Abstract
Thanks to recent advancements of specific acquisition methods and post-processing, proton Magnetic Resonance Imaging became an alternative imaging modality for detecting and monitoring chronic pulmonary disorders. Currently, ventilation maps of the lung are calculated from time-resolved image series which are acquired under free breathing. Each series consists of 140 coronal 2D images containing several breathing cycles. To cover the majority of the lung, such a series is acquired at several coronal slice-positions. A reduction of the number of images per slice enable an increase in the number of slice-positions per patient and therefore a more detailed analysis of the lung function without adding more stress to the patient. In this paper, we present a new method in order to reduce the number of images for one coronal slice while preserving the quality of the ventilation maps. As the input is a time-dependent signal, we designed our model based on Gated Recurrent Units. The results show that our method is able to compute ventilation maps with a high quality using only 40 images. Furthermore, our method shows strong robustness regarding changes in the breathing cycles during the acquisition.
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References
Andermatt, S., Pezold, S., Cattin, P.: Multi-dimensional gated recurrent units for the segmentation of biomedical 3D-data. In: Carneiro, G., et al. (eds.) LABELS/DLMIA -2016. LNCS, vol. 10008, pp. 142–151. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46976-8_15
Bauman, G., Bieri, O.: Matrix pencil decomposition of time-resolved proton MRI for robust and improved assessment of pulmonary ventilation and perfusion. Magn. Reson. Med. 77(1), 336–342 (2017)
Bauman, G., Pusterla, O., Bieri, O.: Ultra-fast steady-state free precession pulse sequence for fourier decomposition pulmonary MRI. Magn. Reson. Med. 75(4), 1647–1653 (2016)
van Beek, E.J., Wild, J.M., Kauczor, H.U., Schreiber, W., Mugler III, J.P., de Lange, E.E.: Functional MRI of the lung using hyperpolarized 3-helium gas. J. Magn. Reson. Imaging 20(4), 540–554 (2004)
Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Sandkühler, R., Jud, C., Pezold, S., Cattin, P.C.: Adaptive graph diffusion regularisation for discontinuity preserving image registration. In: Klein, S., Staring, M., Durrleman, S., Sommer, S. (eds.) WBIR 2018. LNCS, vol. 10883, pp. 24–34. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-92258-4_3
Acknowledgement
The authors would like to thank the Swiss National Science Foundation for funding this project (SNF 320030_149576).
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Sandkühler, R. et al. (2019). Gated Recurrent Neural Networks for Accelerated Ventilation MRI. In: Suk, HI., Liu, M., Yan, P., Lian, C. (eds) Machine Learning in Medical Imaging. MLMI 2019. Lecture Notes in Computer Science(), vol 11861. Springer, Cham. https://doi.org/10.1007/978-3-030-32692-0_63
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DOI: https://doi.org/10.1007/978-3-030-32692-0_63
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