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Ze Wang, PhD

Laboratories

Wang Ze

Ze Wang, PhD

Ze Wang, Ph.D.

Associate Professor, Department of Diagnostic Radiology and Nuclear Medicine
Center for Advanced Imaging Research (CAIR)
Phone: 410-706-2797
Email: ze.wang@som.umaryland.edu

Free research tools for downloading (ASLtbx, SVM-LSM, BENtbx)

Biosketch

Dr. Wang got his PhD from Shanghai Jiao Tong University. His major research interest is in MR imaging, neuroimaging signal processing, and imaging-based translational research in Alzheimer's Disease and Addiction. In MRI, he focuses on Arterial Spin Labeling (ASL) Perfusion MRI and image reconstruction. His ASL work includes a 3D background suppressed spiral readout ASL MRI sequence, a series of ASL MRI data processing methods, as well as the first open-source software package for processing ASL data: ASLtbx. In MR reconstruction, he developed a multi-dimensional k-space based parallel imaging reconstruction algorithm: MCMLI for 2D and SNAPPI for 3D imaging; an optimized and super-fast dictionary searching algorithm: MRF-ZOOM for magnetic resonance fingerprinting, which can work without a pre-defined full dictionary. In neuroimaging, he developed an fMRI-based brain entropy mapping (BEN) tool and a multivariate lesion-symptom mapping algorithm (SVR-LSM). His recent interest in these areas includes incorporating deep machine learning in ASL data processing and image reconstruction.  He has disseminated and is still maintaining three open source software packages: ASLtbx, SVR-LSM, and BENtbx. 

Complete Faculty Profile >


Research Projects

Please select a project title to learn more:

ASL MRI in Alzheimer's Disease Research

The goal of this project is to deep clean ASL MRI and use it for detecting early AD and predicting AD progression. Existing data from Alzheimer's Disease Neuroimaging Initiative (ADNI, http://adni.loni.usc.edu/) are used.

Visit our PMC Project page for:

- project members
- publications

Learn more about this project

 

Deep learning in Arterial Spin Labeling Perfusion MRI

Improving ASL MRI using deep machine learning

This aim of this project is to improve spatial and temporal resolution, signal-to-noise-ratio (SNR) of ASL perfusion MRI using deep machine learning.

Learn more about this project

Brain entropy mapping using resting-state fMRI

Entropy measures irregularity of a dynamic system, which also indicates the information capacity. Human brain is a complex functional system with ongoing information generation and processing. Higher entropy generally bestows a better functional flexibility, which is beneficial in many situations but might be detrimental too for certain range of brain functions such as mood and emotions. Nevertheless, measuring brain entropy provides a tools to assess such information processing balance and the changes due to disorders. The aim of this project is to further develop brain entropy techniques and use it in neurodegenerative disease and drug addiction studies.

 


Lei Zhang, PhD
Dr. Zhang is a Research Associate in Dr. Wang's lab since Oct 2019. 

Aldo Camargo Fernandez-Baca, PhD

Dr. Fernandez-Baca joined Dr. Wang's lab as a postdoc fellow in July 2019. His major research focus is arterial spin labeling perfusion MRI-based Alzheimer's Disease study.

Yiran Li, PhD candidate. Mr Li is a PhD student in Dr. Wang's lab. His research focuses on deep learning-based medical imaging processing, including image synthesis, denoising, and reconstruction. 

 

 

 

Publication

 

Li, Yiran, Sudipto Dolui, Dan-Feng Xie, Ze Wang, and Alzheimer’s Disease Neuroimaging Initiative. "Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI." Journal of neuroscience methods 307 (2018): 248-253.

 

Xie, Danfeng, Yiran Li, HanLu Yang, Donghui Song, Yuanqi Shang, Qiu Ge, Li Bai, and Ze Wang. "BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks." In International Workshop on Machine Learning in Medical Imaging, pp. 373-381. Springer, Cham, 2019.

 

Li, Zheng, Qingping Liu, Yiran Li, Qiu Ge, Yuanqi Shang, Donghui Song, Ze Wang, and Jun Shi. "A Two-Stage Multi-loss Super-Resolution Network for Arterial Spin Labeling Magnetic Resonance Imaging." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 12-20. Springer, Cham, 2019.

 

Xie, Danfeng, Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).

 

Danfeng Xie, PhD candidate. Mr Xie is a PhD student in Dr. Wang's lab. His research focuses on deep learning-based ASL MRI denoising, acceleration, and cross-modality prediction. His publication list is: 

 

  • Xie, Danfeng,Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).
  • Li, Yiran, Sudipto Dolui, Danfeng Xie, Ze Wang, and Alzheimer’s Disease Neuroimaging Initiative. "Priors-guided slice-wise adaptive outlier cleaning for arterial spin labeling perfusion MRI." Journal of neuroscience methods 307 (2018): 248-253.
  • Xie, Danfeng,Yiran Li, Hanlu Yang, Li Bai, Ze Wang. A Learning-From-Noise Dilated Wide Activation Network for Denoising Arterial Spin Labeling (ASL) Perfusion Images. 28th Annual Meeting ISMRM, 2020.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Donghui Song, Yuanqi Shang, Qiu ge, Li Bai and Ze Wang. Estimating Cerebral Blood Flow from BOLD Signal Using Deep Dilated Wide Activation Networks. 28th Annual Meeting ISMRM, 2020.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Donghui Song, Yuanqi Shang, Qiu ge, Li Bai and Ze Wang, “BOLD fMRI-based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks”. 10th International workshop on Machine Learning in Medical Imaging, 2019.
  • Xie, Danfeng, Yiran Li, Li Bai, and Ze Wang.“Super-ASL: Improving SNR & Temporal Resolution of ASL MRI Using Deep Learning.” ISMRM workshop on Machine Learning 2018. 
  • Xie, Danfeng, Yiran Li, Li Bai, and Ze Wang. “Denoising Arterial Spin Labeling Cerebral Blood Flow Images Using Deep Learning-Based Methods.” 26th Joint Annual Meeting ISMRM-ESMRMB. ISMRM-ESMRMB, 2018.
  • Xie, Danfeng, Lei Zhang, and Li Bai. "Deep learning in visual computing and signal processing." Applied Computational Intelligence and Soft Computing 2017 (2017).

 

Hanlu Yang. Miss Yang is a PhD student in Dr. Wang's lab since 2018. Her major focus is deep-learning based MR image reconstruction. She has contributed to several papers:

 

  • POCS Augmented CycleGAN for MR Image Reconstruction (Medical Image Analysis, Under revision)
  • Xie, Danfeng, Yiran Li, HanLu Yang, Donghui Song, Yuanqi Shang, Qiu Ge, Li Bai, and Ze Wang. "BOLD fMRI-Based Brain Perfusion Prediction Using Deep Dilated Wide Activation Networks." In International Workshop on Machine Learning in Medical Imaging, pp. 373-381. Springer, Cham, 2019.
  • Xie, Danfeng, Yiran Li, Hanlu Yang, Li Bai, Tianyao Wang, Fuqing Zhou, Lei Zhang, and Ze Wang. "Denoising arterial spin labeling perfusion MRI with deep machine learning." Magnetic Resonance Imaging (2020).

Liandong Lin, PhD. Dr. Lin got his PhD from HeiLongJiang University in 2017. Dr. Lin is a visiting scholar in Dr. Wang's lab. He has published 6 peer-reviewed journal papers and 3 international conference papers. He is currently working on fMRI signal processing. His recent research interest also includes MR technique development and deep machine learning.

Jue Lu, PhD. Dr. Lu is a mathematician. He is a visiting scholar in Dr. Wang's lab and is working on brain entropy mapping.


Publications

Click here to view Dr. Wang's publications on Pubmed

or here on Google Scholar.

Please refer Dr. Wang's faculty profile for highlighted publications