1136 ISMRM Abstract

gNET: gSlider Self-Supervised Neural Network for Accelerated Reconstruction of Super-resolution Diffusion MRI

1136 · Diffusion: Artificial Intelligence & Machine Learning · Thursday, 9 May, 8:15 AM–10:15 AM · Nicoll 1

Keywords: AI/ML Image Reconstruction Diffusion Reconstruction Deep-learning Diffusion/other diffusion imaging techniques self-supervised gSlider

Accepted

Caique de Oliveira Kobayashi ^1,2,3, Yohan Jun^1,4,5, Jaejin Cho^1,4,5, Xiaoqing Wang^4,5,6, Zihan Li⁷, Qiyuan Tian⁷, Berkin Bilgic^1,4,5

¹Athinoula A. Martinos Center for Biomedical Imaging, Cambridge, United States of America

²Mechanical Engineering, Escola Politécnica da USP, São Paulo, Brazil

³Technical University of Munich, Munich, Germany

⁴Radiology, Harvard Medical School, Boston, United States of America

⁵Harvard-MIT Division of Health Sciences and Technology, Cambridge, United States of America

⁶Boston Children's Hospital, Boston, United States of America

⁷Department of Biomedical Engineering, Tsinghua University, Beijing, China

Presenting Author: Caique de Oliveira Kobayashi

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References

1. JONES, D. K. Diffusion MRI. Oxford University Press, 2010.

2. PELED, S.; YESHURUN, Y. Superresolution in MRI: application to human white matter fiber tract visualization by diffusion tensor imaging. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, Wiley Online Library, v. 45, n. 1, p. 29–35, 2001.

3. GREENSPAN, H. et al. MRI inter-slice reconstruction using super-resolution. Magnetic resonance imaging, Elsevier, v. 20, n. 5, p. 437–446, 2002.

4. SETSOMPOP, K. et al. Slice dithered enhanced resolution simultaneous multislice (slider-SMS) for high resolution (700 μm) diffusion imaging of the human brain. In: 23rd Annual Meeting of International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, Canada, p. 339, 2015.

5. HALDAR, J. P.; FAN, Q.; SETSOMPOP, K. Whole-brain quantitative diffusion MRI at 660 μm resolution in 25 minutes using gSlider-SMS and SNR-enhancing joint reconstruction. In: Proceedings of the 24th Annual Meeting of ISMRM, Singapore, p. 102, 2016.

6. RAMOS-LLORDÉN, G. et al. High-fidelity, accelerated whole-brain submillimeter in vivo diffusion MRI using gSlider-spherical ridgelets (gSlider-SR). Magnetic resonance in medicine, Wiley Online Library, v. 84, n. 4, p. 1781–1795, 2020.

7. RAMOS-LLORDEN, G.; BILGIC, B.; HUANG, S. Y. Rapid, high-spatial resolution in vivo diffusion MRI with joint subsampling and reconstruction in k-, q-and RF-space. ISMRM, London, 2022.

8. RIEDMILLER, Martin; BRAUN, Heinrich. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In: IEEE international conference on neural networks. IEEE, p. 586-591, 1993.

9. SMITH, Stephen M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, v. 23, p. S208-S219, 2004.

10. ZIHAN, L. et al. Dimond: Diffusion model optimization with deep learning. Magnetic Resonance in Medicine, 2022.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2024/1136