Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
464-01-007 ISMRM Abstract

Deep Learning Super-Resolution Improves Image Quality and Sharpness in Diffusion Tensor Imaging of the Brachial Plexus

Primary: Diffusion - DWI/DTI/DKI
Secondary: Neuro - Spinal Cord
464-01-007 · Diffusion MRI Analysis · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row E
Keywords: Diffusion Tensor Imaging Super-resolution
Accepted
Takayuki Sada 1, Hajime Yokota2, Kurosawa Ryuna1, Keisuke Nitta1, Issei Nakanishi1,3, Hirotaka Sato1, Koji Matsumoto1, Takashi Namiki4, Masami Yoneyama4, Johannes M Peeters5,6, Takashi Iimori1, Takashi Uno2
1Chiba University hospital, Chiba, Japan
2Department of Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba, Japan
3Graduate School of Medicine, Chiba, Japan
4Philips Japan, Tokyo, Japan
5Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
6MR Clinical Science, Philips Healthcare, Best, Netherlands
Presenting Author: Takayuki Sada

Synopsis

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References

1. Ryckie G, Alexander W, Irvin T, et al. Diffusion tensor imaging of the roots of the brachial plexus: a systematic review and meta-analysis of normative values. Clin Transl Imaging. 2020;8(6):419-431.
2. Sada T, et al. Feasibility of diffusion tensor imaging of the brachial plexus using iZoom. In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM); 2023. Abstract 1707.
3. Wu Z, et al. iZoom with 2nd order flow compensated diffusion for improving cardiac diffusion imaging: a preliminary study. In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM); 2022. Abstract 4137.
4. Dong C, Loy CC, He K, Tang X. Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016;38(2):295-307. doi:10.1109/TPAMI.2015.2439281 [doi]
5. Li Y, Sixou B, Peyrin F. A Review of the Deep Learning Methods for Medical Images Super Resolution Problems. IRBM. 2021;42(2):120-133. doi:10.1016/j.irbm.2020.06.003 [doi]
6. Kim J, et al. Deep learning-based Reconstruction with Super Resolution for Abdominal Diffusion Weighted Imaging. In: Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM); 2024. Abstract 3626.
7. Fan M, Liu Z, Xu M, et al. Generative adversarial network-based super-resolution of diffusion-weighted imaging: Application to tumour radiomics in breast cancer. NMR Biomed. 2020;33(8):e4345. doi:10.1002/nbm.4345 [doi]
8. Bischoff LM, Peeters JM, Weinhold L, et al. Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI. Radiology. 2023;308(3):e230427. doi:10.1148/radiol.230427 [doi]
9. Leclaire A, Moisan L. No-Reference Image Quality Assessment and Blind Deblurring with Sharpness Metrics Exploiting Fourier Phase Information. J Math Imaging Vis. 2015;52(1):145-172. doi:10.1007/s10851-015-0560-5 [doi]
10. Alexander AL, Hasan KM, Tsuruda JS, et al. Analysis of partial volume effects in diffusion-tensor MRI. Magn Reson Med. 2001;45(5):770-780. doi:10.1002/mrm.1110 [doi]

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http://echo.ismrm.org/p/ISMRM2026/464-01-007