562-03-006 ISMRM Abstract

Assessment of deep learning–based image reconstruction in orbital MRI

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Neuro - Neuroinflammation

562-03-006 · Machine Learning in Neuroimaging · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row C

Keywords: Image Reconstruction Deep learning reconstruction Denoising Optic nerves Artificial Intelligence

Accepted

Aurore Sajust de Bergues de Escalup¹, Augustin Lecler¹, Émilie Poirion¹, Caroline Papeix², Romain Deschamps², Dan Milea³, Julien Savatovsky ¹, Loïc Duron¹, Emma O’Shaughnessy¹

¹Neuroradiology, Fondation Rothschild, Paris, France

²Neurology, Fondation Rothschild, Paris, France

³Neuro-ophthalmology, Fondation Rothschild, Paris, France

Presenting Author: Julien Savatovsky

Synopsis

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References

1. Estler A, Zerweck L, Brunnée M, Estler B, Richter V, Örgel A, et al. Deep learning-accelerated image reconstruction in MRI of the orbit to shorten acquisition time and enhance image quality. J Neuroimaging 2024;34:232–240. https://doi.org/10.1111/jon.13187. [doi]

2. Tajima T, Akai H, Yasaka K, Kunimatsu A, Yamashita Y, Akahane M, et al. Usefulness of deep learning-based noise reduction for 1.5 T MRI brain images. Clin Radiol 2023;78:e13–e21. https://doi.org/10.1016/j.crad.2022.08.127. [doi]

3. Hokamura M, Uetani H, Nakaura T, Matsuo K, Morita K, Nagayama Y, et al. Exploring the impact of super-resolution deep learning on MR angiography image quality. Neuroradiology 2024;66:217–226. https://doi.org/10.1007/s00234-023-03271-1. [doi]

4. Oshima S, Fushimi Y, Miyake KK, Nakajima S, Sakata A, Okuchi S, et al. Denoising approach with deep learning-based reconstruction for neuromelanin-sensitive MRI: image quality and diagnostic performance. Jpn J Radiol 2023;41:1216–1225. https://doi.org/10.1007/s11604-023-01452-9. [doi]

5. Tanabe M, Kawano Y, Inoue A, Miyoshi K, Furutani H, Ihara K, et al. Image quality in three-dimensional (3D) contrast-enhanced dynamic magnetic resonance imaging of the abdomen using deep learning denoising technique: intraindividual comparison between T1-weighted sequences with compressed sensing and with a modified fast 3D mode wheel. Jpn J Radiol 2025;43:455–462. https://doi.org/10.1007/s11604-024-01687-0. [doi]

6. Naganawa S, Nakamichi R, Ichikawa K, Kawamura M, Kawai H, Yoshida T, et al. MR imaging of endolymphatic hydrops: utility of iHYDROPS-Mi2 combined with deep learning reconstruction denoising. Magn Reson Med Sci 2021;20:272–279. https://doi.org/10.2463/mrms.mp.2020-0082. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/562-03-006