Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-017 / 452-03-017 ISMRM Abstract

Continuous Noise-Adaptive Denoising (CoNAD) using a Noise-Conditioned Adversarial Network

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Cardiovascular - Myocardium
452-03-017 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-017 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Deep learning reconstruction Denoising Generative adversarial network Adaptive denoising
Accepted
Omer B Demirel 1, Spencer Waddle1, Dinghui Wang2, Tzu Cheng Chao3, Jacinta Browne3, Tim Leiner2,3
1Philips North America Clinical Science, Rochester, United States of America
2Mayo Clinic, Rochester, United States of America
3Radiology, Mayo Clinic, Rochester, United States of America
Presenting Author: Omer B Demirel

Synopsis

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References

1. Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising. IEEE Trans. Image Process. 26, 3142–3155 (2017). DOI: 10.1109/TIP.2017.2662206 [doi]
2. Zormpas-Petridis, K. et al. Accelerating Whole-Body Diffusion-weighted MRI with Deep Learning–based Denoising Image Filters. Radiol. Artif. Intell. 3, e200279 (2021). DOI: 10.1148/ryai.2021200279 [doi]
3. Zhao, M., Wei, Y. & Wong, K. K. L. A Generative Adversarial Network technique for high-quality super-resolution reconstruction of cardiac magnetic resonance images. Magn. Reson. Imaging 85, 153–160 (2022). DOI: 10.1016/j.mri.2021.10.033 [doi]
4. Perez, E., Strub, F., De Vries, H., Dumoulin, V. & Courville, A. FiLM: Visual Reasoning with a General Conditioning Layer. Proc. AAAI Conf. Artif. Intell. 32, (2018). DOI: 10.1609/aaai.v32i1.11671 [doi]
5. Zhang, R., Isola, P., Efros, A. A., Shechtman, E. & Wang, O. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 586–595 (IEEE, Salt Lake City, UT, 2018). doi:10.1109/CVPR.2018.00068. DOI: 10.1109/CVPR.2018.00068 [doi]
6. Wang, X. et al. ESRGAN: enhanced super-resolution generative adversarial networks. in vol. 11133 63–79 (Munich, Germany, 2018). DOI: 10.1007/978-3-030-11021-5_5 [doi]
7. Danielyan, A., Katkovnik, V. & Egiazarian, K. BM3D Frames and Variational Image Deblurring. IEEE Trans. Image Process. 21, 1715–1728 (2012). DOI: 10.1109/TIP.2011.2176954 [doi]
8. Lempitsky, V., Vedaldi, A. & Ulyanov, D. Deep Image Prior. in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 9446–9454 (IEEE, Salt Lake City, UT, 2018). doi:10.1109/CVPR.2018.00984. DOI: 10.1109/CVPR.2018.00984 [doi]

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http://echo.ismrm.org/p/ISMRM2026/452-03-017