Deep learning studies have successfully used image-to-image neural networks, including U-Net architectures, to segment MRI images and quantify brain atrophy based on segmentation maps. 
A non-invasive magnetic resonance imaging (MRI) method to map brain tissue OEF using quantitative measurements of the blood-oxygenation level dependent (BOLD) MRI signal
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://academicpages.github.io/files/paper1.pdf
Published in Journal 1, 2010
This paper is about the number 2. The number 3 is left for future work.
Recommended citation: Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2). http://academicpages.github.io/files/paper2.pdf
Published in Journal 1, 2015
This paper is about the number 3. The number 4 is left for future work.
Recommended citation: Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3). http://academicpages.github.io/files/paper3.pdf
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The objective of this study is to evaluate the performance of machine learning models based on features extracted from T1 images for identifying Multiple Sclerosis subjects and studying feature importance selected by models.
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I was invited to give a talk on quantitative BOLD modeling at ISMRM-endorsed workshop on MRI Acquisition & Reconstruction
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Preliminary results on research project towards a quantitative MRI method to measure brain oxygenation. Quantitative BOLD technique effectively evaluates the tissue hemodynamic parameters (e.g. OEF), and its response to a vasodilation challenge in a reliable manner with induced tissue perfusion changes.
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Preliminary results on comparision between two quantitative MRI techniques in quantification of brain oxygenation in different gas conditions.
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This study uses Monte Carlo simulations to understand the behavior of quantitative BOLD in different physiological conditions, using a Variational Bayesian inference framework with prior information and data from Asymmetric Spin Echo (ASE) scans. In vivo data showed that this inference approach effectively enables smoother and quantitatively different parameter maps of OEF and DBV.
Graduate course, UC Davis, Department of Biomedical Engineering, 2022
I was a graduate teaching assistant for BIM 289B: Multi-modadl neuroimaging techniques with Professor Audrey Fan. I held office hours, graded projects, and facilitated course discussion and tutorial. This course was a technical electivev in Biomedical Engineering Department at UC Davis. I was on course staff in Winter 2022.