Abstract
Magnetic particle imaging (MPI) is a recent modality that enables high contrast and frame-rate imaging of the magnetic nanoparticle (MNP) distribution. Based on a measured system matrix, MPI reconstruction can be cast as an inverse problem that is commonly solved via regularized iterative optimization. Yet, hand-crafted regularization terms can elicit suboptimal performance. Here, we propose a novel MPI reconstruction “PP-MPI” based on a deep plug-and-play (PP) prior embedded in a model-based iterative optimization. We propose to pre-train the PP prior based on a residual dense convolutional neural network (CNN) on an MPI-friendly dataset derived from magnetic resonance angiograms. The PP prior is then embedded into an alternating direction method of multiplier (ADMM) optimizer for reconstruction. A fast implementation is devised for 3D image reconstruction by fusing the predictions from 2D priors in separate rectilinear orientations. Our demonstrations show that PP-MPI outperforms state-of-the-art iterative techniques with hand-crafted regularizers on both simulated and experimental data. In particular, PP-MPI achieves on average 3.10 dB higher peak signal-to-noise ratio than the top-performing baseline under variable noise levels, and can process 12 frames/sec to permit real-time 3D imaging.
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
Ahmad, R., et al.: Plug-and-play methods for magnetic resonance imaging: Using denoisers for image recovery. IEEE Signal Process. Mag. 37(1), 105–116 (2020). https://doi.org/10.1109/MSP.2019.2949470
Bathke, C., Kluth, T., Brandt, C., Maass, P.: Improved image reconstruction in magnetic particle imaging using structural a priori information. Int. J. Magn. Part. Imaging. 3, 1703015 (2017). https://journal.iwmpi.org/index.php/iwmpi/article/view/64
Chae, B.G.: Neural network image reconstruction for magnetic particle imaging (2017). https://doi.org/10.48550/ARXIV.1709.07560, https://arxiv.org/abs/1709.0756
Deng, L.: The MNIST database of handwritten digit images for machine learning research. IEEE Signal Process. Mag. 29(6), 141–142 (2012)
Dittmer, S., Kluth, T., Baguer, D.O., Maass, P.: A deep prior approach to magnetic particle imaging. In: Deeba, F., Johnson, P., Würfl, T., Ye, J.C. (eds.) MLMIR 2020. LNCS, vol. 12450, pp. 113–122. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-61598-7_11
Güngör, A., et al.: TranSMS: Transformers for super-resolution calibration in magnetic particle imaging. IEEE Trans. Med. Imaging. 1 (2022). https://doi.org/10.1109/TMI.2022.3189693
Hatsuda, T., Shimizu, S., Tsuchiya, H., Takagi, T., Noguchi, T., Ishihara, Y.: A basic study of an image reconstruction method using neural networks for magnetic particle imaging. In: 2015 5th International Workshop on Magnetic Particle Imaging (IWMPI), p. 1 (2015). https://doi.org/10.1109/IWMPI.2015.7107046
Ilbey, S., et al.: Comparison of system-matrix-based and projection-based reconstructions for field free line magnetic particle imaging. Int. J. Magn. Part. Imaging. 3, 1703022 (2017). https://doi.org/10.18416/IJMPI.2017.1703022, https://journal.iwmpi.org/index.php/iwmpi/article/view/81
Kluth, T., Jin, B.: Enhanced reconstruction in magnetic particle imaging by whitening and randomized SVD approximation. Phys. Med. Biol. 64(12), 125026 (2019). https://doi.org/10.1088/1361-6560/ab1a4f
Knopp, T., et al.: Weighted iterative reconstruction for magnetic particle imaging. Phys. Med. Biol. 55(6), 1577–1589 (2010). https://doi.org/10.1088/0031-9155/55/6/003
Knopp, T., Szwargulski, P., Griese, F., Graser, M.: OpenMPIData: An initiative for freely accessible magnetic particle imaging data. Data Brief 28, 104971 (2020). https://doi.org/10.1016/j.dib.2019.104971, https://www.sciencedirect.com/science/article/pii/S2352340919313265
Koch, P., et al.: Neural network for reconstruction of MPI images. In: 9th International Workshop on Magnetic Particle Imaging, pp. 39–40 (2019)
Li, J., Li, J., Xie, Z., Zou, J.: Plug-and-play ADMM for MRI reconstruction with convex nonconvex sparse regularization. IEEE Access 9, 148315–148324 (2021). https://doi.org/10.1109/ACCESS.2021.3124600
McCarthy, J.R., Weissleder, R.: Multifunctional magnetic nanoparticles for targeted imaging and therapy. Adv. Drug Deliv. Rev. 60(11), 1241–1251 (2008)
Zhang, X., Le, T.A., Yoon, J.: Development of a real time imaging-based guidance system of magnetic nanoparticles for targeted drug delivery. J. Magn. Magn. Mater. 427, 345–351 (2017)
Zhang, Y., Tian, Y., Kong, Y., Zhong, B., Fu, Y.: Residual dense network for image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 43(7), 2480–2495 (2021). https://doi.org/10.1109/tpami.2020.2968521
Zheng, B., et al.: Magnetic particle imaging tracks the long-term fate of in vivo neural cell implants with high image contrast. Sci. Rep. 5(1), 1–9 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Askin, B., Güngör, A., Alptekin Soydan, D., Saritas, E.U., Top, C.B., Cukur, T. (2022). PP-MPI: A Deep Plug-and-Play Prior for Magnetic Particle Imaging Reconstruction. In: Haq, N., Johnson, P., Maier, A., Qin, C., Würfl, T., Yoo, J. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2022. Lecture Notes in Computer Science, vol 13587. Springer, Cham. https://doi.org/10.1007/978-3-031-17247-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-17247-2_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17246-5
Online ISBN: 978-3-031-17247-2
eBook Packages: Computer ScienceComputer Science (R0)
