Department of Diagnostic Radiology
How useful is an imaging test in identifying early disease, such as Alzheimer's or liver cancer?
What can functional imaging tell us about disease processes at the molecular level?
Can imaging reveal more than just conventional assessment for diagnosis?
Can use of modern feature extraction and artificial intelligence help advance imaging research?
Major Research Areas
Paediatric neuroimaging, Molecular imaging, including clinical and pre-clinical positron emission tomography (PET) and magnetic resonance imaging (MRI) in oncology.
Advanced MRI and other molecular imaging techniques in neurological diseases:
- Alzheimer's and other neurodegenerative diseases − volumetric MRI, functional MRI, MR perfusion, MR spectroscopy (MRS), amyloid and tau PET, susceptibility-weighted imaging (SWI), chemical exchange saturation transfer (CEST), artificial intelligence in dementia diagnosis.
- Demyelinating diseases, such as systemic lupus erythematosus, neuromyelitis optica and multiple sclerosis − arterial spin labelling (ASL), diffusion tensor imaging (DTI), MRS.
- Brain tumours − surrogate markers in anticancer drug trials using dynamic contrast enhanced (DCE) MR perfusion and MRS.
- Epilepsy − volumetric MRI, T1 Rho, MRI/PET.
Stroke and cardiovascular imaging:
- Stroke imaging − MR perfusion (ASL) imaging in evaluation of stroke, MR vessel wall imaging in aneurysm, vasculitis and intracranial atherosclerosis, blood-brain-barrier imaging in haemorrhagic stroke, stroke imaging database in Hong Kong West Cluster of the Hospital Authority.
- Carotid plaque characterization − multimodal MRI.
Molecular and functional imaging in gynae-oncology, bridging the imaging-pathological translation:
- Body diffusion-weighted MR imaging (DW-MRI) − application in oncology, quantification of diffusion parameters and technique optimisation on 3T MRI.
- DW-MRI quantification beyond mono-exponential model.
- Gynaecological oncology imaging − functional assessment of metabolic activity (PET/CT) and the use of advanced MRI techniques (e.g. DWI, IVIM, DCE-MRI) in disease diagnosis, treatment response assessment and disease prognostication.
- Qualitative and quantitative peritoneal imaging.
- Application of radiomics in gynaecological oncology.
Early clinical translatability of advanced imaging techniques leveraging emerging technologies and complex computational methods:
- Using imaging for early detection, cancer characteristics, and response assessment.
- Nasopharyngeal carcinoma
- Hepatocellular carcinoma
- Prostate cancer
- Low dose CT using iterative reconstruction.
- Cardiovascular imaging using CT and MRI.
- Quantitative radiomics and radiogenomics.
- Image processing, big data, machine learning and use of artificial intelligence.
Dr V. Vardhanabhuti
Cardiac magnetic resonance (CMR):
- Stress cardiac magnetic resonance and perfusion mapping/ quantification in (i) asymptomatic high risk diabetics, (ii) coronary microvascular disease and (iii) empagliflozin
- T1 and T2 mapping for the assessment of myocardial fibrosis and myocardial oedema. This research is currently focused on two types of patients: (1) heart failure with preserved ejection fraction (2) chronic kidney disease patients.
- CMR feature tracking for prognosis and diagnosis of diastolic dysfunction in heart failure with preserved ejection fraction and comparing this to catheter pressure measurements, echocardiography and CMR tagging.
- 4D flow in heart failure with preserved ejection fraction.
Cardiac computed tomography:
Calcium scoring in marathon runners on the progression of calcium in the coronary arteries.
Lung cancer screening and correlation with cancer genetics.
Gastrointestinal and hepatobiliary imaging:
- Using advanced imaging techniques including functional parameters and radiomics in cross-section imaging and PET for the assessment and prediction of treatment response in:
- Hepatocellular carcinoma
- Esophageal cancer
- Colorectal cancer
- Exploring the relationship between radiotracer uptake and underlying metabolism and genomic changes in hepatocellular carcinoma in human and mice models.
- Application of big data and machine learning in gastrointestinal and hepatobiliary cancers.
- Development of advanced imaging techniques in small bowel pathology including inflammatory bowel disease.
Developments of advanced MRI acquisition and reconstruction techniques:
- 2-D/3-D fast MR imaging technique development and reconstruction for fMRI and diffusion weighted/tensor imaging applications (single-shot and multi-shot echo-planar imaging).
- Fat quantification using MRI (acquisition and reconstruction developments).
- Periodically rotated overlapping parallel lines with enhanced reconstruction echo-planar imaging (Propeller-EPI) acquisition and reconstruction developments.
- Artifact reduction of fast MR imaging (Nyquist ghost reduction and geometric distortion correction).
- Reduction of motion artifact in abdominal imaging.
- Motion tracking using external device / MR pulse sequence.
- Image reconstruction of hybrid PET/MRI data.
Advanced MRI and Deep Learning:
- Deep-learning-based MRI reconstruction and quantification
- Deep-learning-based medical imaging segmentation and classification
- fMRI acquisition and modelling for brain function, BOLD and non-BOLD contrast mechanisms
- MRI pulse sequence design and novel imaging contrasts
- Molecular imaging, such as MR spectroscopy and chemical exchange saturation transfer
- MRI hardware development
Chairman of Departmental Research Postgraduate Committee
Dr P. Cao
Tel: 2255 3307