My research spans an interdisciplinary cross-section of Medical Image Processing, Machine learning, and Neuroscience covering clinical neurology and neuropsychiatry. In the fields of medical image processing and analysis, I have studied on multicontrast image registration and segmentation, surface modeling of cortical/subcortical structures which are the prerequisite techniques to proceed the analysis of structural and functional brain imaging studies.
My projects that have been recently launched at USC-INI and USC-LONI include mainly three domains of the research field: 1) Prediction of neurodevelopmental outcome in neonates with various clinical conditions such as preterm birth, hypoxia-ischemia, and congenital heart disease: This project expands in line with my team's expertise in neurodevelopment, neuroimaging, computational imaging feature modeling and machine learning (particularly DEEP learning); 2) Neuroimaging data quality controls (image QC): My team dedicates its efforts to implementation of online-based LONI-QC system that allows the public to evaluate their own data as well as to automated QC feature that will ultimately predict the accuracy of brain image post-processing and the sensitivity in the subsequently biological/clinical analysis to given target pathophysiology, and 3) Prediction of brain age and accelerated aging due to neurodegeneration: combination of brain imaging data and covolutional neural network-based deep-learning can estimate the brain age for individual images. extending this model with a statistical hazard model, we aim to determine risk scores for aging subjects who potentially develop a neurodegenerative disease.
In other clinical/neuroscientific applications, my team has applied various advanced analytic frameworks, including cortical morphometry, voxel-based morphometry, deformation-based morphometry and structural network analysis, to the assessment of brain structure in healthy conditions as well as pathological conditions, which often present anatomical variations beyond the range of normal structures.
My team continues to expand aforementioned techniques to the analysis of BIG DATA of brain imaging data to better understand mechanisms involved in various diseases and disorders such as stroke, epilepsy, dementia, sleep disorders, as well as long-term deafness and sudden hearing loss.