AI in Medical Imaging · Healthcare Data
From neuroimaging quantification and digital twins to surgical vision and trustworthy clinical data pipelines.
# About
I am a Postdoctoral Researcher at NYCU’s Digital Medicine and Smart Healthcare Research Center, working on AI in medical imaging and healthcare data. The path began with family members affected by neurodegenerative disease — and grew into building trustworthy clinical AI across neuroimaging, surgical vision, and privacy-aware data systems.
Read my story# Research Focus
AI in Medical Imaging & Healthcare Data — three lenses, one practice.
# Why the signal matters
A bigger model doesn’t always mean better quantification — the right path to the signal does.
Structural MRI + PET: FreeSurfer-grade anatomy, classical SUVR / Centiloid when MRI is available.
Synthetic CT training → quantify on PET/CT without paired MRI; validated on external cohorts.
# Featured Work
Selected research projects — open a case study
MR-based DL segmentation for amyloid PET — FreeSurfer-aligned performance at cohort scale.
02Quantification without MRI via synthetic CT training — EJNMMI Physics 2026.
03Tracer-aware co-registration for simultaneous PET/MR — EJNMMI Physics 2025.
# Field notes
From the observation log — reflections on research and life
Collaboration, hiring, or just hello — pick an intent on the contact page.
Get In Touch