Tiffany So obtained her medical degree from the University of Melbourne, Australia and completed internship and basic training at the Royal Melbourne Hospital. She completed Radiology training at Alfred Health in Melbourne, training in diagnostic radiology with subspecialty training rotations in trauma imaging, neuroradiology, thoracic and lung transplant imaging and body imaging. Dr So is a Fellow of the Royal Australian and New Zealand College of Radiologists and a registered specialist in Diagnostic Radiology in Australia, New Zealand, and Hong Kong. She has a particular interest in neuroimaging, encompassing quantitative and advanced MRI brain imaging, evaluation of structural and functional abnormalities in multiple sclerosis, stroke and cerebrovascular disease diagnosis and assessment, imaging of traumatic head injury, and head and neck imaging. Her research interests also include artificial intelligence (AI) and medical image analysis for image-based detection and diagnosis of disease, development of medical healthcare technologies, and AI in education. She has completed a Master of Medicine in Radiology, with her work centred on traumatic injuries to the dural venous sinuses. 

In 2018, Dr So joined The Chinese University of Hong Kong. She is actively involved in undergraduate teaching and postgraduate radiology resident training. She serves as a diagnostic radiologist at the Department of Imaging and Interventional Radiology at the Prince of Wales Hospital, New Territories East Cluster, contributing to clinical services provided by the Hospital Authority. Additionally, she is a part of the neuroradiology team delivering diagnostic neuroimaging services to patients within the New Territories East Cluster.

Dr So is a member of the European Society of Radiology, Radiological Society of North America, The Australian and New Zealand Society of Neuroradiology, American Roentgen Ray Society, and The Medical Image Computing and Computer Assisted Intervention Society.

Current teaching profile includes undergraduate teaching (CUHK 4th and final year medical students), postgraduate radiology resident training (FRCR trainees), and visiting students from international institutions, in the form of lectures, small group and clinical tutorials, flipped classroom teaching, on the job teaching as well as informal tutorials. Dr So is a major contributor in curriculum design in the undergraduate radiology teaching program at CUHK, which incorporates both conventional deliveries and eLearning. Her recent teaching projects are focused on the incorporation of technology, artificial intelligence and game-based learning in education. These projects aim to develop innovative and readily accessible teaching materials to enhance teaching curriculums across several disciplines.

Dr So is actively involved in curriculum planning and the development of courseware material and assessments. She takes an interest in quality improvement in medical education and the techniques and approaches to maintain a quality teaching and student learning experience; in the past year attending several relevant workshops and seminars related to these areas.  She also supports collaborative work through actively engaging Students as Partners (SaP) in curriculum and educational material development, working together with other students and staff toward shared educational goals.

Recent Teaching Projects

Recent major teaching projects include the following:

• eLearning Modules for radiographic assessment of lines and tubes and their potential complications – Implementing artificial intelligence (AI) and game-based learning (Project Lead), supported by the University Grants Committee (UGC)- Teaching Development and Language Enhancement Grant (TDLEG)
• Interactive interdisciplinary learning of Nasogastric tube assessment (Project Lead) – project encompassing technology in education with implementation of AI deep learning models and game-based learning within an integrated mobile application (app) platform, supported by The Chinese University of Hong Kong Teaching and Learning Grant
  • Awarded best presentation at Tripartite Medical Education Conference – Actualising the Curriculum Continuum 2023
  • Recipient of Educational Technology Innovation Gold award, Teaching and Learning Innovation Expo, CUHK 2022
• Curriculum development in Abdominal Ultrasound (Project Lead), supported by The Chinese University of Hong Kong Teaching and Learning Grant
• eLearning in Thoracic Imaging (2019)

Dr So provides clinical radiology service in all areas of diagnostic radiology at the Prince of Wales Hospital. She specializes in neuroimaging and has specialty interest in advanced MRI brain imaging, multiple sclerosis, stroke and cerebrovascular imaging, head and neck imaging and artificial intelligence.

Recent service

• Junior Deputy Editor – European Radiology
• Editorial board member – European Radiology (Neuro Section)
• Program Chair, CLINICCAI, MICCAI 2022 – 25^th International Conference on Medical Image Computing and Computer Assisted Intervention
• Program Committee, CLINICCAI, MICCAI 2023 – 25^th International Conference on Medical Image Computing and Computer Assisted Intervention
• Editorial board member – BMC Medical Imaging
• Supervisor – MPhil/PhD students supervisor, Examiner – MPhil/PhD students
• Guest Associate Editor – Frontiers in Radiology
• Conference reviewer (e.g RSNA)

Dr So is actively engaged in interdisciplinary research encompassing neuroimaging, medical artificial intelligence and computer-aided diagnosis. She is particularly interested in the application of advanced imaging techniques to investigate into structural and functional changes in the brain.  Harnessing the capabilities of computational approaches, Dr So and her team focus on methods to integrate imaging, clinical and/or molecular data, and artificial intelligence and machine learning into research.

Current Research

Dr So has recently led and/or contributed to research projects in the following areas:  

• Applications of spin-lock imaging, including quantitative T1rho assessment of normal brain and demyelination in multiple sclerosis
• MPF mapping in multiple sclerosis
• Brain intravoxel incoherent motion MRI
• Evaluation of gadolinium-based contrast agent (Gd-CA) effects in brain imaging
• Machine learning and radiomics for brain tumour (glioma and mengingioma) grading, classification and predictive modelling
• Large-scale federated learning (FL) in glioma

Publications

1. Cai Z, Wong LM, Wong YH, Lee HL, Li KY, So TY*. Dual-Level Augmentation Radiomics Analysis for Multisequence MRI Meningioma Grading. Cancers. 2023 Nov 17;15(22):5459.
2. Wang L, Chen W, Qian Y, So TY*. Repeatability of quantitative T1rho magnetic resonance imaging in normal brain tissues at 3.0 T. Physica Medica. 2023 Aug 1;112:102641.
3. Ai QY, So TY, Hung KF, King AD. Normal size of benign upper neck nodes on MRI: parotid, submandibular, occipital, facial, retroauricular and level IIb nodal groups. Cancer Imaging. 2022 Dec 8;22(1):66.
4. Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, Foley P, Gruzdev A, Karkada D, Davatzikos et al. Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2022 Dec 5;13(1):7346.
5. So TY, Diacinti D, Leung JC, Iannacone A, Kripa E, Kwok TC, Diacinti D, Wang YX. Lower prevalence and severity of degenerative changes in the lumbar spine in elderly Hong Kong Chinese compared with age-matched Italian Caucasian women. Spine. 2022 Dec 15;47(24):1710-8.
6. So TY*. Editorial Comment: Iron-sensitive MR imaging of the primary motor cortex to differentiate hereditary spastic paraplegia from other motor neuron diseases. European Radiology. 2022 Dec;32(12):8055-7.
7. So TY*, Sawhney A, Wang L, Wang YX. Current concepts in imaging diagnosis and screening of blunt cerebrovascular injuries. Tomography. 2022 Feb 7;8(1):402-13.
8. Dou Q, So TY,Jiang M, Liu Q, Vardhanabhuti V, Kaissis G, Li Z, Si W, Lee HH, Yu K, Feng Z. Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ digital medicine. 2021 Mar 29;4(1):60.
9. Ai QY, Chen W, So TY, Lam WK, Jiang B, Poon DM, Qamar S, Mo FK, Blu T, Chan Q, Ma BB. Quantitative T1ρ MRI of the head and neck discriminates carcinoma and benign hyperplasia in the nasopharynx. American Journal of Neuroradiology. 2020 Dec 1;41(12):2339-44.
10. So TY, Ai QY, Lam WJ, Qamar S, Poon DM, Hui EP, Mo FK, Chan KA, King AD. Intravoxel incoherent motion diffusion-weighted imaging for discrimination of benign and malignant retropharyngeal nodes. Neuroradiology. 2020 Dec;62:1667-76.

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