Congbo Cai | Neurotechnology | Best Researcher Award

Published on by Neuroscientists

Prof. Dr. Congbo Cai | Neurotechnology | Best Researcher Award

Prof. Dr. Congbo Cai | Xiamen University | China

Professor Congbo Cai is a distinguished researcher at the School of Electronic Science and Technology, Xiamen University, specializing in advanced Magnetic Resonance Imaging (MRI) technology development. His research encompasses ultra-fast imaging, multi-parametric quantitative MRI, deep learning reconstruction, novel neuroimaging techniques, and quantitative medical image analysis. He has led and contributed to numerous high-impact projects, including national key R&D programs, NSFC key projects, and international cooperative projects, with funding totaling several million yuan. His innovations include pioneering high-entropy encoding and overlapping-echo designs, enabling rapid, high-fidelity MRI mapping, and integrating physics-informed deep learning for enhanced image reconstruction and clinical applications. Professor Cai has published over 80 papers in leading journals such as NeuroImage, IEEE Transactions on Medical Imaging, and Medical Image Analysis. He holds 12 patents and serves on editorial boards, including Health and Metabolism, and as a guest editor for Frontiers in Neuroscience. His professional contributions extend to active membership and leadership roles in major MRI societies. His work has garnered significant academic recognition, with a citation count exceeding 2,300 across 872 documents, an h-index of 25, and an i10-index of 55. Professor Cai’s research continues to advance MRI science, bridging cutting-edge technology and clinical translation.

Profiles: Scopus | Google Scholar | Research Gate | Linked In

Featured publications

  • Author(s). (2018). Accelerating multi-slice spatiotemporally encoded MRI with simultaneous echo refocusing. Journal of Magnetic Resonance.

  • Author(s). (2018). Single-shot T2 mapping using overlapping-echo detachment planar imaging and a deep convolutional neural network. Magnetic Resonance in Medicine.

  • Author(s). (2018). Referenceless distortion correction of gradient-echo echo-planar imaging under inhomogeneous magnetic fields based on a deep convolutional neural network. Computers in Biology and Medicine.

  • Author(s). (2018). Weighted total variation using split Bregman fast quantitative susceptibility mapping reconstruction method. Chinese Physics B.

  • Author(s). (2018). Simultaneous single- and multi-contrast super-resolution for brain MRI images based on a convolutional neural network. Computers in Biology and Medicine.

  • Author(s). (2018). Motion-tolerant diffusion mapping based on single-shot overlapping-echo detachment (OLED) planar imaging. Magnetic Resonance in Medicine.