Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
461-01-005 ISMRM Abstract

Automated Cardiac Inversion Time Prediction with Confidence Feedback for LGE Imaging

Primary: Cardiovascular - Myocardium
Secondary: Analysis Methods - Classification and Prediction
461-01-005 · Cardiac Tissue Characterization · Tuesday, 12 May, 8:20 AM–9:15 AM · Digital Posters Row B
Keywords: Machine Learning/Artificial Intelligence Explainable AI Late gadolinium enhancement Automatic reporting Inversion time
Accepted
Sai Gannavarapu1, SUDHANYA Chatterjee1, Subhashis Banerjee1, 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. 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.
2. 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). https://doi.org/10.1007/s10334-023-01101-2 [doi]
3. Ohta, Y., Tateishi, E., Morita, Y. et al. Optimization of null point in Look-Locker images for myocardial late gadolinium enhancement imaging using deep learning and a smartphone. Eur Radiol 33, 4688–4697 (2023). https://doi.org/10.1007/s00330-023-09465-8 [doi]
4. Xie, C., Zhang, R., Mensink, S. et al. Automated inversion time selection for late gadolinium–enhanced cardiac magnetic resonance imaging. Eur Radiol 34, 5816–5828 (2024). https://doi.org/10.1007/s00330-024-10630-w [doi]
5. Seung Su Yoon, Michaela Schmidt, Manuela Rick et.al. , “Validation of a Deep Learning based Automated Myocardial Inversion Time Selection for Late Gadolinium Enhancement Imaging in a Prospective Study”, Proceedings of ISMRM 2021, p.0452
6. Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu "Image Super-Resolution Using Very Deep Residual Channel Attention Networks" by (2018)

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-01-005