Ziyu Li

Postdoctoral Researcher

I am a postdoctoral researcher in the FMRIB Physics Group, working with Profs Karla Miller & Wenchuan Wu. I completed my DPhil in the same group in December 2024, supported by the Departmental Funding of Nuffield Department of Clinical Neurosciences. My research focuses on developing advanced diffusion MRI techniques to map the connectivity and microstructure of the living human brain with unprecedented detail. Recently, we achieved whole-brain in-vivo diffusion MRI at 0.53 mm isotropic resolution—one of the highest reported to date—using a segmented 3D multi-slab framework that delivers high image quality with minimal blurring.

Another line of my research develops deep learning methods to improve MRI, including denoising, image reconstruction, contrast transfer, super-resolution, and diffusion modeling.

Before joining FMRIB, I obtained my Bachelor of Engineering and Bachelor of Management degrees from Tsinghua University in 2021. During my undergraduate studies, I also worked as a research intern at Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Harvard Medical School from 2020 to 2021 (remotely due to the pandemic), with Profs. Qiyuan Tian & Susie Huang on developing deep learning methods for MRI analysis.

Key publications

Submillimeter diffusion MRI using an in-plane segmented 3D multi-slab acquisition and denoiser-regularized reconstruction

Li Z. et al, (2026), Medical Image Analysis, 107, 103834 - 103834

Enhance the image: Super resolution in MRI

Li Z. et al, (2025)

Self-Navigated 3D Diffusion MRI Using an Optimized CAIPI Sampling and Structured Low-Rank Reconstruction Estimated Navigator

Li Z. et al, (2025), IEEE Transactions on Medical Imaging, 44, 632 - 644

Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high‐resolution 3D diffusion MRI

Li Z. et al, (2023), Magnetic Resonance in Medicine, 90, 1484 - 1501

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat)

Li Z. et al, (2023), Medical Image Analysis, 86, 102744 - 102744

High‐fidelity fast volumetric brain MRI using synergistic wave‐controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)

Li Z. et al, (2022), Medical Physics, 49, 1000 - 1014

Effects of diffusion MRI spatial resolution on human brain short-range association fiber reconstruction and structural connectivity estimation

Zheng J. et al, (2026), Imaging Neuroscience, 4

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Ziyu Li

Key publications

Submillimeter diffusion MRI using an in-plane segmented 3D multi-slab acquisition and denoiser-regularized reconstruction

Enhance the image: Super resolution in MRI

Self-Navigated 3D Diffusion MRI Using an Optimized CAIPI Sampling and Structured Low-Rank Reconstruction Estimated Navigator

Sampling strategies and integrated reconstruction for reducing distortion and boundary slice aliasing in high‐resolution 3D diffusion MRI

Diffusion MRI data analysis assisted by deep learning synthesized anatomical images (DeepAnat)

High‐fidelity fast volumetric brain MRI using synergistic wave‐controlled aliasing in parallel imaging and a hybrid denoising generative adversarial network (HDnGAN)

Recent publications

MicroKAN: Mapping Human Brain Microstructure Using Diffusion MRI and Adaptive Nonlinear Modeling.

Asymmetric fiber orientation distribution estimation via unsupervised deep learning

Noise2Average: An iterative residual learning strategy for image denoising without clean data

A foundation model for efficient and assumption-free characterization of brain microstructure from diffusion MRI

Effects of diffusion MRI spatial resolution on human brain short-range association fiber reconstruction and structural connectivity estimation