Work

Automated brain atrophy quantification from clinical MRI predicts early neurological deterioration in anterior choroidal artery territory infarction

Year: 2025

Type: article

Abstract: Background: Early neurological deterioration (END) occurs in 43%-60% of patients with anterior choroidal artery (AChA) territory infarction. While brain atrophy serves as an imaging biomarker of diminished brain reserve capacity and may influence stroke outcomes, its predictive value for END in AChA infarction remains unclear. Methods: This dual-center retrospective cohort study consecutively enrolled patients with acute AChA territory infarction admitted to two Chinese stroke centers between September 2018 and September 2024. Clinical T1-weighted images were reconstructed into standardized high-resolution images using the SynthSR deep learning algorithm, followed by fully automated brain tissue segmentation via AssemblyNet. We calculated gray matter fraction (GMF), white matter fraction (WMF), brain parenchymal fraction (BPF), and cerebrospinal fluid fraction (CSFF) to quantify brain atrophy severity. Multivariable logistic regression and restricted cubic spline (RCS) analyses were employed to assess associations between brain atrophy metrics and END. Results: -non-linear > 0.05). Conclusion: Automated brain atrophy quantification based on routine clinical MRI can independently predict END risk in patients with AChA infarction, providing a feasible imaging biomarker for this high-risk stroke population and facilitating early risk stratification and treatment optimization.

Source: Frontiers in Neuroscience

DOI: 10.3389/fnins.2025.1714159