Area of research
Radiology, Nuclear Medicine and Imaging · Epidemiology
Research interest
Research topics from publications: Small vessel disease burden predicts functional outcomes in patients with acute ischemic stroke using machine learning; Arterial Spin Labeling‐Based MRI Estimation of Penumbral Tissue in Acute Ischemic Stroke; MRI Assessment of Brain Frailty and Clinical Outcome in Patients With Acute Posterior Perforating Artery Infarction. Representative work: AIMS: Our purpose is to assess the role of cerebral small vessel disease (SVD) in prediction models in patients with different subtypes of acute ischemic stroke (AIS). METHODS: We enrolled 398 small-vessel occlusion (SVO) and 175 large artery atherosclerosis (LAA) AIS patients. Functional outcomes were assessed using the modified Rankin Scale (mRS) at 90 days. MRI was performed to assess white matter hyperintensity (WMH), perivascular space (PVS), lacune, and cerebral microbleed (CMB). Logistic regression (LR) and machine learning (ML) were used to develop predictive models to assess the influences of SVD on the prognosis. RESULTS: In the feature evaluation of SVO-AIS for different outcomes, BACKGROUND: Arterial spin labeling (ASL) has shown potential for the assessment of penumbral tissue in patients with acute ischemic stroke (AIS). The postlabeling delay (PLD) parameter is sensitive to arterial transit delays and influences cerebral blood flow measurements. PURPOSE: To assess the impact of ASL acquisition at different PLDs for penumbral tissue quantification and to compare their performance regarding assisting patient selection for endovascular treatment with dynamic susceptibility contrast MRI (DSC-MRI) as the reference method. STUDY TYPE: Retrospective. POPULATION: A total of 53 patients (59.98 ± 12.60 years, 32% women) with AIS caused by internal carotid or middle cerebral