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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Biography
About me
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Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
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publications
DIMENSION: Dynamic MR imaging with both k-space and spatial prior knowledge obtained via multi-supervised network training
Published in September 4, 2019
This paper proposes a dynamic MR imaging method with both k-space and spatial prior knowledge integrated via multi-supervised network training, dubbed as DIMENSION. Specifically, the DIMENSION architecture consists of a frequential prior network for updating the k-space with its network prediction and a spatial prior network for capturing image structures and details. Furthermore, a multi-supervised network training technique is developed to constrain the frequency domain information and the spatial domain information.
Recommended citation: Wang, S., Ke, Z., Cheng, H., Jia, S., Ying, L., Zheng, H., & Liang, D. (2019). DIMENSION: Dynamic MR imaging with both k‐space and spatial prior knowledge obtained via multi‐supervised network training. NMR in Biomedicine, e4131. https://onlinelibrary.wiley.com/doi/full/10.1002/nbm.4131
A new deep learning method for image deblurring in optical microscopic systems
Published in December 16, 2019
In this paper, we present a deep‐learning‐based deblurring method that is fast and applicable to optical microscopic imaging systems. We tested the robustness of proposed deblurring method on the publicly available data, simulated data and experimental data (including 2D optical microscopic data and 3D photoacoustic microscopic data), which all showed much improved deblurred results compared to deconvolution..
Recommended citation: Zhao, H., Ke, Z., Chen, N., Wang, S., Li, K., Wang, L., ... & Liang, D. (2020). A new deep learning method for image deblurring in optical microscopic systems. Journal of Biophotonics, 13(3), e201960147. https://onlinelibrary.wiley.com/doi/full/10.1002/jbio.201960147
Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks
Published in January 1, 2020
This article provides an overview of deep-learning-based image reconstruction methods for MRI. Two types of deep-learningbased approaches are reviewed, those that are based on unrolled algorithms and those that are not, and the main structures of both are explained. Several signal processing issues for maximizing the potential of deep reconstruction in fast MRI are discussed, which may facilitate further development of the networks and performance analysis from a theoretical point of view.
Recommended citation: Liang, D., Cheng, J., Ke, Z., & Ying, L. (2020). Deep magnetic resonance image reconstruction: Inverse problems meet neural networks. IEEE Signal Processing Magazine, 37(1), 141-151. https://ieeexplore.ieee.org/abstract/document/8962949
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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