Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging.
Authors
Affiliations (11)
Affiliations (11)
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Siemens Medical Solutions USA, Inc., Malvern, PA, USA.
- Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA.
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. [email protected].
- The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. [email protected].
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA. [email protected].
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.