Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
351-03-016 / 351-03-016 ISMRM Abstract

Automated Susceptibility-Informed Gold Seed Detection for MR-Guided Prostate Radiation Therapy using Routine mDixon Scans

Primary: Analysis Methods - Segmentation and Detection
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
351-03-016 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
351-03-016 · AI Methods · Monday, 11 May, 4:10 PM–5:46 PM · Power Pitch Theatre 1
Keywords: Metal Artifacts Deep Learning Segmentation Radiation therapy B0 susceptibility MR guided radiation therapy
Accepted
Siddhant Sahay1, Viswanath Pamulakanty Sudarshan 1, Gipson J Anto1, Suthambhara N1
1Philips Healthcare, Bengaluru, India
Presenting Author: Viswanath Pamulakanty Sudarshan

Synopsis

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References

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2. Bagheri, F., Faeghi, F., Baniasadipour, B., Bakhshandeh, M., Azghandi, S., & Hendudari, F. (2024). Optimizing MRI pulse sequence for displaying gold markers in radiation therapy simulation of patients with prostate cancer. Journal of Radiotherapy in Practice.
3. Deng, J., Liang, X., Iakovenko, V., Lu, W., Hannan, R., Desai, N., Park, Y. K., Lin, M.-H., Jiang, S., Meng, B., Wu, J., Yang, D., Garant, A., & Godley, A. (2025). Development and clinical implementation of an MRI-only planning workflow featuring deep learning-based synthetic CT for prostate cancer external beam radiotherapy. Journal of Applied Clinical Medical Physics.
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6. Wasserthal, J., Breit, H.-C., Meyer, M. T., Pradella, M., Hinck, D., Sauter, A. W., Heye, T., Boll, D. T., Cyriac, J., Yang, S., Bach, M., & Segeroth, M. (2023). TotalSegmentator: Robust segmentation of 104 anatomic structures in CT images. Radiology: Artificial Intelligence, 5(5), e230024. https://doi.org/10.1148/ryai.230024 [doi]
7. Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (pp. 234–241). Springer. https://doi.org/10.1007/978-3-319-24574-4_28 [doi]
8. Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18, 203–211. https://doi.org/10.1038/s41592-020-01008-z [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/351-03-016