Rate My Professor Elaine Lui

Associate Professor Elaine Lui holds the position of Associate Professor in Advanced and Quantitative Imaging within the Department of Radiology at the University of Melbourne's Melbourne Medical School. She possesses qualifications of MBBS (Hons), MMed (Radiol), and FRANZCR. In addition to her academic role, she is Director of Research at the Royal Melbourne Hospital Department of Medical Imaging and Head of Research at the University of Melbourne Department of Radiology. Her academic interests include quantitative and advanced imaging.

Elaine Lui has made substantial contributions to research in advanced imaging techniques, particularly in neuroimaging, artificial intelligence in radiology, cardiac imaging, multiple sclerosis, epilepsy, and sodium MRI. Notable publications include the senior-authored "Impact of Different Artificial Intelligence User Interfaces on Lung Nodule Detection Accuracy" (2023, Radiology: Artificial Intelligence), which demonstrated that AI user interface design influences radiologists' diagnostic accuracy for lung nodules and masses on chest X-rays. Other key works are "3T sodium-MRI as predictor of neurocognition in nondemented older adults: a cross-sectional study" (2024, Brain Communications); "Amygdala enlargement in temporal lobe epilepsy: histopathology and surgical outcomes" (2024, Epilepsia); "How Normal Is Low-Normal Left Ventricular Ejection Fraction in Pregnancy?" (2024, Circulation: Genomic and Precision Medicine); "The topography of infratentorial lesions in depression and anxiety in multiple sclerosis" (2025, Journal of Neurology); "Relationship between brain atrophy and disability in a multi-site MS cohort" (2025); and "Perioperative IDH inhibition in treatment-naive IDH-mutant glioma" (2025, Nature Medicine). She has co-authored numerous papers on topics such as white matter abnormalities in epilepsy syndromes, fibre-specific white matter changes in multiple sclerosis patients with optic neuritis, and aortic root measurements on CT. Her research enhances diagnostic performance and clinical outcomes in radiology through innovative quantitative methods and AI integration.