364-04-014 ISMRM Abstract

Improving image quality and spatial resolution in speech MRI using synthetically supervised deep learning reconstruction

Primary: Acquisition & Reconstruction - Image Reconstruction: AI

Secondary: Analysis Methods - Image Enhancement

364-04-014 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E

Keywords: AI/ML Image Reconstruction Synthetic data Vocal tract U-Net

Accepted

Mario K Nicola ^1,2, Agnieszka Peplinski², Joe Martin², Marc E Miquel^1,3

¹Faculty of Life Sciences & Medicine, King's College, London, United Kingdom

²Clinical Physics, Barts Health NHS Trust, London, United Kingdom

³Medical Physics and Clinical Engineering, Guy's and St. Thomas' NHS Foundation Trust, London, United Kingdom

Presenting Author: Mario K Nicola

Synopsis

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References

1. Lingala SG, Sutton BP, Miquel ME, Nayak KS. Recommendations for real-time speech MRI. J Magn Reson Imaging. 2016 Jan;43(1):28-44. doi: 10.1002/jmri.24997. Epub 2015 Jul 14. PMID: 26174802; PMCID: PMC5079859. [doi] [pmid]

2. Lingala SG, Zhu Y, Kim YC, Toutios A, Narayanan S, Nayak KS. A fast and flexible MRI system for the study of dynamic vocal tract shaping. Magn Reson Med. 2017 Jan;77(1):112-125. doi: 10.1002/mrm.26090. Epub 2016 Jan 17. PMID: 26778178; PMCID: PMC4947574. [doi] [pmid]

3. Dong C, Loy CC, He K, Tang X. Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016 Feb;38(2):295-307. doi: 10.1109/TPAMI.2015.2439281. PMID: 26761735. [doi] [pmid]

4. Ronneberger, O., Fischer, P., Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9351. Springer, Cham. 2015 https://doi.org/10.1007/978-3-319-24574-4_28 [doi]

5. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z. and Shi, W., Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690). 2017. https://doi.org/10.48550/arXiv.1609.04802 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-014