Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
352-02-013 / 352-02-013 ISMRM Abstract

An Open Innovation Emitter–Modulator–Injector Framework for Inline MRI Reconstruction (PRIME)

Primary: Acquisition & Reconstruction - Open source software, sequences, and reconstruction algorithms
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
352-02-013 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
352-02-013 · Segmentation: Automated, Scalable, and Clinically Oriented MRI Analysis · Monday, 11 May, 1:50 PM–3:26 PM · Power Pitch Theatre 2
Keywords: AI/ML Image Reconstruction Image Reconstruction Machine Learning/Artificial Intelligence Open MRI Open-Source
Accepted
Omer B Demirel 1, Spencer Waddle1, Sandeep Ganji1, Melvyn B Ooi1, Ryan Robison1
1Philips North America Clinical Science, Rochester, United States of America
Presenting Author: Omer B Demirel

Synopsis

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References

1. Veldmann, M. et al. OPEN‐SOURCE MR imaging and reconstruction workflow. Magn. Reson. Med. 88, 2395–2407 (2022). DOI: 10.1002/mrm.29384 [doi]
2. Uecker, M. et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magn. Reson. Med. 71, 990–1001 (2014). DOI: 10.1002/mrm.24751 [doi]
3. Pruessmann, K. P., Weiger, M., Börnert, P. & Boesiger, P. Advances in sensitivity encoding with arbitrary k-space trajectories. Magn. Reson. Med. 46, 638–651 (2001). DOI: https://doi.org/10.1002/mrm.1241 [doi]
4. Schlemper, J. et al. Attention gated networks: Learning to leverage salient regions in medical images. Med. Image Anal. 53, 197–207 (2019). DOI: https://doi.org/10.1016/j.media.2019.01.012 [doi]
5. Bernard, O. et al. Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? IEEE Trans. Med. Imaging 37, 2514–2525 (2018). DOI: 10.1109/TMI.2018.2837502 [doi]
6. Campello, V. M. et al. Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge. IEEE Trans. Med. Imaging 40, 3543–3554 (2021). DOI: 10.1109/TMI.2021.3090082 [doi]
7. He, K., Zhang, X., Ren, S. & Sun, J. Deep Residual Learning for Image Recognition. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, Las Vegas, NV, USA, 2016). doi:10.1109/CVPR.2016.90. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/352-02-013