Brain metabolism reveals pathways by which neuronal and glial cells use nutrients, neurotransmitters, and other neuro metabolites to fuel their growth and maintain their function. In the occurrence of diseases, metabolic changes often precede changes in tissue morphology and function. The metabolic mechanism underlying different kinds of disorders in the nervous system still needs to be further investigated. MR metabolic imaging is recognized as the only way for in vivo non-invasive and radiation-free evaluation of brain metabolism. However, brain MR metabolic imaging is usually associated with a low signal-to-noise ratio and low spatial resolution due to the low concentration of brain metabolites. Recent advances in MR metabolic imaging methods greatly promote clinical studies on brain metabolism.
Method development in 1H editing MRS and high-resolution 1H MRSI significantly improves the precision of 1H MRS for brain metabolite quantification. Besides, the progress in multinuclear (including the nuclei of 13P, 23Na, 19F, 13C, etc.) MRI/MRS has achieved increased interest from clinical scientists for brain metabolic imaging. Chemical exchange saturation transfer (CEST) MRI can also be employed for indirect mapping of the brain metabolites. Moreover, advances in MR hardware (such as the increased magnetic field strength and the enhanced gradient systems) and data reconstruction (such as compressed sensing and AI-based reconstruction for under-sampled signals) significantly improve the feasibility of MR metabolic imaging for clinical applications. Overall, these advances in brain metabolic imaging demonstrate the translational potential to better understand brain disorders and guide diagnosis and treatment.
We will encourage the submission of article types of original research and review articles. The scope of the manuscripts in this Research Topic covers, but not limited to:
• Advances in MR metabolic imaging methods for neuroscience, including the technical advances in MRS, MR CEST imaging, MR multinuclear imaging, etc.
• Clinical studies on brain metabolism by using MR metabolic imaging methods. The subjects can be healthy volunteers or patients with different kinds of nervous system disorders, such as degeneration, functional disorders, vascular disorders, tumors, structural disorders, etc.
• Advances in the introduction of artificial intelligence to the workflow of brain metabolic imaging studies, including in the steps of data collection, data post-processing, data analysis, etc.
Liangjie Lin Ph.D. and Jiazheng Wang Ph.D. are MR research scientists in Philips Healthcare, a commercial producer of diagnosis imaging devices. This should not pose any conflict for this project, as they will maintain objectivity.
Brain metabolism reveals pathways by which neuronal and glial cells use nutrients, neurotransmitters, and other neuro metabolites to fuel their growth and maintain their function. In the occurrence of diseases, metabolic changes often precede changes in tissue morphology and function. The metabolic mechanism underlying different kinds of disorders in the nervous system still needs to be further investigated. MR metabolic imaging is recognized as the only way for in vivo non-invasive and radiation-free evaluation of brain metabolism. However, brain MR metabolic imaging is usually associated with a low signal-to-noise ratio and low spatial resolution due to the low concentration of brain metabolites. Recent advances in MR metabolic imaging methods greatly promote clinical studies on brain metabolism.
Method development in 1H editing MRS and high-resolution 1H MRSI significantly improves the precision of 1H MRS for brain metabolite quantification. Besides, the progress in multinuclear (including the nuclei of 13P, 23Na, 19F, 13C, etc.) MRI/MRS has achieved increased interest from clinical scientists for brain metabolic imaging. Chemical exchange saturation transfer (CEST) MRI can also be employed for indirect mapping of the brain metabolites. Moreover, advances in MR hardware (such as the increased magnetic field strength and the enhanced gradient systems) and data reconstruction (such as compressed sensing and AI-based reconstruction for under-sampled signals) significantly improve the feasibility of MR metabolic imaging for clinical applications. Overall, these advances in brain metabolic imaging demonstrate the translational potential to better understand brain disorders and guide diagnosis and treatment.
We will encourage the submission of article types of original research and review articles. The scope of the manuscripts in this Research Topic covers, but not limited to:
• Advances in MR metabolic imaging methods for neuroscience, including the technical advances in MRS, MR CEST imaging, MR multinuclear imaging, etc.
• Clinical studies on brain metabolism by using MR metabolic imaging methods. The subjects can be healthy volunteers or patients with different kinds of nervous system disorders, such as degeneration, functional disorders, vascular disorders, tumors, structural disorders, etc.
• Advances in the introduction of artificial intelligence to the workflow of brain metabolic imaging studies, including in the steps of data collection, data post-processing, data analysis, etc.
Liangjie Lin Ph.D. and Jiazheng Wang Ph.D. are MR research scientists in Philips Healthcare, a commercial producer of diagnosis imaging devices. This should not pose any conflict for this project, as they will maintain objectivity.
