting confined stimulation fields. Validated on clinical imaging data (pre-op T1/T2 MRI; post-op CT), the pipeline generated anatomically grounded electrophysiological models in

İlgili konular