Yibo Zhao, Yudu Li, Wen Jin, Rong Guo, Chao Ma, Weijun Tang, Yao Li, Georges El Fakhri, Zhi-Pei Liang
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引用次数: 0
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.
期刊介绍:
Nature Biomedical Engineering is an online-only monthly journal that was launched in January 2017. It aims to publish original research, reviews, and commentary focusing on applied biomedicine and health technology. The journal targets a diverse audience, including life scientists who are involved in developing experimental or computational systems and methods to enhance our understanding of human physiology. It also covers biomedical researchers and engineers who are engaged in designing or optimizing therapies, assays, devices, or procedures for diagnosing or treating diseases. Additionally, clinicians, who make use of research outputs to evaluate patient health or administer therapy in various clinical settings and healthcare contexts, are also part of the target audience.