Fangfang Tang

Dr. Fangfang Tang is an Adjunct Research Fellow in the School of Electrical Engineering and Computer Science at The University of Queensland (UQ). Her research focuses on advancing medical imaging technologies, particularly in MRI gradient coil design, biomedical engineering, and deep learning applications in healthcare. She holds a PhD in Electrical Engineering from UQ (2016), specializing in gradient coil design and intra-coil eddy currents in MRI systems.

Education: Dr. Tang earned her PhD in Electrical Engineering at UQ in 2016, with a thesis titled 'Gradient coil design and intra-coil eddy currents in MRI systems.'

Research Interests: Dr. Tang’s work emphasizes MRI system optimization, including gradient coil design for high-resolution imaging, safety in pediatric MRI, and integration of MRI with radiation therapy systems (MRI-Linac). She also explores deep learning techniques for medical image analysis, such as lung nodule detection and cardiac arrhythmia classification.

Publications: Her research spans 30+ peer-reviewed articles, focusing on gradient coil design, MRI safety, and AI-driven medical imaging solutions. Recent work includes advancements in 3D lung nodule detection networks and geometric distortion correction in MRI-Linac systems.

Collaborations: She collaborates with experts like Prof. Stuart Crozier and Prof. Feng Liu on MRI system development and biomedical engineering challenges.