Pathology-Anchored Transcranial Sonography: A Cascaded Super-Resolution Deep Learning System for Early-Stage Parkinson’s Disease Grading

Delayed diagnosis of Parkinson's disease (PD) due to undetectable early pathological changes remains a major clinical challenge limiting effective treatment. This study presents a novel diagnostic approach that integrates non-invasive imaging techniques with deep learning, facilitating accurate early diagnosis and staging of PD. A rat model of PD induced by 6-hydroxydopamine (6-OHDA) was established. Neuronal damage was quantitatively assessed through histological examination, while transcranial sonography (TCS) was employed to capture and analyze brain region images. This approach enabled the establishment of a quantitative relationship between TCS-derived imaging features and the extent of pathological injury. A deep learning framework based on TCS images was developed, integrating cascaded super-resolution reconstruction techniques (Wide Activation Super-Resolution Network (WDSR) with traditional interpolation methods) to enhance TCS image quality (PSNR = 30.67, SSIM = 0.94). Furthermore, the ResNet18 model was incorporated for disease staging of PD with 89% diagnostic accuracy. This advancement holds promise for enhancing early intervention and precision medicine strategies in PD management.