Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
469-03-003 ISMRM Abstract

In Vivo Fatty Acid Composition of Adipose Tissue Using MR Spectroscopy and Deep Neural Networks

Primary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging
Secondary: Body - Body Composition
469-03-003 · Software and Analysis for MR Spectroscopy and Chemical Shift Imaging · Tuesday, 12 May, 1:40 PM–2:35 PM · Digital Posters Row J
Keywords: Spectroscopy Adipose tissue Neural Network Fatty acid composition Oils
Accepted
Aftar Ahmad Sami1, Nakul Gupta2, Aaryani Tipirneni-Sajja 1,3
1Biomedical Engineering, University of Houston, Houston, United States of America
2Department of Radiology, Houston Methodist Hospital, Houston, United States of America
3Biomedical Sciences, University of Houston, Houston, United States of America
Presenting Author: Aaryani Tipirneni-Sajja

Synopsis

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References

1. Hamilton, G., Schlein, A. N., Middleton, M. S., Hooker, C. A., Wolfson, T., Gamst, A. C., Loomba, R., & Sirlin, C. B. (2017). In vivo triglyceride composition of abdominal adipose tissue measured by 1 H MRS at 3T. Journal of magnetic resonance imaging: JMRI, 45(5), 1455–1463. https://doi.org/10.1002/jmri.25453 [doi]
2. Machann, J., Stefan, N., Wagner, R., Bongers, M., Schleicher, E., Fritsche, A., Häring, H. U., Nikolaou, K., & Schick, F. (2017). Intra- and interindividual variability of fatty acid unsaturation in six different human adipose tissue compartments assessed by 1 H-MRS in vivo at 3 T. NMR in biomedicine, 30(9), 10.1002/nbm.3744. https://doi.org/10.1002/nbm.3744 [doi]
3. Peterson, P., Trinh, L., & Månsson, S. (2021). Quantitative 1 H MRI and MRS of fatty acid composition. Magnetic resonance in medicine, 85(1), 49–67. https://doi.org/10.1002/mrm.28471 [doi]
4. Johnson, H., Puppa, M., van der Merwe, M., & Tipirneni-Sajja, A. (2023). Rapid and automated lipid profiling by nuclear magnetic resonance spectroscopy using neural networks. NMR in biomedicine, 36(11), e5010. https://doi.org/10.1002/nbm.5010 [doi]
5. Johnson, H., & Tipirneni-Sajja, A. (2024). Explainable AI to Facilitate Understanding of Neural Network-Based Metabolite Profiling Using NMR Spectroscopy. Metabolites, 14(6), 332. https://doi.org/10.3390/metabo14060332 [doi]
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7. Stefan, D., Di Cesare, F., Andrasescu, A., Popa, E., Lazariev, A., Vescovo, E., Strbak, O., Williams, S., Starcuk, Z., Cabanas, M., van Ormondt, D., & Graveron-Demilly, D. (2009). Quantitation of magnetic resonance spectroscopy signals: The jMRUI software package. Measurement Science and Technology, 20(10), 104035. https://doi.org/10.1088/0957-0233/20/10/104035 [doi]
8. Zhang, Y., & Shen, J. (2023). Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting. Magnetic resonance in medicine, 90(4), 1282–1296. https://doi.org/10.1002/mrm.29711 [doi]
9. Ali, A. H., Koutsari, C., Mundi, M., Stegall, M. D., Heimbach, J. K., Taler, S. J., Nygren, J., Thorell, A., Bogachus, L. D., Turcotte, L. P., Bernlohr, D., & Jensen, M. D. (2011). Free fatty acid storage in human visceral and subcutaneous adipose tissue: role of adipocyte proteins. Diabetes, 60(9), 2300–2307. https://doi.org/10.2337/db11-0219 [doi]

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http://echo.ismrm.org/p/ISMRM2026/469-03-003