Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging.

Journal: Nature biomedical engineering
Published Date: Jun 20, 2025

Abstract

Magnetic resonance spectroscopic imaging has potential for non-invasive metabolic imaging of the human brain. Here we report a method that overcomes several long-standing technical barriers associated with clinical magnetic resonance spectroscopic imaging, including long data acquisition times, limited spatial coverage and poor spatial resolution. Our method achieves ultrafast data acquisition using an efficient approach to encode spatial, spectral and J-coupling information of multiple molecules. Physics-informed machine learning is synergistically integrated in data processing to enable reconstruction of high-quality molecular maps. We validated the proposed method through phantom experiments. We obtained high-resolution molecular maps from healthy participants, revealing metabolic heterogeneities in different brain regions. We also obtained high-resolution whole-brain molecular maps in regular clinical settings, revealing metabolic alterations in tumours and multiple sclerosis. This method has the potential to transform clinical metabolic imaging and provide a long-desired capability for non-invasive label-free metabolic imaging of brain function and diseases for both research and clinical applications.

Authors

  • Yibo Zhao
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • Yudu Li
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • Wen Jin
    Department of Clinical Medical Research Center/Inner Mongolia, Key Laboratory of Gene Regulation of the Metabolic Disease, Inner Mongolia People's Hospital, Hohhot 010010, P. R. China.
  • Rong Guo
    Department of Biochemistry and Molecular Biology, West China School of Basic Medical Sciences and Forensic Medicine, Sichuan University, Chengdu, 610041, China. Electronic address: rongguo@scu.edu.cn.
  • Chao Ma
  • Weijun Tang
    Department of Radiology, Huashan Hospital, Fudan University, Shanghai 200040, China.
  • Yao Li
    Center of Robotics and Intelligent Machine, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Science, No. 266 Fangzhen Road, Beibei District, Chongqing, 400714, China.
  • Georges El Fakhri
  • Zhi-Pei Liang
    Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals