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

IF 26.8 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Yibo Zhao, Yudu Li, Wen Jin, Rong Guo, Chao Ma, Weijun Tang, Yao Li, Georges El Fakhri, Zhi-Pei Liang
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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.

Abstract Image

用于高分辨率代谢脑成像的超快j分辨磁共振波谱成像
磁共振波谱成像具有对人脑进行无创代谢成像的潜力。在这里,我们报告了一种方法,克服了与临床磁共振光谱成像相关的几个长期存在的技术障碍,包括较长的数据采集时间,有限的空间覆盖和较差的空间分辨率。该方法通过对多分子的空间、光谱和j耦合信息进行有效编码,实现了超快的数据采集。物理知识的机器学习协同集成在数据处理中,以实现高质量分子图谱的重建。我们通过模拟实验验证了所提出的方法。我们获得了健康参与者的高分辨率分子图谱,揭示了不同大脑区域的代谢异质性。我们还在常规临床环境中获得了高分辨率的全脑分子图谱,揭示了肿瘤和多发性硬化症的代谢变化。这种方法有可能改变临床代谢成像,并为研究和临床应用提供一种长期渴望的无创脑功能和疾病的无标签代谢成像能力。
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来源期刊
Nature Biomedical Engineering
Nature Biomedical Engineering Medicine-Medicine (miscellaneous)
CiteScore
45.30
自引率
1.10%
发文量
138
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.
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