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Ze Wang, PhD

Wang Ze

Ze Wang, PhD

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

Biosketch

Dr. Wang received his PhD from Shanghai Jiao Tong University. His major research interests are in MR imaging, neuroimaging signal processing, and imaging-based translational research in Alzheimer's disease and addiction. Regarding 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 and 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. 

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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 use deep machine learning to improve spatial and temporal resolution and signal-to-noise-ratio (SNR) of ASL perfusion MRI.

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. The 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  for a certain range of brain functions such as mood and emotions. Nevertheless, measuring brain entropy provides a tool 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 them in neurodegenerative disease and drug addiction studies.

 

ASL MRI in Alzheimer's Disease Research

In a recently published paper (https://www.frontiersin.org/articles/10.3389/fnagi.2020.596122/full), we characterized resting brain entropy in normal aging and patients with different level of Alzheimer's Disease (AD) (from early Mild Cognitive Impairment to AD) using resting state fMRI. We found abnormal brain entropy (BEN) changes in the default mode network, medial temporal lobe, and prefrontal cortex which are associated with cognitive impairment and AD pathology. Collectively, the results showed that: (1) BEN increased with age and pathological deposition in normal aging but decreased with age and pathological deposition in the AD continuum; (2) AD showed catastrophic BEN reduction, which was related to more severe cognitive impairment and daily function disability; and (3) BEN decreased with education years in normal aging, but not in the AD continuum. BEN evolution follows an inverse-U trajectory when AD progresses from normal aging to AD dementia. Education is beneficial for suppressing the entropy increase potency in normal aging.

Visit our PMC Project page for:

- project members
- publications

 

Lab Members

Lei Zhang, PhD

Lei Zhang, PhD
Dr. Zhang began working in Dr. Wang's lab as a Research Associate in October 2019. 

Aldo Camargo Fernandez-Baca, PhD

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 for studying Alzheimer's disease.

Yiran Li

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. 

Publications:

  • Yiran Li, Danfeng Xie, Abigail Cember, Ravi Prakash Reddy Nanga, Hanlu Yang, Dushyant Kumar, Hari Hariharan, Li Bai, John A. Detre, Ravinder Reddy, Ze Wang, Accelerating GluCEST imaging using Deep Learning, MRM, 2020 Oct;84(4):1724-1733. doi: 10.1002/mrm.28289.
  • 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

Danfeng Xie, PhD
Dr. Xie is now a postdoc research fellow. He got his PhD in Dr. Wang's lab in 2020. His research focuses on deep learning-based ASL MRI denoising, acceleration, and cross-modality prediction.

Publications:

  • Yiran Li, Danfeng Xie, Abigail Cember, Ravi Prakash Reddy Nanga, Hanlu Yang, Dushyant Kumar, Hari Hariharan, Li Bai, John A. Detre, Ravinder Reddy, Ze Wang, Accelerating GluCEST imaging using Deep Learning, MRM, 2020 Oct;84(4):1724-1733. doi: 10.1002/mrm.28289.
  • 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

Hanlu Yang
Miss Yang was a Master student in Dr. Wang's lab from 2018 to 2020. She is now a PhD student in UMBC. 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

Liandong Lin, PhD

Dr. Lin was a visiting scholar in Dr. Wang's lab from 2019 to 2020. He has published six peer-reviewed journal papers and three international conference papers. He is currently working on fMRI signal processing. His research interests also include MR technique development and deep machine learning.

Jue Lu

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.

Donghui Song

Mr. Donghui Song got his Master degree in Cognitive Neuroscience under the supervision of Dr. Wang. He is currently a Research Specialist in Dr. Wang's lab. He has published about 10 peer-reviewed papers including 3 first-authored ones. He is proficient at processing fMRI data using the tools such as SPM, FSL, ASLtbx, BENtbx or packaged pipelines such as fMRIprep. He has been well trained for performing fMRI and TMS experiments.

Grants and Proposals

PIGrant TitleTotal Project PeriodFunding SourceTotal Costs
Ze Wang Assessing ASL CBF as a biomarker for early disease detection and disease progression 05/01/19 – 02/28/22 NIH/NIA $1,371,655.00
Ze Wang Assessing ASL CBF as a biomarker for early disease detection and disease progression (Supplement) 05/01/20 – 04/31/21 NIH/NIA $377,046.00
Ze Wang Brain entropy mapping in Alzheimer’s Disease 05/01/20 – 04/31/21 UMB ATIP $35,000.00

Total: $1,783,701.00


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


Research Projects

Please select a project title to learn more: