Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
569-03-003 ISMRM Abstract

Deep Learning-based Super-Resolution for BLADE MRI using Simulated and Native Training Datasets

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Analysis Methods - Image Enhancement
569-03-003 · Super-Resolving MRI: Methods and Applications · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J
Keywords: Super-resolution Artifacts correction Deep-learning-based image reconstruction Image enhacement BLADE
Accepted
Keerthi Prabhu M 1, Punith Bidarakka Venkategowda1,2, Asha KumaraSwamy Kuppe1, Kun Zhou3, Thomas Benkert4, Seung Su Yoon5, Marcel Dominik Nickel4
1Magnetic Resonance, Siemens Healthineers, Bangalore, India
2International Institute of Information Technology Bangalore, Bangalore, India
3Magnetic resonance, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
4Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
5Research & Development, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
Presenting Author: Keerthi Prabhu M

Synopsis

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References

1. Altmann, Sebastian, et al. "Deep Learning Accelerated Brain Diffusion-Weighted MRI with Super Resolution Processing." Academic Radiology (2024).
2. Kim, Dong Kyun, et al. "Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI." Magnetic Resonance Imaging 105 (2024): 82-91.
3. Herrmann, Judith, et al. "Diagnostic confidence and feasibility of a deep learning accelerated HASTE sequence of the abdomen in a single breath-hold." Investigative radiology 56.5 (2021): 313-319.
4. Gassenmaier, Sebastian, et al. "Deep learning applications in magnetic resonance imaging: has the future become present?." Diagnostics 11.12 (2021): 2181.
5. Chaika, Maryanna, et al. "Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time." Diagnostic and interventional imaging 104.2 (2023): 53-59.
6. Almansour, Haidara, et al. "Combined deep learning-based super-resolution and partial fourier reconstruction for gradient echo sequences in abdominal MRI at 3 Tesla: shortening breath-hold time and improving image sharpness and lesion conspicuity." Academic radiology 30.5 (2023): 863-872.
7. Almansour, Haidara, et al. "Deep learning-based superresolution reconstruction for upper abdominal magnetic resonance imaging: an analysis of image quality, diagnostic confidence, and lesion conspicuity." Investigative radiology 56.8 (2021): 509-516.
8. B Venkategowda, Punith, et al. “Combined Super Resolution and Partial Fourier Reconstruction for 3D Magnetic Resonance Imaging with varying Partial Fourier settings.” Submitted in parallel to the Annual Meeting of ISMRM 2025. 2025.
9. Li, Bryan M., et al. "Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols." Frontiers in Computational Neuroscience 16 (2022): 887633.
10. Zhou, Zhiyi, et al. "Super‐resolution of brain tumor MRI images based on deep learning." Journal of Applied Clinical Medical Physics 23.11 (2022): e13758.
11. Shi, Wenzhe, et al. "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
12. Kingma, Diederik P., and Jimmy Ba. "Adam: A method for stochastic optimization." Proceedings of the 3rd International Conference on Learning Representations (ICLR). 2015.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-03-003