Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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

Nature Biomedical Engineering (2025)Cite this article

Subjects

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.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: High-resolution metabolic imaging using J-resolved MRSI.
Fig. 2: Performance validation using phantom experiments.
Fig. 3: Brain metabolic heterogeneity revealed by high-resolution MRSI in healthy participants.
Fig. 4: High-resolution metabolic imaging of tumour.
Fig. 5: High-resolution metabolic imaging for relapsing–remitting MS.

Similar content being viewed by others

Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw data and quantification results from a representative participant are available at http://mri.beckman.illinois.edu/software.html. Source data supporting the results in Figs. 3–5 and Extended Data Figs. 6–10 are available from figshare at https://doi.org/10.6084/m9.figshare.24962580 (ref. 83). Source data are provided with this paper.

Code availability

The custom code to reconstruct metabolite and neurotransmitter signals from FID/SE hybrid MRSI data is publicly available at https://github.com/Zhi-Pei-Liang-s-Group/FID-SE-joint-reconstruction (ref. 84).

References

  1. Barker, P. B. et al. Acute stroke: evaluation with serial proton MR spectroscopic imaging. Radiology 192, 723–732 (1994).

    Article  CAS  PubMed  Google Scholar 

  2. Dani, K. A. & Warach, S. Metabolic imaging of ischemic stroke: the present and future. Am. J. Neuroradiol. 35, S37–S43 (2014).

    Article  CAS  PubMed  Google Scholar 

  3. Fulham, M. J. et al. Mapping of brain tumor metabolites with proton MR spectroscopic imaging: clinical relevance. Radiology 185, 675–686 (1992).

    Article  CAS  PubMed  Google Scholar 

  4. Horská, A. & Barker, P. B. Imaging of brain tumors: MR spectroscopy and metabolic imaging. Neuroimaging Clin. N. Am. 20, 293–310 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Govindaraju, V. et al. Volumetric proton spectroscopic imaging of mild traumatic brain injury. AJNR Am. J. Neuroradiol. 25, 730–737 (2004).

    PubMed  PubMed Central  Google Scholar 

  6. Lin, A. P. et al. Metabolic imaging of mild traumatic brain injury. Brain Imaging Behav. 6, 208–223 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Pan, J. W. & Kuzniecky, R. I. Utility of magnetic resonance spectroscopic imaging for human epilepsy. Quant. Imaging Med. Surg. 5, 313–322 (2015).

    PubMed  PubMed Central  Google Scholar 

  8. Kirov, I. I. et al. Whole brain neuronal abnormalities in focal quantified with proton MR spectroscopy. Epilepsy Res. 139, 85–91 (2018).

    Article  PubMed  Google Scholar 

  9. Murray, M. E. et al. Early Alzheimer’s disease neuropathology detected by proton MR spectroscopy. J. Neurosci. 34, 16247–16255 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Kantarci, K. et al. Regional metabolic patterns in mild cognitive impairment and Alzheimer’s disease. Neurology 55, 210–217 (2000).

    Article  CAS  PubMed  Google Scholar 

  11. Azevedo, C. J. et al. In vivo evidence of glutamate toxicity in multiple sclerosis. Ann. Neurol. 76, 269–278 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Srinivasan, R., Sailasuta, N., Hurd, R., Nelson, S. & Pelletier, D. Evidence of elevated glutamate in multiple sclerosis using magnetic resonance spectroscopy at 3 T. Brain 128, 1016–1025 (2005).

    Article  PubMed  Google Scholar 

  13. Egerton, A., Modinos, G., Ferrera, D. & McGuire, P. Neuroimaging studies of GABA in schizophrenia: a systematic review with meta-analysis. Transl. Psychiatry 7, e1147 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Wang, A. M. et al. Assessing brain metabolism with 7-T proton magnetic resonance spectroscopy in patients with first-episode psychosis. JAMA Psychiatry 76, 314 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Brennan, B. P., Rauch, S. L., Jensen, J. E. & Pope, H. G. A critical review of magnetic resonance spectroscopy studies of obsessive-compulsive disorder. Biol. Psychiatry 73, 24–31 (2013).

    Article  PubMed  Google Scholar 

  16. De Graaf, R. A. In Vivo NMR Spectroscopy: Principles and Techniques (Wiley, 2019).

  17. Mansfield, P. Spatial mapping of the chemical shift in NMR. Magn. Reson. Med. 1, 370–386 (1984).

    Article  CAS  PubMed  Google Scholar 

  18. Posse, S., Tedeschi, G., Risinger, R., Ogg, R. & Le Bihan, D. High speed 1H spectroscopic imaging in human brain by echo planar spatial-spectral encoding. Magn. Reson. Med. 33, 34–40 (1995).

    Article  CAS  PubMed  Google Scholar 

  19. Posse, S. et al. Proton echo-planar spectroscopic imaging of J-coupled resonances in human brain at 3 and 4 Tesla. Magn. Reson. Med. 58, 236–244 (2007).

    Article  CAS  PubMed  Google Scholar 

  20. Maudsley, A. A. et al. Mapping of brain metabolite distributions by volumetric proton MR spectroscopic imaging (MRSI). Magn. Reson. Med. 61, 548–559 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dydak, U., Weiger, M., Pruessmann, K. P., Meier, D. & Boesiger, P. Sensitivity-encoded spectroscopic imaging. Magn. Reson. Med. 46, 713–722 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Strasser, B. et al. (2 + 1)D-CAIPIRINHA accelerated MR spectroscopic imaging of the brain at 7T. Magn. Reson. Med. 78, 429–440 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Peng, X., Lam, F., Li, Y., Clifford, B. & Liang, Z.-P. Simultaneous QSM and metabolic imaging of the brain using SPICE. Magn. Reson. Med. 79, 13–21 (2018).

    Article  PubMed  Google Scholar 

  24. Hingerl, L. et al. Density-weighted concentric circle trajectories for high resolution brain magnetic resonance spectroscopic imaging at 7T. Magn. Reson. Med. 79, 2874–2885 (2018).

    Article  CAS  PubMed  Google Scholar 

  25. Hangel, G. et al. Ultra-high resolution brain metabolite mapping at 7 T by short-TR Hadamard-encoded FID-MRSI. NeuroImage 168, 199–210 (2018).

    Article  PubMed  Google Scholar 

  26. Klauser, A. et al. Fast high-resolution brain metabolite mapping on a clinical 3T MRI by accelerated 1H-FID-MRSI and low-rank constrained reconstruction. Magn. Reson. Med. 81, 2841–2857 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Niess, F. et al. Noninvasive 3-dimensional 1H-magnetic resonance spectroscopic imaging of human brain glucose and neurotransmitter metabolism using deuterium labeling at 3T: feasibility and interscanner reproducibility. Invest. Radiol. 58, 431–437 (2023).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Bednarik, P. et al. 1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolism at 7 T in the human brain. Nat. Biomed. Eng. 7, 1011–1013 (2023).

    Article  Google Scholar 

  29. Hangel, G. et al. Emerging methods and applications of ultra-high field MR spectroscopic imaging in the human brain. Anal. Biochem. 638, 114479 (2022).

    Article  CAS  PubMed  Google Scholar 

  30. Haase, A., Frahm, J., Hanicke, W. & Matthaei, D. 1H NMR chemical shift selective (CHESS) imaging. Phys. Med. Biol. 30, 341–344 (1985).

    Article  CAS  PubMed  Google Scholar 

  31. Kumaragamage, C. et al. Robust outer volume suppression utilizing elliptical pulsed second order fields (ECLIPSE) for human brain proton MRSI. Magn. Reson. Med. 83, 1539–1552 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Aue, W. P., Bartholdi, E. & Ernst, R. R. Two-dimensional spectroscopy. Application to nuclear magnetic resonance. J. Chem. Phys. 64, 2229–2246 (1976).

    Article  CAS  Google Scholar 

  33. Bottomley, P. A. Spatial localization in NMR spectroscopy in vivo. Ann. N. Y. Acad. Sci. 508, 333–348 (1987).

    Article  CAS  PubMed  Google Scholar 

  34. Schulte, R. F., Lange, T., Beck, J., Meier, D. & Boesiger, P. Improved two-dimensional J-resolved spectroscopy. NMR Biomed. 19, 264–270 (2006).

    Article  CAS  PubMed  Google Scholar 

  35. Jensen, J. E., Frederick, B. D., Wang, L., Brown, J. & Renshaw, P. F. Two-dimensional, J-resolved spectroscopic imaging of GABA at 4 Tesla in the human brain. Magn. Reson. Med. 54, 783–788 (2005).

    Article  CAS  PubMed  Google Scholar 

  36. Wang, Z., Li, Y. & Lam, F. High-resolution, 3D multi-TE 1H MRSI using fast spatiospectral encoding and subspace imaging. Magn. Reson. Med. 87, 1103–1118 (2022).

    Article  CAS  PubMed  Google Scholar 

  37. Nelson, S. J. Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI. NMR Biomed. 24, 734–749 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Thomas, M. A., Ryner, L. N., Mehta, M. P., Turski, P. A. & Sorenson, J. A. Localized 2D J-resolved 1H MR spectroscopy of human brain tumors in vivo. J. Magn. Reson. Imaging 6, 453–459 (1996).

    Article  CAS  PubMed  Google Scholar 

  39. Ke, Y. et al. Frontal lobe GABA levels in cocaine dependence: a two-dimensional, J-resolved magnetic resonance spectroscopy study. Psychiatry Res. Neuroimaging 130, 283–293 (2004).

    Article  CAS  Google Scholar 

  40. Ma, C., Lam, F., Johnson, C. L. & Liang, Z.-P. Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model. Magn. Reson. Med. 75, 488–497 (2016).

    Article  PubMed  Google Scholar 

  41. Li, Y., Lam, F., Clifford, B. & Liang, Z.-P. A subspace approach to spectral quantification for MR spectroscopic imaging. IEEE Trans. Biomed. Eng. 64, 2486–2489 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Lam, F. et al. Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces. Magn. Reson. Med. 83, 377–390 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Mori, S. et al. Stereotaxic white matter atlas based on diffusion tensor imaging in an ICBM template. NeuroImage 40, 570–582 (2008).

    Article  PubMed  Google Scholar 

  44. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  45. Degaonkar, M. N., Pomper, M. G. & Barker, P. B. Quantitative proton magnetic resonance spectroscopic imaging: regional variations in the corpus callosum and cortical gray matter. J. Magn. Reson. Imaging 22, 175–179 (2005).

    Article  PubMed  Google Scholar 

  46. Warburg, O., Wind, F. & Negelein, E. The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Goldenberg, M. M. Multiple sclerosis review. Pharm. Ther. 37, 175–184 (2012).

    Google Scholar 

  48. Narayana, P. A. Magnetic resonance spectroscopy in the monitoring of multiple sclerosis. J. Neuroimaging 15, 46S–57S (2005).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Brand, A., Richter-Landsberg, C. & Leibfritz, D. Multinuclear NMR studies on the energy metabolism of glial and neuronal cells. Dev. Neurosci. 15, 289–298 (1993).

    Article  CAS  PubMed  Google Scholar 

  50. Simmons, M. L., Frondoza, C. G. & Coyle, J. T. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 45, 37–45 (1991).

    Article  CAS  PubMed  Google Scholar 

  51. Mathur, D., Lopez-Rodas, G., Casanova, B. & Marti, M. B. Perturbed glucose metabolism: insights into multiple sclerosis pathogenesis. Front. Neurol. 5, 250 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Nijland, P. G. et al. Differential expression of glucose-metabolizing enzymes in multiple sclerosis lesions. Acta Neuropathol. Commun. 3, 79 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Balasa, R., Barcutean, L., Mosora, O. & Manu, D. Reviewing the significance of blood–brain barrier disruption in multiple sclerosis pathology and treatment. Int. J. Mol. Sci. 22, 8370 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Simone, I. L. et al. High resolution proton MR spectroscopy of cerebrospinal fluid in MS patients. Comparison with biochemical changes in demyelinating plaques. J. Neurol. Sci. 144, 182–190 (1996).

    Article  PubMed  Google Scholar 

  55. Langer, D. L., Rakaric, P., Kirilova, A., Jaffray, D. A. & Damyanovich, A. Z. Assessment of metabolite quantitation reproducibility in serial 3D 1H-MR spectroscopic imaging of human brain using stereotactic repositioning. Magn. Reson. Med. 58, 666–673 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Ratai, E. et al. Automatic repositioning of MRSI voxels in longitudinal studies: impact on reproducibility of metabolite concentration measurements. J. Magn. Reson. Imaging 27, 1188–1193 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hensley, C. T., Wasti, A. T. & DeBerardinis, R. J. Glutamine and cancer: cell biology, physiology, and clinical opportunities. J. Clin. Invest. 123, 3678–3684 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Altman, B. J., Stine, Z. E. & Dang, C. V. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat. Rev. Cancer 16, 619–634 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Blanchart, A. et al. Endogenous GABAA receptor activity suppresses glioma growth. Oncogene 36, 777–786 (2017).

    Article  CAS  PubMed  Google Scholar 

  60. Hujber, Z. et al. GABA, glutamine, glutamate oxidation and succinic semialdehyde dehydrogenase expression in human gliomas. J. Exp. Clin. Cancer Res. 37, 271 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Huang, Q., Chen, L., Liang, J., Huang, Q. & Sun, H. Neurotransmitters: potential targets in glioblastoma. Cancers 14, 3970 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Perazella, M. Gadolinium-contrast toxicity in patients with kidney disease: nephrotoxicity and nephrogenic systemic fibrosis. Curr. Drug Saf. 3, 67–75 (2008).

    Article  CAS  PubMed  Google Scholar 

  63. Dillman, J. R., Ellis, J. H., Cohan, R. H., Strouse, P. J. & Jan, S. C. Frequency and severity of acute allergic-like reactions to gadolinium-containing IV contrast media in children and adults. Am. J. Roentgenol. 189, 1533–1538 (2007).

    Article  Google Scholar 

  64. Barker, P. B. et al. Clinical MR Spectroscopy: Techniques and Applications (Cambridge Univ. Press, 2009).

  65. Ramesh, K. et al. A multi-institutional pilot clinical trial of spectroscopic MRI-guided radiation dose escalation for newly diagnosed glioblastoma. Neurooncol. Adv. 4, vdac006 (2022).

    PubMed  PubMed Central  Google Scholar 

  66. Graves, E. E. et al. Serial proton MR spectroscopic imaging of recurrent malignant gliomas after gamma knife radiosurgery. Am. J. Neuroradiol. 22, 613–624 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Tang, L. et al. Accelerated J-resolved 1H-MRSI with limited and sparse sampling of (k, t1, t2)-space. Magn. Reson. Med. 85, 30–41 (2021).

    Article  CAS  PubMed  Google Scholar 

  68. Li, Y. et al. T1 and T2 metabolite relaxation times in normal brain at 3T and 7T. J. Mol. Imaging Dyn. S1, 002 (2012).

    Google Scholar 

  69. Guo, R. et al. Simultaneous QSM and metabolic imaging of the brain using SPICE: further improvements in data acquisition and processing. Magn. Reson. Med. 85, 970–977 (2021).

    Article  PubMed  Google Scholar 

  70. Schulte, R. F. & Boesiger, P. ProFit: two-dimensional prior-knowledge fitting of J-resolved spectra. Magn. Reson. Med. 19, 255–263 (2006).

    CAS  Google Scholar 

  71. Soher, B. J. et al. Vespa: integrated applications for RF pulse design, spectral simulation and MRS data analysis. Magn. Reson. Med. 90, 823–838 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Moulin, P. & Veeravalli, V. Statistical Inference for Engineers and Data Scientists 319–357 (Cambridge Univ. Press, 2018).

  73. Golub, G. H. & Reinsch, C. Singular value decomposition and least squares solutions. Numer. Math. 14, 403–420 (1970).

    Article  Google Scholar 

  74. Haldar, J. P., Hernando, D., Song, S.-K. & Liang, Z.-P. Anatomically constrained reconstruction from noisy data. Magn. Reson. Med. 59, 810–818 (2008).

    Article  PubMed  Google Scholar 

  75. Vogel, C. R. Computational Methods for Inverse Problems (Society for Industrial and Applied Mathematics, 2002).

  76. Bernstein, M., King, K. & Zhou, X. Handbook of MRI Pulse Sequences (Elsevier, 2004).

  77. Valverde, S. et al. Automated tissue segmentation of MR brain images in the presence of white matter lesions. Med. Image Anal. 35, 446–457 (2017).

    Article  PubMed  Google Scholar 

  78. Meier, D. S. et al. Dual-sensitivity multiple sclerosis lesion and CSF segmentation for multichannel 3T brain MRI. J. Neuroimaging 28, 36–47 (2018).

    Article  PubMed  Google Scholar 

  79. Lipp, I. et al. Comparing MRI metrics to quantify white matter microstructural damage in multiple sclerosis. Hum. Brain Mapp. https://doi.org/10.1002/hbm.24568 (2019).

  80. Van Walderveen, M. A. A. et al. Neuronal damage in T1-hypointense multiple sclerosis lesions demonstrated in vivo using proton magnetic resonance spectroscopy. Ann. Neurol. 46, 79–87 (1999).

    Article  PubMed  Google Scholar 

  81. Schiavi, S. et al. Non-invasive quantification of inflammation, axonal and myelin injury in multiple sclerosis. Brain 144, 213–223 (2021).

    Article  PubMed  Google Scholar 

  82. Calvi, A. et al. In vivo imaging of chronic active lesions in multiple sclerosis. Mult. Scler. J. 28, 683–690 (2022).

    Article  CAS  Google Scholar 

  83. Zhao, Y. et al. Dataset for “Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging”. figshare https://doi.org/10.6084/m9.figshare.24962580 (2024).

  84. Zhao, Y. et al. Code for FID/SE hybrid MRSI reconstruction. GitHub https://github.com/Zhi-Pei-Liang-s-Group/FID-SE-joint-reconstruction (2025).

Download references

Acknowledgements

We thank B. Bo (School of Biomedical Engineering, Shanghai Jiao Tong University) for helping with tumour and MS lesion segmentation. We thank W. Li and D. Wang (School of Biomedical Engineering, Shanghai Jiao Tong University) for helping with healthy participant brain segmentation. We thank G. Xiao (School of Molecular and Cellular Biology, University of Illinois at Urbana-Champaign) and Y. Zhang and Z. Xu (Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign) for helping with phantom preparation. We thank Z. Zhu (Huashan Hospital, Fudan University) for helping with tumour biopsy. This work was supported, in part, by the Beckman Institute for Advanced Science and Technology Postdoctoral Fellows Program with support from the Arnold and Mabel Beckman Foundation (Y.Z.).

Author information

Authors and Affiliations

  1. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Yibo Zhao, Yudu Li, Wen Jin, Rong Guo & Zhi-Pei Liang

  2. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Yudu Li

  3. The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Yudu Li & Zhi-Pei Liang

  4. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Wen Jin & Zhi-Pei Liang

  5. Siemens Medical Solutions USA, Inc., Malvern, PA, USA

    Rong Guo

  6. Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, USA

    Chao Ma & Georges El Fakhri

  7. Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China

    Weijun Tang

  8. School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yao Li

Authors
  1. Yibo Zhao
    View author publications

    Search author on:PubMed Google Scholar

  2. Yudu Li
    View author publications

    Search author on:PubMed Google Scholar

  3. Wen Jin
    View author publications

    Search author on:PubMed Google Scholar

  4. Rong Guo
    View author publications

    Search author on:PubMed Google Scholar

  5. Chao Ma
    View author publications

    Search author on:PubMed Google Scholar

  6. Weijun Tang
    View author publications

    Search author on:PubMed Google Scholar

  7. Yao Li
    View author publications

    Search author on:PubMed Google Scholar

  8. Georges El Fakhri
    View author publications

    Search author on:PubMed Google Scholar

  9. Zhi-Pei Liang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

Y.Z. and Z.-P.L. conceived of the project. Y.Z., R.G. and Z.-P.L. designed and developed the data acquisition scheme. Yudu Li, Y.Z., W.J. and Z.-P.L. developed the data processing scheme. Y.Z., R.G., Yudu Li, C.M., W.T., Yao Li and Z.-P.L. designed the experimental studies. Y.Z. performed the phantom experiments. Y.Z., C.M. and W.T. performed the healthy participant experiments. W.T. and Yao Li performed the tumour and MS patient experiments. Y.Z. analysed the phantom and healthy participant data. Y.Z., Yudu Li, R.G. and W.J. processed the tumour and MS patient data. Y.Z., Yudu Li, R.G., W.J., W.T., C.M., G.E.F., Yao Li and Z.-P.L. analysed the experimental results. Y.Z., Yao Li, Yudu Li, R.G., C.M. and Z.-P.L. wrote the paper. All authors discussed the results and revised the paper. Z.-P.L. conceived of the imaging framework and directed the project.

Corresponding author

Correspondence to Zhi-Pei Liang.

Ethics declarations

Competing interests

R.G. declares the following competing interests. R.G. is currently employed by Siemens Medical Solutions USA, Inc. The work reported in this paper was performed when he was a graduate student at the University of Illinois and is not related to his employment at Siemens Medical Solutions USA, Inc. The other authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Petr Bednarik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Additional spectroscopic phantom results.

a, Structure of a home-made phantom, including fourteen tubes containing neurometabolite solutions at physiological concentrations. These tubes are attached to each other to form the letter “I”. The widths of these tubes are 10 mm. The solutions filled in the left and right-side tubes are different, representing “healthy tissues” and “pathological tissues”. Ground truth molecular concentrations for “healthy tissue” solution are NAA 15 mM, Cr 12 mM, Cho 3 mM, mI 8 mM, Glu 10 mM, GABA 2 mM and Lac 1 mM; for “pathological tissue” solution are NAA 5 mM, Cr 4 mM, Cho 9 mM, mI 4 mM, Glu 5 mM, GABA 3 mM and Lac 5 mM. b, Averaged FID spectra of the measured signals from the “healthy tissue” tubes and the “pathological tissue” tubes, respectively. c, Measured molecular concentrations in the “healthy tissue” tubes (using “pathological tissue” tubes for normalization, n = 343) matched well with the ground truth concentrations. Error bars are shown as mean ± standard deviation. The identity line is shown as a grey line. d, Molecular maps obtained using the proposed method. Distinct molecular levels can be resolved from different tubes, matching our expected concentration differences. Adjacent tubes are clearly separated spatially, demonstrating our high spatial resolution capability. e, Molecular maps obtained using a conventional low-resolution MRSI method. The boundaries between “healthy tissue” and “pathological tissue” cannot be clearly defined based on the molecular maps. Abbreviations: NAA, N-acetyl aspartate; Cr, creatine; Cho, choline; mI, myo-inositol; Glu, glutamate; Gln, glutamine; GABA, gamma-aminobutyric acid; Lac, lactate; FID, free induction decay; a.u., arbitrary unit.

Extended Data Fig. 2 Phantom to demonstrate Lac/lipid separation.

a, Structure of the Lac/lipid phantom, which is made of a commercial spectroscopic phantom with Lac, as well as a bag of vegetable oil attached to it to mimic the subcutaneous lipid in human data. b, Original map before water and lipid removal, showing the unsuppressed water and lipid signals. c, Estimated lipid signals using the proposed method. d, Lac map obtained using the proposed method. e, Localized FID spectra from the yellow voxel indicated in the image in b. No noticeable lipid artifacts could be observed in the Lac map and localized spectra. f-h, Lipid signals, Lac map and localized spectra similar to c-e, but obtained with lower spatial resolution. Substantial lipid leakage was observed in the low-resolution data. Abbreviations: Lac, lactate; FID, free induction decay; a.u., arbitrary unit.

Extended Data Fig. 3 Representative 3D high-resolution metabolite and neurotransmitter maps obtained from a healthy subject.

T1w anatomical image as well as NAA, Cr, Cho, mI, Glu, Gln and GABA maps obtained by the proposed method are displayed. Eight slices from the 3D datasets are shown. As can be seen, high-resolution molecular maps were obtained by the proposed method, showing good tissue contrast. Abbreviations: 3D, three-dimensional; T1w, T1-weighted; NAA, N-acetyl aspartate; Cr, creatine; Cho, choline; mI, myo-inositol; Glu, glutamate; Gln, glutamine; GABA, gamma-aminobutyric acid.

Extended Data Fig. 4 Averaged 3D high-resolution metabolite and neurotransmitter maps from all healthy subjects included in our reproducibility study (n = 26).

The molecular maps from different subjects were spatially aligned to produce the averaged maps. Eight slices from the 3D averaged dataset are shown. As can be seen, the averaged maps of NAA, Cr, Cho, mI, Glu, Gln and GABA showed high spatial resolution and good tissue contrast. Abbreviations: 3D, three-dimensional; T1w, T1-weighted; NAA, N-acetyl aspartate; Cr, creatine; Cho, choline; mI, myo-inositol; Glu, glutamate; Gln, glutamine; GABA, gamma-aminobutyric acid.

Extended Data Fig. 5 Biopsy results obtained from the oligodendroglioma patients.

a, Hematoxylin and Eosin (H&E) histology result from the grade-II oligodendroglioma patient (scale bar = 50 µm). b, H&E histology result from the grade-III oligodendroglioma patient, with the same magnification factor as in a. Noticeably increased number of cells can be seen in the grade-III oligodendroglioma patient result. Histological analysis was performed once for each patient. c, Cell density counting results for these two oligodendroglioma patients. More than a factor of two increase in cell density can be found for the grade-III oligodendroglioma patient.

Extended Data Fig. 6 Quantitative analysis of metabolic imaging results obtained from oligodendroglioma patients.

a, T1C, T1w and T2w FLAIR structural images obtained from grade II oligodendroglioma (tumour 1) and grade III oligodendroglioma (tumour 2) patients, along with the normal and tumour ROIs for regional analysis. b, Box plots of the concentrations of different metabolites in these two oligodendroglioma patients. Metabolite concentrations were shown in arbitrary units. Box plots are represented by median and interquartile range, with ±1.5 interquartile range as whiskers. Significantly elevated Cho (87.6%, P < 0.001) and Lac (107.5%, P < 0.001) levels were observed in grade III oligodendroglioma (n = 337) in comparison with grade II oligodendroglioma (n = 374). Unpaired two-tailed Student’s t-tests were performed. Abbreviations: T1C, T1-weighted contrast-enhanced; T1w, T1-weighted; T2w, T2-weighted; FLAIR, fluid attenuated inversion recovery; ROI: region of interest; NAA, N-acetyl aspartate; Cho, choline; Lac, lactate.

Extended Data Fig. 7 Quantitative analysis of metabolic imaging results obtained from a glioblastoma patient.

a, T1C, T1w, T2w FLAIR and T2w SPACE structural images obtained from the glioblastoma patient. ROIs, including normal (red), oedema (blue) and enhanced tumour (yellow), were determined from structural images. b, Box plots of the concentrations of different metabolites and neurotransmitters in different regions of the glioblastoma. Metabolite and neurotransmitter concentrations were shown in arbitrary units. Box plots are represented by median and interquartile range, with ±1.5 interquartile range as whiskers. Compared with the normal region, elevated levels of Cho (45.7%, P < 0.001), Lac (1573.2%, P < 0.001) and Gln (79.9%, P < 0.001) and decreased levels of NAA (58.2%, P < 0.001) were observed in the enhancing tumour region (n = 97). Decreased levels of NAA (39.8%, P < 0.001) and Cho (13.6%, P < 0.001) were observed in the oedema region (n = 492). Unpaired two-tailed Student’s t-tests were performed. Abbreviations: T1C, T1-weighted contrast-enhanced; T1w, T1-weighted; T2w, T2-weighted; FLAIR, fluid attenuated inversion recovery; SPACE, sampling perfection with application optimized contrasts using different flip angle evolutions; ROI: region of interest; NAA, N-acetyl aspartate; Cho, choline; Gln, glutamine; Lac, lactate.

Extended Data Fig. 8 Quantitative analysis of metabolic imaging for detecting MS lesions prior to the changes observable in conventional structural images.

a, Identification of the prelesion regions (yellow), based on the difference between initial and follow-up T2w images obtained from the patient. b, Regional average (solid line) and standard deviation (shaded area) of FID spectra in NAWM and prelesion regions, respectively. c, Box plots of mI/NAA and Lac levels in NAWM (n = 1103), prelesion 1 (n = 83) and prelesion 2 (n = 45) regions. Lac levels were shown in arbitrary units. Box plots are represented by median and interquartile range, with ±1.5 interquartile range as whiskers. Elevated mI/NAA and Lac were detected in the prelesion regions at the time of the initial scan, prior to noticeable changes in the structural image. The specific molecular changes were: prelesion 1 & 2 mI/NAA: 30.3%, P < 0.001; prelesion 1 mI/NAA: 31.5%, P < 0.001; prelesion 2 mI/NAA: 26.2%, P < 0.001; prelesion 1 & 2 Lac: 120.9%, P < 0.001; prelesion 1 Lac: 73.7%; P < 0.001; prelesion 2 Lac: 200.0%, P < 0.001). Unpaired two-tailed Student’s t-tests were performed. Abbreviations: T2w, T2-weighted; NAWM, normal-appearing white matter; NAA, N-acetyl aspartate; mI, myo-inositol; Lac, lactate.

Extended Data Fig. 9 Quantitative analysis of metabolic imaging for characterizing metabolic profiles in T1w-dark lesion, T1w-hypointense lesion and T1w-isointense lesion of MS.

a, T1w and T2w images along with the identified ROIs, including T1w-dark lesion (purple), T1w-hypointense lesion (green) and T1w-isointense lesion (yellow). b, Regional average (solid line) and standard deviation (shaded area) of FID spectra in these regions. c, Box plots of mI/NAA and Lac levels in these regions. Lac levels were shown in arbitrary units. Box plots are represented by median and interquartile range, with ±1.5 interquartile range as whiskers. A Clear trend of increasing mI/NAA was found from T1w-isointense, T1w-hypointense to T1w-dark lesions (38.3%, n = 292, 51.1%, n = 312, and 73.0%, n = 137, over NAWM, n = 502, P < 0.001). Elevated Lac was found in all types of lesions (T1w-isointense 27.2%, T1w-hypointense 20.9%, and T1w-dark 29.5%, P < 0.001) compared with NAWM. Unpaired two-tailed Student’s t-tests were performed. Abbreviations: T1w, T1-weighted; T2w, T2-weighted; ROI, region of interest; NAWM, normal-appearing white matter; NAA, N-acetyl aspartate; mI, myo-inositol; Lac, lactate.

Extended Data Fig. 10 Quantitative analysis of metabolic imaging for differentiating between active and chronic MS lesions.

a, T2w and T1C images along with the identified ROIs, including active lesions (yellow) and chronic lesions (green). b, Regional average (solid line) and standard deviation (shaded area) of FID spectra in active and chronic lesions. c, Box plots of mI/NAA and Lac levels in active and chronic lesions. Lac levels were shown in arbitrary units. Box plots are represented by median and interquartile range, with ±1.5 interquartile range as whiskers. Significantly increased Lac (237.0%, P < 0.001) as well as reduced mI/NAA (27.9%, P < 0.001) are found in active lesions (n = 228) as compared with chronic lesions (n = 488). Compared with NAWM (n = 1103), the chronic lesion showed significantly increased mI/NAA (37.9%, P < 0.001) and both lesion types showed significantly increased Lac (chronic: 35.8%, P < 0.001; active: 357.8%, P < 0.001). Unpaired two-tailed Student’s t-tests were performed. Abbreviations: T2w, T2-weighted; T1C, T1-weighted contrast-enhanced; ROI, region of interest; NAWM, normal-appearing white matter; NAA, N-acetyl aspartate; mI, myo-inositol; Lac, lactate.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Notes 1–7 and Tables 1–8.

Reporting Summary

Source data

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Y., Li, Y., Jin, W. et al. Ultrafast J-resolved magnetic resonance spectroscopic imaging for high-resolution metabolic brain imaging. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01418-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41551-025-01418-4

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing