Research Areas

Department of Diagnostic Radiology

http://www.radiology.hku.hk

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.

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.

Thoracic imaging:
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