Professor Feng Liu
Professor

Feng Liu

Email: 
Phone: 
+61 7 336 53982

Availability

Professor Feng Liu is:
Available for supervision
Media expert

Fields of research

Biomedical engineering

Qualifications

  • Bachelor of Engineering, Shandong University (山东大学)
  • Masters (Coursework) of Science, Shandong University (山东大学)
  • Doctor of Philosophy, Zhejiang University

Research interests

  • Research interests

    My research lies in medical imaging, with the focus on magnetic resonance imaging (MRI) hardware design and electromagnetic analysis + cardiac electrical function imaging. My current research program includes: (1) Magnetic Resonance Engineering (Electromagnetic Analysis and Design); (2) Magnetic Resonance Imaging (MRI Image reconstruction, including parallel imaging, compressed sensing, etc.); (3) Bioelectromagnetism (AI-based ECG; TMS designs); (4) Computational Electromagnetics (in particular, Finite-difference Time-domain (FDTD)); (5) Engineering Optimization; (6) High-performance Parallel Computing (in particular, GPU computing). (7) Machine learning/deep learning based medical imaging. I am currently recruiting graduate students. Check out Available Projects for details. Open to both Domestic and International students currently onshore.

Search Professor Feng Liu’s works on UQ eSpace

428 works between 1997 and 2024
All (428) Journal Article (260) Conference Publication (165) Other Outputs (3)

61 - 80 of 428 works

2022

Journal Article

A forced cough sound based pulmonary function assessment method by using machine learning

Xu, Wenlong, He, Guoqiang, Pan, Chen, Shen, Dan, Zhang, Ning, Jiang, Peirong, Liu, Feng and Chen, Jingjing (2022). A forced cough sound based pulmonary function assessment method by using machine learning. Frontiers in Public Health, 10 1015876, 1-10. doi: 10.3389/fpubh.2022.1015876

A forced cough sound based pulmonary function assessment method by using machine learning

2022

Journal Article

Analysis and suppression of thermal magnetic noise of ferrite in the SERF co-magnetometer

Pang, Haoying, Liu, Feng, Fan, Wengfeng, Wu, Jiaqi, Yuan, Qi, Wu, Zhihong and Quan, Wei (2022). Analysis and suppression of thermal magnetic noise of ferrite in the SERF co-magnetometer. Materials, 15 (19) 6971, 1-10. doi: 10.3390/ma15196971

Analysis and suppression of thermal magnetic noise of ferrite in the SERF co-magnetometer

2022

Journal Article

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

Gao, Yang, Xiong, Zhuang, Fazlollahi, Amir, Nestor, Peter J., Vegh, Viktor, Nasrallah, Fatima, Winter, Craig, Pike, G. Bruce, Crozier, Stuart, Liu, Feng and Sun, Hongfu (2022). Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks. NeuroImage, 259 119410, 1-13. doi: 10.1016/j.neuroimage.2022.119410

Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks

2022

Journal Article

Endogenous biocatalytic reaction-based nanoplatform for multifunctional tumor theranostics

Sheng, Shu, Lin, Lin, Liu, Feng, Zhang, Ying, Xu, Caina, Hao, Kai, Wang, Dianwei, Tian, Huayu and Chen, Xuesi (2022). Endogenous biocatalytic reaction-based nanoplatform for multifunctional tumor theranostics. Chemistry of Materials, 34 (19), 8664-8674. doi: 10.1021/acs.chemmater.2c01648

Endogenous biocatalytic reaction-based nanoplatform for multifunctional tumor theranostics

2022

Journal Article

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

Zhu, Xuanyu, Gao, Yang, Liu, Feng, Crozier, Stuart and Sun, Hongfu (2022). BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources. Zeitschrift fur Medizinische Physik, 33 (4), 578-590. doi: 10.1016/j.zemedi.2022.08.001

BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources

2022

Journal Article

Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning

Zhang, Hua, Liu, Chengyu, Tang, Fangfang, Li, Mingyan, Zhang, Dongxia, Xia, Ling, Zhao, Nan, Li, Sheng, Crozier, Stuart, Xu, Wenlong and Liu, Feng (2022). Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning. Frontiers in Physiology, 13 956320, 956320. doi: 10.3389/fphys.2022.956320

Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning

2022

Journal Article

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

Zhu, Xuanyu, Gao, Yang, Liu, Feng, Crozier, Stuart and Sun, Hongfu (2022). Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning. Zeitschrift fur Medizinische Physik, 32 (2), 188-198. doi: 10.1016/j.zemedi.2021.06.004

Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning

2022

Journal Article

Dense channel splitting network for MR image super-resolution

He, Yu, Tang, Fangfang, Jin, Jin, Li, Mingyan, Zhang, Hua and Liu, Feng (2022). Dense channel splitting network for MR image super-resolution. Magnetic Resonance Imaging, 88, 53-61. doi: 10.1016/j.mri.2022.01.016

Dense channel splitting network for MR image super-resolution

2022

Journal Article

A novel active shim coil design scheme for the effective imaging region above the patient bed in MRI

Niu, Chaoqun, Tang, Fangfang, Wang, Qiuliang and Liu, Feng (2022). A novel active shim coil design scheme for the effective imaging region above the patient bed in MRI. Journal of Superconductivity and Novel Magnetism, 35 (6), 1685-1691. doi: 10.1007/s10948-022-06249-x

A novel active shim coil design scheme for the effective imaging region above the patient bed in MRI

2022

Journal Article

Progress of ultra-high-field superconducting magnets in China

Wang, Qiuliang, Liu, Jianhua, Zheng, Jinxing, Qin, Jinggang, Ma, Yanwei, Xu, Qingjin, Wang, Dongliang, Chen, Wenge, Qu, Timing, Zhang, Xingyi, Jiang, Donghui, Wang, Yaohui, Zhou, Benzhe, Qin, Lang, Jin, Huan, Liu, Huajun, Zhai, Yujia and Liu, Feng (2022). Progress of ultra-high-field superconducting magnets in China. Superconductor Science and Technology, 35 (2) 023001, 023001. doi: 10.1088/1361-6668/ac3f9b

Progress of ultra-high-field superconducting magnets in China

2022

Journal Article

Exposure of infants to gradient fields in a baby MRI scanner

Tang, Fangfang, Giaccone, Luca, Hao, Jiahao, Freschi, Fabio, Wu, Tongning, Crozier, Stuart and Liu, Feng (2022). Exposure of infants to gradient fields in a baby MRI scanner. Bioelectromagnetics, 43 (2), 69-80. doi: 10.1002/bem.22387

Exposure of infants to gradient fields in a baby MRI scanner

2022

Conference Publication

Undersampled MRI reconstruction with side information-guided normalisation

Liu, Xinwen, Wang, Jing, Peng, Cheng, Chandra, Shekhar S., Liu, Feng and Zhou, S. Kevin (2022). Undersampled MRI reconstruction with side information-guided normalisation. Medical Image Computing and Computer Assisted Intervention – MICCAI, Singapore, Singapore, 18-22 September 2022. Cham, Switzerland: Springer Nature Switzerland. doi: 10.1007/978-3-031-16446-0_31

Undersampled MRI reconstruction with side information-guided normalisation

2022

Journal Article

Actively-shielded ultrahigh field MRI/NMR superconducting magnet design

Wang, Yaohui, Wang, Qiuliang, Wang, Hui, Chen, Shunzhong, Hu, Xinning, Liu, Yang and Liu, Feng (2022). Actively-shielded ultrahigh field MRI/NMR superconducting magnet design. Superconductor Science and Technology, 35 (1) 014001, 014001. doi: 10.1088/1361-6668/ac370e

Actively-shielded ultrahigh field MRI/NMR superconducting magnet design

2021

Journal Article

Design of an insertable cone-shaped gradient coil matrix for head imaging with a volumetric finite-difference method

Kang, Liyi, Tang, Fangfang, Xia, Ling and Liu, Feng (2021). Design of an insertable cone-shaped gradient coil matrix for head imaging with a volumetric finite-difference method. Review of Scientific Instruments, 92 (12) 124709, 124709. doi: 10.1063/5.0060194

Design of an insertable cone-shaped gradient coil matrix for head imaging with a volumetric finite-difference method

2021

Journal Article

AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction

Gan, Hongping, Gao, Yang, Liu, Chunyi, Chen, Haiwei, Zhang, Tao and Liu, Feng (2021). AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction. IEEE Transactions on Cybernetics, PP (99), 1-14. doi: 10.1109/tcyb.2021.3127657

AutoBCS: Block-based image compressive sensing with data-driven acquisition and noniterative reconstruction

2021

Conference Publication

Image reconstruction for the rotating RF coil using k-t bin robust principal component analysis (RPCA) method

Shi, Ke, Li, Mingyan, Weber, Ewald, Crozier, Stuart and Liu, Feng (2021). Image reconstruction for the rotating RF coil using k-t bin robust principal component analysis (RPCA) method. Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Virtual, 1-5 November 2021. Piscataway, NJ, United States: IEEE. doi: 10.1109/EMBC46164.2021.9631104

Image reconstruction for the rotating RF coil using k-t bin robust principal component analysis (RPCA) method

2021

Journal Article

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

Gao, Yang, Cloos, Martijn, Liu, Feng, Crozier, Stuart, Pike, G. Bruce and Sun, Hongfu (2021). Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction. NeuroImage, 240 118404, 1-13. doi: 10.1016/j.neuroimage.2021.118404

Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction

2021

Conference Publication

Universal Undersampled MRI Reconstruction

Liu, Xinwen, Wang, Jing, Liu, Feng and Zhou, S. Kevin (2021). Universal Undersampled MRI Reconstruction. MICCAI 2021: Medical Image Computing and Computer Assisted Intervention, Strasbourg, France, 27 September - 1 October 2021. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-87231-1_21

Universal Undersampled MRI Reconstruction

2021

Journal Article

Determination of the absolute branching fractions of D<sup>0</sup> → K<sup>-</sup>e<sup>+</sup>ν<sub>e</sub> and D<sup>+</sup> → (K)over-bar<sup>0</sup>e<sup>+</sup>ν<sub>e</sub>

Ablikim, M., Achasov, M. N., Adlarson, P., Ahmed, S., Albrecht, M., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, X. H., Bai, Y., Bakina, O., Ferroli, R. Baldini, Balossino,, Ban, Y., Begzsuren, K., Berger, N., Bertani, M., Bettoni, D., Bianchi, F., Bloms, J., Bortone, A., Boyko,, Briere, R. A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N. ... Zou, J. H. (2021). Determination of the absolute branching fractions of D0 → K-e+νe and D+ → (K)over-bar0e+νe. Physical Review D, 104 (5) 052008, 1-10. doi: 10.1103/PhysRevD.104.052008

Determination of the absolute branching fractions of D<sup>0</sup> → K<sup>-</sup>e<sup>+</sup>ν<sub>e</sub> and D<sup>+</sup> → (K)over-bar<sup>0</sup>e<sup>+</sup>ν<sub>e</sub>

2021

Journal Article

Deep unregistered multi-contrast MRI reconstruction

Liu, Xinwen, Wang, Jing, Jin, Jin, Li, Mingyan, Tang, Fangfang, Crozier, Stuart and Liu, Feng (2021). Deep unregistered multi-contrast MRI reconstruction. Magnetic Resonance Imaging, 81, 33-41. doi: 10.1016/j.mri.2021.05.005

Deep unregistered multi-contrast MRI reconstruction

Current funding

  • 2025 - 2027
    Quantum-Enabled Low-Field Magnetic Resonance Imaging for High-Performance Sport
    Queensland Government Department of Environment, Science and Innovation
    Open grant
  • 2024 - 2026
    Disambiguating Parkinson's disease from disorders with mimicking symptoms using ultra-high-field (7 Tesla) multi-modal MRI
    NHMRC IDEAS Grants
    Open grant
  • 2023 - 2026
    Tissue Bio-physicochemical Quantification Using Magnetic Resonance Imaging
    ARC Discovery Projects
    Open grant
  • 2022 - 2025
    Evaluation of 7T 8-channel MRI coil for imaging of the human spine
    Aix-Marseille University
    Open grant

Past funding

  • 2023 - 2024
    Versatile Physical Property Measurement System for South-East Queensland (ARC LIEF administered by Queensland University of Technology)
    Queensland University of Technology
    Open grant
  • 2018 - 2022
    Dielectric contrast imaging for 7 Tesla Magnetic Resonance applications
    ARC Discovery Projects
    Open grant
  • 2016 - 2020
    Rotating Radiofrequency Phased-array for 7 Tesla Magnetic Resonance Imaging
    ARC Discovery Projects
    Open grant
  • 2014 - 2016
    Advanced Magnetic Resonance Imaging at 7 Tesla: Resolving the fundamental radiofrequency field-tissue interaction problem at ultra-high field
    ARC Discovery Projects
    Open grant
  • 2013 - 2016
    Heteronuclear parallel imaging and spectroscopy for Magnetic Resonance
    ARC Linkage Projects
    Open grant
  • 2013 - 2016
    Real-time cardiac magnetic resonance imaging: a compressed-sensing framework incorporating sensor design and multidimensional signal reconstruction
    ARC Discovery Projects
    Open grant
  • 2010 - 2012
    Solutions for reducing magnetic resonance image degradations and tissue heating at high frequencies
    ARC Discovery Projects
    Open grant
  • 2010
    UQ Travel Awards #2 - Feng LIU
    UQ Travel Grants Scheme
    Open grant
  • 2008 - 2011
    Transceive Phased Arrays for Parallel Imaging in High Field Magnetic Resonance Microscopy.
    ARC Linkage Projects
    Open grant
  • 2007 - 2011
    UQ Mid-Career Research Fellowship Start-Up Funding: Bioelectromagnetics and medical diagnostics
    UQ Mid-Career Research Fellowship
    Open grant
  • 2005 - 2007
    Cardiac electrographic modelling and analysis
    ARC Discovery Projects
    Open grant

Availability

Professor Feng Liu is:
Available for supervision

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Available projects

  • AI-based Magnetic Resonance Imaging

  • Novel Hardware design in MRI

  • AI-based ECG study

  • Computational Electromagnetics and Its Application in MRI

  • Novel Imaging Methods in MRI

Supervision history

Current supervision

  • Doctor Philosophy

    Passive shimming and electromagnetic contrast imaging at ultrahigh field MRI

    Principal Advisor

  • Doctor Philosophy

    Advance low-field MRI with deep learning fused techonologies

    Principal Advisor

  • Doctor Philosophy

    AI-empowered super-resolution MRI

    Principal Advisor

  • Doctor Philosophy

    Diagnosis of Arrhythmia using ECG signal classification by neural networks

    Principal Advisor

  • Doctor Philosophy

    A novel radio-frequency (RF) antenna system for X-nuclei magnetic resonance imaging

    Principal Advisor

  • Doctor Philosophy

    The design of metamaterial RF shield to reduce specific absorption rate and improve B1 efficiency for ultra-high field MRI

    Principal Advisor

    Other advisors: Emeritus Professor Stuart Crozier, Dr Lei Guo

  • Doctor Philosophy

    Radio-frequency system design for magnetic resonance imaging at 7 Tesla

    Principal Advisor

  • Doctor Philosophy

    Combined Compressed sensing and machine learning/deep learning methods for rapid MRI

    Principal Advisor

    Other advisors: Professor Kwun Fong, Associate Professor Henry Marshall, Dr Hongfu Sun

  • Doctor Philosophy

    Improving Artificial Intelligence And Deep Learning Algorithms In Super-Resolution Imaging

    Principal Advisor

    Other advisors: Associate Professor Kai-Hsiang Chuang

  • Doctor Philosophy

    MR image processing through advanced optimisation techniques and deep learning

    Associate Advisor

    Other advisors: Dr Hongfu Sun

  • Doctor Philosophy

    MRI methods development through deep learning

    Associate Advisor

    Other advisors: Dr Hongfu Sun

  • Doctor Philosophy

    MR image processing through advanced optimisation techniques and deep learning

    Associate Advisor

    Other advisors: Dr Hongfu Sun

  • Doctor Philosophy

    Development of novel deep learning methods for medical imaging

    Associate Advisor

    Other advisors: Dr Nan Ye, Dr Hongfu Sun

  • Doctor Philosophy

    MR image processing through advanced optimization techniques and deep learning

    Associate Advisor

    Other advisors: Dr Hongfu Sun

Completed supervision

Enquiries

Contact Professor Feng Liu directly for media enquiries about:

  • BioElectromagnetics
  • BioMechanical Engineering
  • Biomedical Engineering
  • Computational Electromagnetics
  • Engineering optimisation
  • Magnetic Resonance Imaging (MRI)

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