乐播传媒

My PhD focuses on neuromodulation and neuroimaging. It examines the effects of theta-burst stimulation (TBS) of the dorsolateral prefrontal cortex on brain networks in adults with obesity. Using advanced multimodal neuroimaging approaches, including functional magnetic resonance imaging (fMRI), structural brain mapping, and quantitative magnetic resonance spectroscopy (MRS), I investigate the functional, structural, and neurochemical changes in food-related reward and cognitive control circuits following brain stimulation. By integrating these techniques, my research seeks to improve our understanding of how non-invasive brain stimulation modulates these brain networks in obesity and how these neural changes relate to eating behaviors.

I’m a PhD student with main focusing on utilizing whole-body MRI images for body composition analysis especially regional composition e.g. whole-leg muscle, whole-leg SAT, or VAT. This data could be achieved by automate segmentation algorithm, allowing physiological studies in large number of participants. I apply this method to DYNAMO, a project studying the influence of COVID infection in long-term (5-7 months and 11-13 months), to identify the association between regional composition and insulin resistance among post-infection patients.

I work on the development and clinical translation of high and ultra-high field MRI methods for the characterisation of brain lesions and epilepsy. My research centres on advancing Arterial Spin Labelling (ASL) at 7T, addressing B1+ field inhomogeneity challenges and optimising readout strategies for whole-brain and high spatial resolution perfusion mapping. These methods are integrated within a comprehensive multimodal imaging framework - combining vessel size imaging, oxygenation mapping, CEST, MRS, and 23Na MRI - to provide simultaneous anatomical, physiological, and metabolic characterisation of the brain in vivo. This work is conducted within the PHYSMET project (Ultrahigh magnetic field anatomical PHYSioMETabolic signal maximisation in brain lesions) and an epilepsy study, with the overarching aim of establishing robust, clinically translatable 7T MRI biomarkers for neurological disease.

My main research focus is on reducing the annotation burden associated with medical image segmentation through the use of minimally supervised machine learning techniques. There are several approaches used, including attentive initial image selection, clustering to improve performance on underrepresented classes of data, and semi-supervised learning to ensure full utilisation available unlabelled data. I work with several researchers at the MR Centre to streamline data analysis pipelines, and evaluate machine learning models on real world MR data.

My work investigates sweet sensing across multiple physiological systems and how these processes are altered in type 2 diabetes. At the 乐播传媒, I use sensory science approaches to investigate sweet taste intensity perception and liking, working within the team at the Sensory Science Centre. At the SPMIC, I use functional MRI to examine how the brain responds to sweet taste and whether patterns of neural activation differ between health and diabetes. As part of a collaboration with the University of Adelaide, I am investigating sweet taste receptors in the gastrointestinal tract and how they contribute to metabolic responses to different sweeteners in health and type 2 diabetes. I am also exploring the downstream effects of sweeteners on renal pathways involved in glucose and sweetener excretion, providing a broader understanding of sweet sensing beyond the oral cavity.

My research focuses on developing radio-frequency (RF) coils for ultra-high-field (UHF) MRI. The advent of the new 11.7T human MRI system offers novel opportunities to study human physiology, function, and metabolism, but also presents significant RF engineering challenges, including achieving homogeneous magnetic fields while maintaining safe specific absorption rate (SAR) levels. My research concerns developing innovative RF coils for both proton and X-nuclear MRI, enabling high-resolution anatomical, functional, and metabolic imaging. UHF MRI is particularly advantageous for X-nuclear imaging, where the increased signal-to-noise ratio substantially improves the feasibility and sensitivity of X-nuclear studies.