507-04-002 ISMRM Abstract

Evaluating Direct Plane Prediction Model and conventional Landmark-based Approach for Robust Multi-View Cardiac MRI Planning

Primary: Acquisition & Reconstruction - AI methods

Secondary: Analysis Methods - Segmentation and Detection

507-04-002 · Advanced Segmentation in MRI Across Scales and Domains · Wednesday, 13 May, 4:00 PM–5:50 PM · Meeting Room 1.40

Keywords: Cardiovascular magnetic resonance Deep Learning Segmentation Democratizing mri Automated plane prescription Cardiac views

Accepted

Viswanath Pamulakanty Sudarshan ¹, Vineeth VS¹, Rajat Kumar¹, Suranjita Ganguly¹, Jaladhar Neelavalli², Suthambhara Nagaraj¹, Yogesh k Mariappan¹

¹Philips Healthcare, Bengaluru, India

²Biomedical Engineering, Indian Institute of Technology, Hyderabad, India

Presenting Author: Viswanath Pamulakanty Sudarshan

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References

1. Blansit K, Retson T, Masutani E, Bahrami N, Hsiao A. Deep Learning–based Prescription of Cardiac MRI Planes. Radiology: Artificial Intelligence. 2019;1(6):e180069. doi:10.1148/ryai.2019180069 [doi]

2. Wei D, Huang Y, Lu D, Li Y, Zheng Y. Automatic view plane prescription for cardiac magnetic resonance imaging via supervision by spatial relationship between views. Medical Physics. 2023. DOI: https://doi.org/10.1002/mp.16743 [doi]

3. Sharma S, Pamulakanty Sudarshan V, Hegde A, Ali Mattathodi RA, Vineeth VS, Prasad VN, Saraswathy S, Neelavalli J. Instance Segmentation Based Approach for Robust Automatic 3D Multi-View Planning for Cardiac MRI. ISMRM 2024 Annual Meeting & Exhibition, Abstract #1667.

4. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer, 2015:234–241. DOI: https://doi.org/10.1007/978-3-319-24574-4_28 [doi]

5. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation. Nature Methods. 2021;18:203–211. DOI: https://doi.org/10.1038/s41592-020-01008-z [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/507-04-002