Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
365-02-004 ISMRM Abstract

One-Shot Multi-Tissue Inversion Time Prediction for Cardiac MRI

Primary: Cardiovascular - Myocardium
Secondary: Analysis Methods - Classification and Prediction
365-02-004 · Post-Processing of Cardiovascular MRI · Monday, 11 May, 9:15 AM–10:10 AM · Digital Posters Row F
Keywords: Machine Learning/Artificial Intelligence Late gadolinium enhancement Cardiac Inversion time TI-Scout
Accepted
Sai Gannavarapu1, SUDHANYA Chatterjee1, Gaspar Delso2,3, Sajith Rajamani1, Justin Leonard4,5, Uday Patil1, Martin A Janich6, Dattesh Dayanand Shanbhag 1
1GE HealthCare, Bengaluru, India
2GE HealthCare, Madrid, Spain
3GE HealthCare (ES), Spain
4GE Healthcare (UK), United Kingdom
5GE Healthcare, Little Chalfont, United Kingdom
6GE HealthCare (DE), Germany
Presenting Author: Dattesh Dayanand Shanbhag

Synopsis

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References

1. Maillot, A., Sridi, S., Pineau, X. et al. Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice. Magn Reson Mater Phy 36, 877–885 (2023).
2. Bahrami N, Retson T, Blansit K, Wang K, Hsiao A. Automated selection of myocardial inversion time with a convolutional neural network: Spatial temporal ensemble myocardium inversion network (STEMI-NET). Magn Reson Med. 2019; 81: 3283–3291.
3. Jens Wetzl, Seung Su Yoon, Michaela Schmidt, et.al, “AI-based Single-Click Cardiac MRI Exam: Initial Clinical Experience and Evaluation in 44 Patients”, Proceedings of ISMRM 2023, p.1325
4. S. Banerjee, Chatterjee, et.al, Inter-Frame distance metric-based Auto-Inversion-Time prediction for Cardiac MR, ISMRM 2025
5. Paul A. Yushkevich, Joseph Piven, Heather Cody Hazlett, Rachel Gimpel Smith, Sean Ho, James C. Gee, and Guido Gerig. User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage 2006 Jul 1;31(3):1116-28.
6. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/365-02-004