Imaging Biomarker

Introduction

Magnetic resonance imaging has been the mainstay for the diagnosis of neuropsychiatric diseases and disorders. Substantial efforts have been attempted to develop new MRI pulse sequences, and data modelling and analysis methods for probing novel and quantitative tissue contrasts in the hope of improving our understanding on the plausible pathophysiological mechanism of diseases and disorders.

Development & evaluation of novel imaging biomarker for

  • Stroke diagnosis and prognosis
  • Neuropsychiatric applications

Development of imaging biomarker for brain mapping

  • Brain coupling
  • Microstructural properties

Representative publications:

  1. Zhang H, Cao P, Mak HKF, Hui ES*. The structural-functional-connectivity coupling of the aging brain. Gero- science. (Q2 journal in geriatrics & gerontology; article link).
  2. Xia P, Hui ES, Chua BJ, Huang F, Wang Z, Zhang H, Yu H, Lau KK, Mak HKF, Cao P. Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping. J Magn Reson Imaging. 2023 Dec 27. doi: 10.1002/jmri.29198. (Q1 journal in radiology, nuclear medicine & medical imaging; article link).
  3. Liu C, Li T, Cao P, Hui ES, Wong YL, Wang Z, Xiao H, Zhi S, Zhou T, Li W, Lam SK, Cheung AL, Lee VH, Ying M, Cai J. Respiratory-correlated 4-dimensional magnetic resonance fingerprinting for liver cancer radiation therapy motion management. Int J Radiat Oncol Biol Phys. 2023 Oct 1;117(2):493-504. (Q1 journal in oncology; article link).
  4. Hui ES*. Advanced diffusion MRI for prediction of stroke recovery. J Magn Reson Imaging. J Magn Reson Imaging. 2023 May;57(5):1312-1319. (Q1journal in radiology, nuclear medicine & medical imaging; article link).
  5. Liu X, Chen S, Cui D, Hui ES, Chan Q, Chen NK, Chang HC. A robust self-referenced 2D nyquist ghost cor- rection for different MRI-biomarker measurements based on multi-band interleaved EPI. Front Phys. 2023 Jan;10:1298. (Q2 journal in physics, multidisciplinary; article link).
  6. Lian J, Deng J, Hui ES, Koohi-Moghadam M, She Y, Chen C, Vardhanabhuti V. Early stage NSCLS patients’ prognostic prediction with multi-information using transformer and graph neural network model. Elife. 2022 Oct 4:11:e80547. (Q1 journal in biology; article link).