461-02-004 ISMRM Abstract

Manual vs automated planning of cardiac MRI planes: A reproducibility study across different field strengths

Primary: Cardiovascular - Myocardium

Secondary: Acquisition & Reconstruction - AI methods

461-02-004 · AI To Make Protocols, Plan, QC, and Correct Motion · Tuesday, 12 May, 9:15 AM–10:10 AM · Digital Posters Row B

Keywords: Segmentation MRI Workflow optimzation Cardiovascular magnetic resonance Patient comfort Automated plane prescription

Accepted

Margarita Gorodezky¹, Gaspar Delso ², Karolin K Deyerberg³, Lena-Maria Watzke³, Ann-Christin Klemenz⁴, Mathias Manzke³, Antonia Dalmer³, Roberto Lorbeer⁵, Marc-André Weber⁴, Benjamin Böttcher³, Felix G Meinel³

¹GE Healthcare, Munich, Germany

²GE HealthCare (ES), Spain

³University Medical Centre Rostock, Rostock, Germany

⁴Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Centre Rostock, Rostock, Germany

⁵Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany

Presenting Author: Gaspar Delso

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References

1. Herzog, B. A., et al. "Cardiovascular magnetic resonance pocket guide." Eur Soc Cardiol (2017).

2. Blansit, Kevin, et al. "Deep learning–based prescription of cardiac MRI planes." Radiology: Artificial Intelligence 1.6 (2019): e180069. https://doi.org/10.1148/ryai.2019180069 [doi]

3. Orii, Makoto, et al. "Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population." The International Journal of Cardiovascular Imaging 39.5 (2023): 1001-1011. https://doi.org/10.1007/s10554-023-02793-2 [doi]

4. Klemenz, Ann-Christin, et al. "Pushing the limits of cardiac MRI: deep-learning based real-time cine imaging in free breathing vs breath hold." European Radiology (2025): 1-14. DOI:10.1007/s00330-025-11941-2. [doi]

5. Koo, Terry K., and Mae Y. Li. "A guideline of selecting and reporting intraclass correlation coefficients for reliability research." Journal of chiropractic medicine 15.2 (2016): 155-163. https://doi.org/10.1016/j.jcm.2016.02.012 [doi]

6. Shrout, Patrick E., and Joseph L. Fleiss. "Intraclass correlations: uses in assessing rater reliability." Psychological bulletin 86.2 (1979): 420. https://doi.org/10.1037/0033-2909.86.2.420 [doi]

7. Suinesiaputra, Avan, et al. "Quantification of LV function and mass by cardiovascular magnetic resonance: multi-center variability and consensus contours." Journal of cardiovascular magnetic resonance 17.1 (2015): 63. https://doi.org/10.1186/s12968-015-0170-9 [doi]

8. Raisi-Estabragh, Zahra, et al. "Cardiovascular magnetic resonance reference ranges from the Healthy Hearts Consortium." Cardiovascular Imaging 17.7 (2024): 746-762. https://doi.org/10.1016/j.jcmg.2024.01.009 [doi]

9. Böttcher, Benjamin, et al. "Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm." The international journal of cardiovascular imaging 36.11 (2020): 2239-2247. https://doi.org/10.1007/s10554-020-01935-0 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/461-02-004