Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
470-08-137 ISMRM Abstract

Comparing Deep Learning Denoising Models for Preserving Diagnostic Value in Neuromelanin-Sensitive MRI of the Locus Coeruleus

Primary: Analysis Methods - Image Enhancement
Secondary: Neuro - Aging
470-08-137 · Analysis: Neuro · Tuesday, 12 May, 4:00 PM–4:55 PM · Traditional Posters | Exhibition Hall
Keywords: Neurodegeneration Scan time reduction Mild cognitive impairment Denoising AI-Accelerated MRI
Accepted
Phuong T Vu 1, James Lah2, Allan Levey2, Deqiang Qiu1,3
1Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, United States of America
2Department of Neurology, Emory University School of Medicine, Atlanta, United States of America
3Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, United States of America
Presenting Author: Phuong T Vu

Synopsis

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References

1. Samuels, E. R. & Szabadi, E. Functional Neuroanatomy of the Noradrenergic Locus Coeruleus: Its Roles in the Regulation of Arousal and Autonomic Function Part I: Principles of Functional Organisation. Curr. Neuropharmacol. 6, 235–253 (2008). https://doi.org/10.2174/157015908785777229 [doi]
2. Poe, G. R. et al. Locus coeruleus: a new look at the blue spot. Nat. Rev. Neurosci. 21, 644–659 (2020). https://doi.org/10.1038/s41583-020-0360-9 [doi]
3. Sasaki, M. et al. Neuromelanin magnetic resonance imaging of locus ceruleus and substantia nigra in Parkinson’s disease. NeuroReport 17, 1215–1218 (2006). https://doi.org/10.1097/01.wnr.0000227984.84927.a7 [doi]
4. Kelberman, M. et al. What’s That (Blue) Spot on my MRI? Multimodal Neuroimaging of the Locus Coeruleus in Neurodegenerative Disease. Front. Neurosci. 14, 583421 (2020). https://doi.org/10.3389/fnins.2020.583421 [doi]
5. Chen, X. et al. Simultaneous imaging of locus coeruleus and substantia nigra with a quantitative neuromelanin MRI approach. Magn. Reson. Imaging 32, 1301–1306 (2014). https://doi.org/10.1016/j.mri.2014.07.003 [doi]
6. Liu, K. Y. et al. Noradrenergic-dependent functions are associated with age-related locus coeruleus signal intensity differences. Nat. Commun. 11, 1712 (2020). https://doi.org/10.1038/s41467-020-15410-w [doi]
7. Betts, M. J. et al. Locus coeruleus MRI contrast is reduced in Alzheimer’s disease dementia and correlates with CSF Aβ levels. Alzheimers Dement. Diagn. Assess. Dis. Monit. 11, 281–285 (2019). https://doi.org/10.1016/j.dadm.2019.02.001 [doi]
8. Sulzer, D. et al. Neuromelanin detection by magnetic resonance imaging. NPJ Parkinson’s Disease 4, 11 (2018). https://doi.org/10.1038/s41531-018-0047-3 [doi]
9. Trujillo, P. et al. Neuromelanin-sensitive MRI as a promising biomarker of human catecholamine function: current status and future directions. Brain 146, 2359–2377 (2023). https://doi.org/10.1093/brain/awad251 [doi]
10. Oshima, S. et al. Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance. Jpn. J. Radiol. 41, 1216-1225 (2023). https://doi.org/10.1007/s11604-023-01452-9 [doi]
11. Heo, Y. C. et al. Image Denoising Using Non-Local Means (NLM) Approach in Magnetic Resonance (MR) Imaging: A Systematic Review. Appl. Sci. 10, 7028 (2020). https://doi.org/10.3390/app10207028 [doi]
12. Zhang, K. et al. Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017). https://doi.org/10.1109/TIP.2017.2662206 [doi]
13. Chen, H. et al. Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN). IEEE Trans. Med. Imaging (2017). 10.1109/TMI.2017.2715284 [doi]
14. Liang, J. et al. SwinIR: Image Restoration Using Swin Transformer. Proc. ICCV Workshops (2021). https://doi.org/10.1109/ICCVW54120.2021.00210 [doi]
15. Zamir, S. W. et al. Restormer: Efficient Transformer for High-Resolution Image Restoration. Proc. CVPR 2022, 5718–5729 (2022). https://doi.org/10.1109/CVPR52688.2022.00564 [doi]
16. Esser, P. et al. Taming Transformers for High-Resolution Image Synthesis. Proc. CVPR (2021). https://doi.org/10.1109/CVPR46437.2021.01268 [doi]
17. Jiang, H. et al. Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation. IEEE J. Biomed. Health Informatics (2025). https://doi.org/10.1109/JBHI.2025.3565183 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/470-08-137