Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning

Wang Y, Lombardo E, Wang J, Fan Y, Zhao Y, Corradini S, Belka C, Riboldi M, Kurz C, Landry G (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Physics and Imaging in Radiation Oncology Elsevier

Book Volume: 34

Article Number: 100789

DOI: 10.1016/j.phro.2025.100789

Abstract

Background and purpose: Deep learning-based tumor tracking is promising for real-time magnetic-resonance-imaging (MRI)-guided radiotherapy. We investigate the applicability of a tumor tracking model developed for 0.35 T MRI-linac sagittal cine-MRI for 1.5 T interleaved orthogonal cine-MRI and implement transfer learning to further improve its performance. Materials and methods: We collected 3600 cine-MRI frames in sagittal, coronal and axial planes from 24 patients (validation 10, testing 14) treated on a 1.5 T MRI-linac, where two expert clinicians manually segmented target labels. A transformer-based deformation model trained on 0.35T MRI-linac images (baseline model, BL) was evaluated and used as a starting point to train patient-specific (PS) models. The Dice similarity coefficient (DSC) and the surface distance (50th and 95th percentiles, SD50%, SD95%) were used to compare the obtained target segmentations with the ground truth labels. The percentage of negative Jacobian determinant values (NegJ), accounting for the folding pixel ratio, was determined. Results: Outperformed by all the PS models, the BL model averaged in a DSC of 0.85, SD50% of 1.9 mm, SD95% of 5.9 mm and NegJ of 0.45 % in testing. The best PS model averaged in a DSC of 0.90, SD50% of 1.3 mm, SD95% of 3.9 mm and NegJ of 0.02 % in testing. Conclusion: We have found the 0.35 T model trained on sagittal cine-MRIs cannot be directly applied to a 1.5 T interleaved orthogonal cine-MRI system. However, PS transfer learning could improve the target tracking performance and reach an accuracy comparable to the inter-observer variability.

Involved external institutions

University of Electronic Science and Technology of China (UESTC) / 电子科技大学 CN China (CN) Klinikum der Universität München (LMU Klinikum) DE Germany (DE) Ludwig-Maximilians-Universität (LMU) DE Germany (DE)

How to cite

APA:

Wang, Y., Lombardo, E., Wang, J., Fan, Y., Zhao, Y., Corradini, S.,... Landry, G. (2025). Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning. Physics and Imaging in Radiation Oncology, 34. https://doi.org/10.1016/j.phro.2025.100789

MLA:

Wang, Yiling, et al. "Real-time target localization on 1.5 T magnetic resonance imaging linac orthogonal cine images using transfer learning." Physics and Imaging in Radiation Oncology 34 (2025).

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