Zusammenfassung
Image reconstruction in Magnetic Particle Imaging is mainly performed by using a system matrix or by mapping the time signal into spatial domain and deconvolving the tracer properties. In this work, a neural network is designed and trained for reconstructing 1D images. Test data are reconstructed using both the neural network and a conventional approach. Background artefacts that appear during conventional reconstruction are not visible when reconstructing with the neural network. The images that have been reconstructed using the neural network are superior in terms of quantifiability and spatial resolution in comparison to conventionally reconstructed images.
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Literatur
Gleich B, Weizenecker J. Tomographic imaging using the nonlinear response of magnetic particles. Nature. 2005;435(7046):1214–7.
Knopp T, Rahmer J, Sattel TF, Biederer S,Weizenecker J, Gleich B et al.Weighted iterative reconstruction for magnetic particle imaging. Phys Med Biol. 2010;55(6):1577–89.
Boberg M, Gdaniec N, Szwargulski P, Werner F, Möddel M, Knopp T. Simultaneous imaging of widely differing particle concentrations in MPI: problem statement and algorithmic proposal for improvement. Phys Med Biol. 2021;66(9):095004.
Goodwill PW, Conolly SM. Multidimensional X-Space magnetic particle imaging. IEEE Trans Med Imaging. 2011;30(9):1581–90.
Hatsuda T, Takagi T, Matsuhisa A, Arayama M, Tsuchiya H, Takahashi S et al. Basic study of image reconstruction method using neural networks with additional learning for magnetic particle imaging. Int J Magn Part Imaging. 2016;2(2).
Lecun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE. 1998;86(11):2278–324.
Koch P, Maass M, Bruhns M, Droigk C, Parbs TJ, Mertins A. Neural network for reconstruction of MPI images. International Workshop on Magnetic Particle Imaging. 2019:39– 40.
Dittmer S, Kluth T, Henriksen MTR, Maass P. Deep image prior for 3D magnetic particle imaging: a quantitative comparison of regularization techniques on Open MPI dataset. Int J Magn Part Imaging. 2021;7(1).
Chen X, Graeser M, Behrends A, von Gladiss A, Buzug TM. First measurement results of a 3D magnetic particle spectrometer. Int J Magn Part Imaging. 2018;4(1).
von Gladiss A, Graeser M, Cordes A, BakeneckerAC, Behrends A, ChenXet al. Investigating spatial resolution, field sequences and image reconstruction strategies using hybrid phantoms in MPI. Int J Magn Part Imaging. 2020;6(1).
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© 2022 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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von Gladiss, A., Memmesheimer, R., Theisen, N., Bakenecker, A.C., Buzug, T.M., Paulus, D. (2022). Reconstruction of 1D Images with a Neural Network for Magnetic Particle Imaging. In: Maier-Hein, K., Deserno, T.M., Handels, H., Maier, A., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2022. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-36932-3_52
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DOI: https://doi.org/10.1007/978-3-658-36932-3_52
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