Research

My research interests are to interact with nuclear spins and obtain information for tissue properties, brain metabolism, and neural functions.

Simultaneous Multi-Model Brain Imaging

Removing water suppression pulses in traditional magnetic resonance spectrscopic imaging (MRSI) techniques enables simultaneous acquisition of metabolite signals and water signals. Therefore, in a single scan, metabolic imaging can be achieved simultaneously with water-based imaging modalities, such as tissue susceptibility mapping (QSM), myelin water imaging (MWI), tissue relaxometry, etc.

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High-Resolution Metabolic Imaging at Ultra-High-Field

Magnetic Resonance Spectroscopic Imaging (MRSI) has long been limited by the low sensitivity of metabolites, leading low-resolution and long scan time. The ultra-high-field MR system could provide significant advantages for MRSI with improved SNR. Benefited from the development of subspace-based MRSI at 3T, high-resolution MRSI with improved SNR can be achieved at ultra-high-field.

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Dynamic Brain Metabolic Imaging and Functional Imaging

Study of the correlation between brain function and metabolism is of fundamental interest for neuroscience. Simultaneous dynamic metabolic imaging and functional imaging with high spatial-temporal enables investigation on the coupling of the hemodynamic and neurochemical responses to brain activity.

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Clinical Applications of High-Resolution Metabolic Imaging

Rapid, high-resolution, whole brain metabolic imaging promises to show significant clinical values in various diseases, such as stroke, brain tumor, traumatic brain injury, Alzheimer's disease, etc.

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Publication

Journal Papers

R. Guo, Y. Zhao, Y. Li, T. Wang, Y. Li, B. Sutton, Z.-P Liang. “Simultaneous QSM and metabolic imaging of the brain using SPICE: further improvement in data acquisition and processing,” Magn. Reson. Med., vol. 85, pp. 970-977, 2020
R. Guo, Y. Zhao, Y. Li, Y. Li, and Z.-P Liang. “Simultaneous metabolic and functional imaging of the brain using SPICE,” Magn. Reson. Med., vol. 82, pp. 1993-2002, 2019.
R. Guo, Y. Petibon, Y. Ma, G. Fakhri, K. Ying, and J. Ouyang. “MR-based motion correction for cardiac PET parametric imaging: a simulation study,” EJNMMI physics, vol. 5, num. 3, 2018.
Y. Zhao, R. Guo, Y. Li, K.R. Thulborn and Z.-P. Liang. “High-Resolution Sodium Imaging Using Anatomical and Sparsity Constraints for Denoising and Recovery of Novel Features,” Magn. Reson. Med, 2021.
Y. Li, T. Wang, T. Zhang, Y. Li, R. Guo, Y. Zhao, Z. Meng, J. Liu, X. Yu, Z.-P Liang, N. Parashkev. “Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy,” Brain, vol.143, pp.3224-3233, 2020
Z. Meng, R. Guo, Y. Li, Y. Guan, T. Wang, Y. Zhao, B. Sutton, Y. Li, Z.-P Liang. “Accelerating T2 mapping of the brain by integrating deep learning priors with low-rank and sparse modeling,” Magn. Reson. Med., vol. 85, pp. 1455-1467, 2020.
L. Tang, Y. Zhao, Y. Li, R. Guo, B. Clifford, G. Fakhri, C. Ma, Z.-P Liang, and J. Luo. “Accelerated J-resolved 1H-MRSI with limited and sparse sampling of (k, t1, t2)-space,” Magn. Reson. Med., vol. 85, pp. 30-41, 2020.
L. Fan, Y. Li, R. Guo, B. Clifford, and Z.-P Liang. “Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces,” Magn. Reson. Med., vol. 83, pp. 377-390, 2020.

Conference Papers

R. Guo, Y. Zhao, Y. Li, Y. Li, and Z.-P. Liang, “Rapid parametric mapping using the unsuppressed water signals in metabolic imaging of the brain,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1803, 2021.
R. Guo, Y. Zhao, Y. Li, P. Bhattacharyya, M. Lowe, H. M. Wiesner, Y. Li, X.-H. Zhu, W. Chen, and Z.-P. Liang, “Fast high-resolution 1H-MRSI of the human brain at 7T,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1354, 2021.
R. Guo, Y. Li, Y. Zhao, T. Wang, Y. Li, B. Sutton, and Z.-P. Liang. “High-resolution QSM for simultaneous QSM/MRSI,” Proc. Intl. Soc. Magn. Reson. Med. pp. 1891, 2020.
R. Guo, Y. Li, Y. Zhao, Y. Li, and Z.-P. Liang. “Making SPICE spicier with sparse sampling of (k, t)-space and learned subspaces,” Proc. Intl. Soc. Magn. Reson. Med. pp. 1899, 2020.
R. Guo, Y. Zhao, Y. Li, Y. Li, and Z.-P Liang. “High-resolution simultaneous mapping of brain function and metabolism,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4669, 2019.
R. Guo, Y. Zhao, Y. Li, Y. Li, and Z.-P Liang. “Mapping brain neurochemical and functional coupling using dynamic SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 3735, 2019.
R. Guo, Y. Petibon, Y. Ma, K. Ying, J. Ouyang. “The effect of MR-based motion correction on PET kinetic parameters estimation,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1868, 2016.
R. Guo, Y. Chen, J. Ouyang, G. Fakhri, K. Ying. “Joint reconstruction of PET and MRI with attenuation correction incorporating TOF information,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1784, 2016.
R. Guo, P. Han, Y. Chen, J. Ouyang, G. Fakrhri, K. Ying. “Simultaneous reconstruction of activity and attenuation involving MRI information as a prior,” Proc. Intl. Soc. Magn. Reson. Med., pp. 1797, 2016
H. M. Wiesner, R. Guo, Y. Li, Y. Zhao, Z.-P. Liang, X.-H. Zhu, and W. Chen, “High-resolution 3D phosphorus metabolic imaging of the human brain at 7T using SPICE,” Proc. Intl. Soc. Magn. Reson. Med., pp. 3731, 2021.
P. Bhattacharyya, R. Guo, Y. Li, Y. Zhao, Z.-P. Liang, and M. J. Lowe, “Reproducibility of high-resolution 1H-MRSI at 7T using SPICE,” Proc. Intl. Soc. Magn. Reson. Med., pp. 3805, 2021.
T. Zhang, R. Guo, Y. Li, Y. Zhao, Z.-P. Liang, and Y. Li, “Improved T2' mapping in simultaneous neurometabolic and qxygenation imaging experiments,” Proc. Intl. Soc. Magn. Reson. Med., pp. 4286, 2021.
Y. Zhao, R. Guo, Y. Li, K. R. Thulborn, and Z.-P. Liang, “High-Resolution Sodium Imaging Using Anatomical and Sparsity Constraints for Denoising and Recovery of Novel Features,” Proc. Intl. Soc. Magn. Reson. Med., pp. 133, 2021.
Y. Li, Y. Zhao, R. Guo, T. Wang, Y. Zhang, M. Chrostek, W.C. Low, X.-H. Zhu, W. Chen, and Z.-P. Liang, “A marriage of subspace modeling with deep learning to enable high-resolution dynamic deuterium MR spectroscopic imaging,” Proc. Intl. Soc. Magn. Reson. Med., pp. 2524, 2021.
Y. Li, J. Xiong, R. Guo, Y. Zhao, Y. Li, and Z.-P. Liang, “Improved estimation of myelin water fractions with learned parameter distributions,” Proc. Intl. Soc. Magn. Reson. Med., pp. 2075, 2021.
T. Zhang, R. Guo, T. Wang, Z. Lin, Y. Li, Y. Zhao, J. Liu, D. Wu, Z. Jin, X. Yu, Z.-P. Liang, and Y. Li, “Simultaneous oxygenation and metabolic imaging relates oxygen and neuronal metabolism in acute stroke,” Proc. Intl. Soc. Magn. Reson. Med., pp. 4203, 2021.
Y. Li, Y. Zhao, R. Guo, F. Yu, X.-H. Zhu, W. Chen, and Z.-P. Liang. “Rapid dynamic deuterium MR spectroscopic imaging using deep-SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 3739, 2020.
Z. Meng, Y. Li, R. Guo, T. Wang, Y. Zhao, F. Yu, B. Sutton, Y. Li, and Z.-P. Liang, “Accelerated T2 mapping by integrating two-stage learning with sparse modeling,” Proc. Intl. Soc. Magn. Reson. Med. pp. 5261, 2020.
J. Hu, M. Zhang, R. Guo, Y. Li, W. Sun, D. Wang, H. Huang, Y. Zhao, Z. Meng, B. Li, J. Liu, J. Luo, X. Yu, Z.-P. Liang, and Y. Li, “Fast 3D high-resolution metabolic imaging in Alzheimer's disease using SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 5364, 2020.
H. Huang, M. Zhang, R. Guo, Y. Li, Y. Zhao, J. Hu, H. Meng, X. Huang, X. Lin, W. Liu, B. Li, B. Sun, Y. Li, Z.-P. Liang, and J. Luo, “Simultaneous 18F-FDG-PET and 1H-MRSI metabolic imaging in epilepsy patients: a feasibility study ,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4130, 2020.
Y. Li, K. Kim, B. Clifford, R. Guo, Y. Gu, Z.-P. Liang, and X. Yu. “High-resolution dynamic 31P-MRSI of ischemia-reperfusion in rat using low-rank tensor model with deep learning priors,” Proc. Intl. Soc. Magn. Reson. Med. p. 4538, 2020.
Y. Zhao, Y. Li, J. Xiong, R. Guo, Y. Li, and Z.-P. Liang. “Rapid high-resolution mapping of brain metabolites and neurotransmitters using hybrid FID/SE-J-resolved spectroscopic signals,” Proc. Intl. Soc. Magn. Reson. Med. pp. 721 2020.
T. Zhang, T. Wang, Z. Lin, R. Guo, Y. Li, Y. Zhao, Z. Meng, J. Liu, D. Wu, Z. Jin, X. Yu, Z.-P. Liang, and Y. Li, “Simultaneous high-resolution 3D MRSI and oxygen extraction fraction mapping in acute stroke using SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4201, 2020.
Y. Zhao, Y. Li, J. Xiong, R. Guo, Y. Li, and Z.-P. Liang, “Spatiospectral reconstruction from hybrid FID/SE J-resolved MRSI data with limited coverage of (k,t,tJ)-space,” Proc. Intl. Soc. Magn. Reson. Med. p. 722, 2020.
P. Yu, T. Wang, Y. Hu, Y. Li, R. Guo, Y. Zhao, Z. Meng, X. Yu, and Z.-P. Liang, “Differentiation between Glioblastomas and Cerebral Metastases using high-resolution 3D MRSI,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4414, 2020.
Y. Li, Y. Guan, Z. Meng, F. Yu, R. Guo, Y. Zhao, T. Wang, Y. Li, and Z.-P. Liang. “An information theoretical framework for machine learning based MR image reconstruction,” Proc. Intl. Soc. Magn. Reson. Med. pp. 3858, 2020.
X.-H. Zhu, T. Wang, Y. Zhao, Y. Li, R. Guo, Y. Zhang, W. Low, Z.-P. Liang, and W. Chen, “Dynamic deuterium MRS imaging of brain tumor with enhanced sensitivity and spatiotemporal resolution,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4019, 2020.
Y. Guan, Y. Li, Z. Meng, T. Wang, R. Guo, R. Liu, Y. Li, Y. Du, and Z.-P. Liang, “Highly accelerated MPRAGE imaging of the brain incorporating deep learning priors with subject-specific novel features ,” Proc. Intl. Soc. Magn. Reson. Med. pp. 3498, 2020.
Y. Li, Z. Lin, T. Wang, T. Zhang, R. Guo, Y. Li, Y. Zhao, Z. Meng, J. Liu, X. Yu, and Z.-P. Liang, “Penumbra Identification in Acute Stroke Using Fast 3D 1H-MRSI,” Proc. Intl. Soc. Magn. Reson. Med. pp. 7190, 2020.
T. Wang, J. Hu, D. Wang, Y. Hu, J. Sun, J. Liu, Y. Li, R. Guo, Y. Zhao, Z. Meng, Z.-P. Liang and Y. Li, “Altered neurometabolic changes in acute mild traumatic brain injury patients: a SPICE study ,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4629, 2020.
Y. Li, R. Guo, Y. Zhao, T. Wang, Z. Meng, Y. Li, and Z.-P Liang. “Rapid high-resolution simultaneous acquisition of metabolites, myelin water fractions, and tissue susceptibility of the whole brain using SPICY 1H-MRSI,” Proc. Intl. Soc. Magn. Reson. Med. pp. 946, 2019.
Y. Li, R. Guo, Y. Zhao, Y. Chen, B. Clifford, T. Wang, C. Wang, Y. Du, Y. Li, and Z.-P Liang. “A model-based method for estimation of myelin water fractions,” Proc. Intl. Soc. Magn. Reson. Med. pp. 4902, 2019.
Y. Zhao, Y. Li, R. Guo, B. Clifford, X. Yu, and Z.-P Liang. “Accelerating high-resolution semi-LASER 1H-MRSI using SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 2478, 2019.
L. Tang, Y. Zhao, Y. Li, R. Guo, B. Clifford, C. Ma, Z.-P Liang, and J. Luo. “Accelerated J-resolved 1H-MRSI with limited and sparse sampling of (k, tj)-apace,” Proc. Intl. Soc. Magn. Reson. Med. pp. 2486, 2019.
T. Wang, J. Liu, T. Zhang, Z. Meng, D. Wang, K. Xue, Y. Li, R. Guo, Y. Zhao, X. Yu, Z.-P Liang and Y. Li. “Metabolic imaging of traumatic brain injuries using ultrahigh-resolution 1H-MRSI,” Proc. Intl. Soc. Magn. Reson. Med. pp. 76, 2019.
J. Liu, Y. Li, T. Wang, T. Zhang, Z. Meng, K. Xue, R. Guo, Y. Zhao, Y. Du, Q. Chen, Z.-P Liang and Y. Li. “Multimodal imaging of brain tumors using high-resolution 1H-MRSI without water suppression,” Proc. Intl. Soc. Magn. Reson. Med. pp. 856, 2019.
T. Zhang, T. Wang, Z. Meng, K. Xue, Y. Li, R. Guo, Y. Zhao, J. Liu, J. Zheng, X. Yu, Z.-P Liang and Y. Li. “Rapid high-resolution metabolic imaging of stroke using SPICE,” Proc. Intl. Soc. Magn. Reson. Med. pp. 2250, 2019.
Y. Li, F. Lam, B. Clifford, R. Guo, X. Peng, and Z.-P. Liang, “Constrained MRSI reconstruction using water side information with a kernel-based method,” Proc. Intl. Soc. Magn. Reson. Med., pp. 540, 2018.
Y. Li, F. Lam, R. Guo, B. Clifford, X. Peng, and Z.-P. Liang, “Removal of water sidebands from 1H-MRSI data acquired without water suppression,” Proc. Intl. Soc. Magn. Reson. Med., pp. 288, 2018.
F. Lam, Y. Li, R. Guo, B. Clifford, X. Peng, and Z.-P. Liang, “Further accelerating SPICE for ultrafast MRSI using learned spectral features,” Proc. Intl. Soc. Magn. Reson. Med., pp. 623, 2018.
X. Peng, Y. Li, F. Lam, R. Guo, B. Clifford, and Z.-P. Liang, “Constrained dipole inversion for quantitative susceptibility mapping using a “kernel+sparse” model,” Proc. Intl. Soc. Magn. Reson. Med., pp. 3406, 2018.

Resume

4259 Beckman Institute
University of Illinois at Urbana-Champaign
rongguo2@illinois.edu

Education

University of Illinois at Urbana-Champaign

Doctor of Philosophy in Electrical and Computer Engineering
Master of Science in Electrical and Computer Engineering
August 2016 - Present

Tsinghua University

Bachelor of Science in Engineering Physics
Bachelor of Business Administration
August 2012 - July 2016

Awards

  • Beckman Institute Graduate Fellowship, University of Illinois at Urbana-Champaign - 2021

  • Shun Lien Chuang Memorial Award, University of Illinois at Urbana-Champaign - 2021

  • Magna Cum Laude Paper Awards (Twice), ISMRM (International Society for Magnetic Resonance in Medicine) - 2020

  • Knight Fellowship in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign - 2020

  • Magna Cum Laude Paper Award, ISMRM (International Society for Magnetic Resonance in Medicine) - 2019

  • Outstanding Graduate Award, Tsinghua University - 2016

  • Academic Excellence Scholarship, Tsinghua University - 2015

  • 1st Place - “Challenge Cup” Student Academic Scientific Works Competition, Tsinghua University - 2015

  • National Scholarship of China - 2014

  • Academic Excellence Scholarship, Tsinghua University - 2014

  • 2nd Place - “Freescale Cup” National Intelligent Car Competition, CCD Group in North China - 2014

  • Academic Excellence Scholarship, Tsinghua University - 2013

  • 2nd Place - National Areal Undergraduate Physics Competition - 2013

Experience

University of Illinois at Urbana-Champaign
Graduate research in Professor Zhi-Pei Liang's group.
* Proposed a novel method to enable simultaneous functional and molecular imaging of the brain, which is expected to transform the investigation of the coupling between neurochemicals and neuroactivities in the brain.
* Proposed a novel acquisition scheme to obtain high-resolution maps of brain tissue susceptibility variations. The method increases the resolution of the state-of-the-art methods by a factor of 18 and will provide a new capability to detect metabolic abnormalities and microbleed in various brain diseases.
* Solved several key technical problems to enable clinical applications of 3D ultrafast high-resolution MR spectroscopic imaging (MRSI). The resulting technology laid the foundation for several clinical studies.
August 2016 - Present
Tsinghua University
Undergrad research in Professor Kui Ying's group.
* Developed and built a tabletop 0.15T MRI system.
* Developed a simulation platform for dynamic PET-MRI study and evaluated the effects of MR-based motion correction and the resulting improvement on PET kinetic model parameter estimation.
September 2014 - July 2016
Gordon Center at Massachusetts General Hospital
Research internship in Professor Georges El Fakhri's group.
* Developed a simulation platform for dynamic PET-MRI study and evaluated the effects of MR-based motion correction and the resulting improvement on PET kinetic model parameter estimation.
July 2015 - August 2015
Martinos Center at Massachusetts General Hospital
Research internship in Professor Larrance Wald's group.
* Built tabletop 0.15T MRI systems.
February 2014

Contact Me

4259 Beckman Institute
University of Illinois at Urbana-Champaign
Email: rongguo2@illinois.edu
Tel: 217-693-9862
WeChat: Air_OnTheWay