Abstract
The lack of real-time techniques for monitoring respiratory motion impairs the development of guidance systems for image-guided interventions. Recent works show that U-Net based real-time 4D MRI prediction methods are promising, but prone to bad image quality when small training data sets and inputs with multiple MR contrast are used. To overcome this problem, we propose a more efficient use of the spare training data and re-utilize 2D training samples as a secondary input for construction of transitive information bridges between the navigator slice primary input and the data slice prediction. We thus remove the need for a separate 3D breath-hold MRI with different MR contrast as the secondary input. Results show that our novel construction leads to improved prediction quality with very sparse training data, with a significant decrease in root mean squared error (RMSE) from 0.3 to 0.27 (p\(<2.2e^{-16}\), d=0.19). Additionally, removing 3D imaging reduces prior acquisition time from 3 to 2 min.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cai, J., Chang, Z., Wang, Z., Paul Segars, W., Yin, F.F.: Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study. Med. Phys. 38(12), 6384–6394 (2011)
Gulamhussene, G., Joeres, F., Rak, M., Lübeck, C., Pech, M., Hansen, C.: 2D MRI liver slices with navigator frames. a test data set for image based 4D MRI reconstruction (2019). https://doi.org/10.24352/UB.OVGU-2019-093
Gulamhussene, G., Joeres, F., Rak, M., Pech, M., Hansen, C.: 4D MRI: robust sorting of free breathing MRI slices for use in interventional settings. PLoS ONE 15(6), e0235175 (2020)
Gulamhussene, G., et al.: Predicting 4D liver MRI for MR-guided interventions. Comput. Med. Imaging Graph., 102122 (2022). https://doi.org/10.1016/j.compmedimag.2022.102122, https://www.sciencedirect.com/science/article/pii/S0895611122000921
Gulamhussene, G., et al.: 2D MRI liver slices with navigator frames. a test data set for image based 4D MRI reconstruction. Part II (2021). https://doi.org/10.24352/UB.OVGU-2021-071
Gulamhussene, G., et al.: Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction (2022)
Han, F., Zhou, Z., Cao, M., Yang, Y., Sheng, K., Hu, P.: Respiratory motion-resolved, self-gated 4D-MRI using rotating cartesian k-space (ROCK). Med. Phys. 44(4), 1359–1368 (2017)
Hu, Y., Caruthers, S.D., Low, D.A., Parikh, P.J., Mutic, S.: Respiratory amplitude guided 4-dimensional magnetic resonance imaging. Int. J. Radiat. Oncol.* Biol.* Phys. 86(1), 198–204 (2013)
van de Lindt, T., et al.: A self-sorting coronal 4D-MRI method for daily image guidance of liver lesions on an MR-LINAC. Int. J. Radiat. Oncol.* Biol.* Phys. 102(4), 875–884 (2018)
Paganelli, C., Summers, P., Bellomi, M., Baroni, G., Riboldi, M.: Liver 4DMRI: a retrospective image-based sorting method. Med. Phys. 42(8), 4814–4821 (2015)
von Siebenthal, M., Szekely, G., Gamper, U., Boesiger, P., Lomax, A., Cattin, P.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52(6), 1547 (2007)
Tanner, C., Samei, G., Székely, G.: Improved reconstruction of 4D-MR images by motion predictions. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14-18, 2014, Proceedings, Part I, pp. 146–153. Springer International Publishing, Cham (2014). https://doi.org/10.1007/978-3-319-10404-1_19
Tryggestad, E., et al.: Respiration-based sorting of dynamic MRI to derive representative 4D-MRI for radiotherapy planning. Med. Phys. 40(5), 051909 (2013)
Yang, Z., Ren, L., Yin, F.F., Liang, X., Cai, J.: Motion robust 4D-MRI sorting based on anatomic feature matching: a digital phantom simulation study. Radiat. Med. Prot. 1(1), 41–47 (2020)
Yuan, J., Wong, O.L., Zhou, Y., Chueng, K.Y., Yu, S.K.: A fast volumetric 4D-MRI with sub-second frame rate for abdominal motion monitoring and characterization in MRI-guided radiotherapy. Quant. Imaging Med. Surg. 9(7), 1303 (2019)
Acknowledgements
The authors acknowledge the financial support from the Federal Ministry for Economics and Energy of Germany (project number 16KN093921). This work was carried out as part of the STIMULATE research campus.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gulamhussene, G., Bashkanov, O., Omari, J., Pech, M., Hansen, C., Rak, M. (2023). Using Training Samples as Transitive Information Bridges in Predicted 4D MRI. In: Xue, Z., et al. Medical Image Learning with Limited and Noisy Data. MILLanD 2023. Lecture Notes in Computer Science, vol 14307. Springer, Cham. https://doi.org/10.1007/978-3-031-44917-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-44917-8_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47196-4
Online ISBN: 978-3-031-44917-8
eBook Packages: Computer ScienceComputer Science (R0)