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

560 works between 1997 and 2026
All (560) Journal Article (380) Other Outputs (3) Conference Publication (177)

41 - 60 of 560 works

2024

Journal Article

Fat-water signal-based electrical properties tomography using the Dixon technique

Ren, Yinhao, Yuan, Kecheng, Xu, Guofang, Ye, Chunyou, Liu, Feng, Qiu, Bensheng, Nan, Xiang and Han, Jijun (2024). Fat-water signal-based electrical properties tomography using the Dixon technique. IEEE Transactions on Instrumentation and Measurement, 73 4510408. doi: 10.1109/tim.2024.3485405

Fat-water signal-based electrical properties tomography using the Dixon technique

2024

Journal Article

Study of the decay and production properties of Ds1 (2536) and Ds2* (2573)

Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskay, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F. ... Zu, J. (2024). Study of the decay and production properties of Ds1 (2536) and Ds2* (2573). Physical Review Letters, 133 (17) 171903, 1-10. doi: 10.1103/PhysRevLett.133.171903

Study of the decay and production properties of Ds1 (2536) and Ds2* (2573)

2024

Journal Article

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI

Chen, Zhifeng, Yuan, Zhenguo, Cheng, Junying, Liu, Jinhai, Liu, Feng and Chen, Zhaolin (2024). An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI. Physics in Medicine and Biology, 69 (21) 215012, 1-17. doi: 10.1088/1361-6560/ad8545

An adaptive parameter decoupling algorithm-based image reconstruction model (ADAIR) for rapid golden-angle radial DCE-MRI

2024

Journal Article

Image reconstruction with B0 inhomogeneity using a deep unrolled network on an open-bore MRI-Linac

Shan, Shanshan, Gao, Yang, Waddington, David, Chen, Hongli, Whelan, Brendan, Liu, Paul, Wang, Yaohui, Liu, Chunyi, Gan, Hongping, Gao, Mingyuan and Liu, Feng (2024). Image reconstruction with B0 inhomogeneity using a deep unrolled network on an open-bore MRI-Linac. IEEE Transactions on Instrumentation and Measurement, 73 2534109. doi: 10.1109/tim.2024.3481545

Image reconstruction with B0 inhomogeneity using a deep unrolled network on an open-bore MRI-Linac

2024

Journal Article

Using adaptive imaging parameters to improve PEGylated ultrasmall iron oxide nanoparticles-enhanced magnetic resonance angiography

Li, Cang, Shan, Shanshan, Chen, Lei, Afshari, Mohammad Javad, Wang, Hongzhao, Lu, Kuan, Kou, Dandan, Wang, Ning, Gao, Yang, Liu, Chunyi, Zeng, Jianfeng, Liu, Feng and Gao, Mingyuan (2024). Using adaptive imaging parameters to improve PEGylated ultrasmall iron oxide nanoparticles-enhanced magnetic resonance angiography. Advanced Science, 11 (39) 2405719, e2405719. doi: 10.1002/advs.202405719

Using adaptive imaging parameters to improve PEGylated ultrasmall iron oxide nanoparticles-enhanced magnetic resonance angiography

2024

Journal Article

A multiscale 3D network for lung nodule detection using flexible nodule modeling

Song, Wenjia, Tang, Fangfang, Marshall, Henry, Fong, Kwun M. and Liu, Feng (2024). A multiscale 3D network for lung nodule detection using flexible nodule modeling. Medical Physics, 51 (10), 7356-7368. doi: 10.1002/mp.17283

A multiscale 3D network for lung nodule detection using flexible nodule modeling

2024

Journal Article

LUCMT: learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction

Yang, Ziqi, Jiang, Mingfeng, Ruan, Dongshen, Li, Yang, Tan, Tao, Huang, Sumei and Liu, Feng (2024). LUCMT: learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction. Computer Methods and Programs in Biomedicine, 255 108359, 1-9. doi: 10.1016/j.cmpb.2024.108359

LUCMT: learnable under-sampling and reconstructed network with cross multi-head attention transformer for accelerating MR image reconstruction

2024

Journal Article

Search for rare decays of Ds+ to final states π+ e+ e-, ρ+ e+ e-, π+ π0e+e-, K+ π0e+e-, and KS0π+ e+ e-

Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F. ... Zu, J. (2024). Search for rare decays of Ds+ to final states π+ e+ e-, ρ+ e+ e-, π+ π0e+e-, K+ π0e+e-, and KS0π+ e+ e-. Physical Review Letters, 133 (12) 121801, 1-10. doi: 10.1103/PhysRevLett.133.121801

Search for rare decays of Ds+ to final states π+ e+ e-, ρ+ e+ e-, π+ π0e+e-, K+ π0e+e-, and KS0π+ e+ e-

2024

Journal Article

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI

Zhao, Jie, Zhu, Minhua, Xia, Ling, Fan, Yifeng and Liu, Feng (2024). A hybrid, nonlinear programming approach for optimizing passive shimming in MRI. Medical Physics, 51 (11), 8613-8622. doi: 10.1002/mp.17403

A hybrid, nonlinear programming approach for optimizing passive shimming in MRI

2024

Journal Article

MTC-CSNet: marrying transformer and convolution for image compressed sensing

Shen, Minghe, Gan, Hongping, Ma, Chunyan, Ning, Chao, Li, Hongqi and Liu, Feng (2024). MTC-CSNet: marrying transformer and convolution for image compressed sensing. IEEE Transactions on Cybernetics, 54 (9), 4949-4961. doi: 10.1109/tcyb.2024.3363748

MTC-CSNet: marrying transformer and convolution for image compressed sensing

2024

Journal Article

Active shimming for a 25 T NMR superconducting magnet by spectrum convergence method

Chen, Haoran, Wang, Yaohui, Wang, Wenchen, Zhou, Guyue, Wu, Pengfei, Qu, Hongyi, Liu, Jianhua, Li, Liang and Liu, Feng (2024). Active shimming for a 25 T NMR superconducting magnet by spectrum convergence method. Journal of Magnetic Resonance, 364 107711, 107711. doi: 10.1016/j.jmr.2024.107711

Active shimming for a 25 T NMR superconducting magnet by spectrum convergence method

2024

Journal Article

Solar-powered mixed-linker metal-organic frameworks for water harvesting from arid air

Yan, Xueli, Xue, Fei, Zhang, Chunyang, Peng, Hao, Huang, Jie, Liu, Feng, Lu, Kejian, Wang, Ruizhe, Shi, Jinwen, Li, Naixu, Chen, Wenshuai and Liu, Maochang (2024). Solar-powered mixed-linker metal-organic frameworks for water harvesting from arid air. Ecomat, 6 (7) e12473, 1-13. doi: 10.1002/eom2.12473

Solar-powered mixed-linker metal-organic frameworks for water harvesting from arid air

2024

Journal Article

Electromagnetic design and mechanical analysis of a 28.2 T/1.2 GHz high field NMR magnet

Ren, Yong, Liu, Feng and Li, Da (2024). Electromagnetic design and mechanical analysis of a 28.2 T/1.2 GHz high field NMR magnet. IEEE Transactions on Applied Superconductivity, 34 (7) 4301507. doi: 10.1109/TASC.2024.3418385

Electromagnetic design and mechanical analysis of a 28.2 T/1.2 GHz high field NMR magnet

2024

Journal Article

Passive shimming optimization method of MRI based on genetic algorithm-sequential quadratic programming 

Zhao, Jie, Liu, Feng, Xia, Ling and Fan, Yifeng (2024). Passive shimming optimization method of MRI based on genetic algorithm-sequential quadratic programming . Zhejiang Daxue Xuebao (Gongxue Ban), 58 (6), 1305-1314. doi: 10.3785/j.issn.1008-973X.2024.06.020

Passive shimming optimization method of MRI based on genetic algorithm-sequential quadratic programming 

2024

Conference Publication

Fast controllable diffusion models for undersampled MRI reconstruction

Jiang, Wei, Xiong, Zhuang, Liu, Feng, Ye, Nan and Sun, Hongfu (2024). Fast controllable diffusion models for undersampled MRI reconstruction. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi56570.2024.10635891

Fast controllable diffusion models for undersampled MRI reconstruction

2024

Conference Publication

Single image compressed sensing MRI via a self-supervised deep denoising approach

Bran Lorenzana, Marlon, Liu, Feng and Chandra, Shekhar S. (2024). Single image compressed sensing MRI via a self-supervised deep denoising approach. 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 27-30 May 2024. Piscataway, NJ, United States: IEEE. doi: 10.1109/isbi56570.2024.10635749

Single image compressed sensing MRI via a self-supervised deep denoising approach

2024

Journal Article

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound

Xu, Wenlong, Bao, Xiaofan, Lou, Xiaomin, Liu, Xiaofang, Chen, Yuanyuan, Zhao, Xiaoqiang, Zhang, Chenlu, Pan, Chen, Liu, Wenlong and Liu, Feng (2024). Feature fusion method for pulmonary tuberculosis patient detection based on cough sound. PLoS One, 19 (5) e0302651, 1-12. doi: 10.1371/journal.pone.0302651

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound

2024

Journal Article

First Observation of a Three-Resonance Structure in e<sup>+</sup>e<sup>-</sup> → Nonopen Charm Hadrons

Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, M. R., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N. ... Zu, J. (2024). First Observation of a Three-Resonance Structure in e+e- → Nonopen Charm Hadrons. Physical Review Letters, 132 (19) 191902. doi: 10.1103/PhysRevLett.132.191902

First Observation of a Three-Resonance Structure in e<sup>+</sup>e<sup>-</sup> → Nonopen Charm Hadrons

2024

Journal Article

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

Xiong, Zhuang, Gao, Yang, Liu, Yin, Fazlollahi, Amir, Nestor, Peter, Liu, Feng and Sun, Hongfu (2024). Quantitative susceptibility mapping through model-based deep image prior (MoDIP). NeuroImage, 291 120583, 120583. doi: 10.1016/j.neuroimage.2024.120583

Quantitative susceptibility mapping through model-based deep image prior (MoDIP)

2024

Journal Article

A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction

Yuan, Tengfei, Yang, Jie, Chi, Jieru, Yu, Teng and Liu, Feng (2024). A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction. Magnetic Resonance Imaging, 108, 86-97. doi: 10.1016/j.mri.2024.02.004

A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction

Current funding

  • 2024 - 2027
    Quantum-Enabled Low-Field Magnetic Resonance Imaging for High-Performance Sport
    Quantum 2032 Challenge Program
    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

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
  • 2022 - 2025
    Evaluation of 7T 8-channel MRI coil for imaging of the human spine
    Aix-Marseille University
    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

Looking for a supervisor? Read our advice on how to choose a supervisor.

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

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

    Principal Advisor

  • Doctor Philosophy

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

    Principal Advisor

  • Doctor Philosophy

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

    Principal Advisor

  • Doctor Philosophy

    Passive shimming and electromagnetic contrast imaging at ultrahigh field MRI

    Principal Advisor

  • Doctor Philosophy

    Diagnosis of Arrhythmia using ECG signal classification by neural networks

    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

    AI-empowered super-resolution MRI

    Principal Advisor

  • 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 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

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)

Need help?

For help with finding experts, story ideas and media enquiries, contact our Media team:

communications@uq.edu.au