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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

Talk 1 on Relevant Topic in Your Field

Published: March 01, 2012

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Tutorial 1 on Relevant Topic in Your Field

Published: March 01, 2013

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Talk 2 on Relevant Topic in Your Field

Published: February 01, 2014

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Conference Proceeding talk 3 on Relevant Topic in Your Field

Published: March 01, 2014

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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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