RSL REU Undergrads Recognized

Recognized Undergraduate Research on Identifying High-Risk Stroke Patients

Moyamoya disease is a chronic neurovascular disorder that causes occlusions in cerebral blood vessels. A hallmark of Moyamoya disease is impaired vascular hemodynamics and the associated increase in stroke risk. Characterizing hemodynamics has important implications for patient triage and treatment evaluation. In a recent study reported in Applied Sciences, undergraduate scholars of the Stanford Research Experience for Undergraduates (REU) program Britney Campbell and Dhruv Yadav, together with members of the Center for Advanced Functional Neuroimaging (CAFN), demonstrated that deep learning strategies can effectively identify high-risk stroke patients with Moyamoya disease. The investigators created a novel deep learning model to measure cerebral blood flow using MRI data from 153 individuals. Their study demonstrated that deep learning can precisely characterize impaired cerebral blood flow in this vulnerable patient population.

Additional Stanford affiliated investigators who contributed to this study include Ramy Hussein, Maria Jovin, Sierrah Hoover, Kim Halbert, Dawn Holley, Mehdi Khalighi, Guido Davidzon, Elizabeth Tong, Michael Moseley, Gary Steinberg, Moss Zhao, and Greg Zaharchuk.

Campbell, B.; Yadav, D.; Hussein, R.; Jovin, M.; Hoover, S.; Halbert, K.; Holley, D.; Khalighi, M.; Davidzon, G.A.; Tong, E.; et al. Segmenting Cervical Arteries in Phase Contrast Magnetic Resonance Imaging Using Convolutional Encoder–Decoder Networks. Appl. Sci. 2023, 13, 11820.


Phase contrast (PC) magnetic resonance imaging (MRI) is a primary method used to quantify blood flow. Cerebral blood flow (CBF) is an important hemodynamic parameter to characterize cerebrovascular and neurological diseases. However, a critical step in CBF quantification using PC MRI is vessel segmentation, which is largely manual, and thus time-consuming and prone to interrater variability. Here, we present encoder–decoder deep learning models to automate segmentation of neck arteries to accurately quantify CBF. The PC-MRI data were collected from 46 Moyamoya (MM) patients and 107 healthy control (HC) participants. Three segmentation U-Net models (Standard, Nested, and Attention) were compared. The PC MRI images were taken before and 15 min after vasodilation. The models were assessed based on their ability to detect the internal carotid arteries (ICAs), external carotid arteries (ECAs), and vertebral arteries (VAs), using the Dice score coefficient (DSC) of overlap between manual and predicted segmentations and receiver operator characteristic (ROC) metric. Analysis of variance, Wilcoxon rank-sum test, and paired t-test were used for comparisons. The Standard U-NET, Attention U-Net, and Nest U-Net models achieved results of mean DSCs of 0.81 ± 0.21, and 0.85 ± 0.14, and 0.85 ± 0.13, respectively. The ROC curves revealed high area under the curve scores for all methods (≥0.95). While the Nested and Attention U-Net architectures accomplished reliable segmentation performance for HC and MM subsets, Standard U-Net did not perform as well in the subset of MM patients. Blood flow velocities calculated by the models were statistically comparable. In conclusion, optimized deep learning architectures can successfully segment neck arteries in PC MRI images and provide precise quantification of their blood flow.