Abstract
Dynamic Positron Emission Tomography imaging (dPET) provides temporally resolved images of a tracer. Voxel-wise physiologically-based pharmacokinetic modeling of the Time Activity Curves (TAC) extracted from dPET can provide relevant diagnostic information for clinical workflow. Conventional fitting strategies for TACs are slow and ignore the spatial relation between neighboring voxels. We train a spatio-temporal UNet to estimate the kinetic parameters given TAC from dPET. This work introduces a self-supervised loss formulation to enforce the similarity between the measured TAC and those generated with the learned kinetic parameters. Our method provides quantitatively comparable results at organ level to the significantly slower conventional approaches while generating pixel-wise kinetic parametric images which are consistent with expected physiology. To the best of our knowledge, this is the first self-supervised network that allows voxel-wise computation of kinetic parameters consistent with a non-linear kinetic model.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
Avula, X.J.: Mathematical modeling. In: Meyers, R.A. (ed.) Encyclopedia of Physical Science and Technology, 3rd edn., pp. 219–230. Academic Press, New York (2003)
Besson, F.L., et al.: 18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F] FDG PET-MRI data. EJNMMI Res. 10(1), 1–13 (2020)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Cui, J., Gong, K., Guo, N., Kim, K., Liu, H., Li, Q.: Unsupervised PET logan parametric image estimation using conditional deep image prior. Med. Image Anal. 80, 102519 (2022)
Dias, A.H., Hansen, A.K., Munk, O.L., Gormsen, L.C.: Normal values for 18F-FDG uptake in organs and tissues measured by dynamic whole body multiparametric FDG PET in 126 patients. EJNMMI Res. 12(1), 1–14 (2022)
Dimitrakopoulou-Strauss, A., Pan, L., Sachpekidis, C.: Kinetic modeling and parametric imaging with dynamic PET for oncological applications: general considerations, current clinical applications, and future perspectives. Eur. J. Nucl. Med. Mol. Imaging 48, 21–39 (2021). https://doi.org/10.1007/s00259-020-04843-6
Fahrni, G., Karakatsanis, N.A., Di Domenicantonio, G., Garibotto, V., Zaidi, H.: Does whole-body Patlak \(^{18}\)F-FDG PET imaging improve lesion detectability in clinical oncology? Eur. Radiol. 29, 4812–4821 (2019). https://doi.org/10.1007/s00330-018-5966-1
Guo, X., Zhou, B., Chen, X., Liu, C., Dvornek, N.C.: MCP-Net: inter-frame motion correction with Patlak regularization for whole-body dynamic PET. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part IV. LNCS, vol. 13434, pp. 163–172. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16440-8_16
Huang, Z., et al.: Parametric image generation with the uEXPLORER total-body PET/CT system through deep learning. Eur. J. Nucl. Med. Mol. Imaging 49(8), 2482–2492 (2022). https://doi.org/10.1007/s00259-022-05731-x
Küstner, T., et al.: CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions. Sci. Rep. 10(1), 13710 (2020)
Li, A., Tang, J.: Direct parametric image reconstruction for dynamic myocardial perfusion PET using artificial neural network representation (2022)
Li, Y., et al.: A deep neural network for parametric image reconstruction on a large axial field-of-view PET. Eur. J. Nucl. Med. Mol. Imaging 50(3), 701–714 (2023). https://doi.org/10.1007/s00259-022-06003-4
Moradi, H., Vegh, V., O’Brien, K., Hammond, A., Reutens, D.: FDG-PET kinetic model identifiability and selection using machine learning (2022)
Pantel, A.R., Viswanath, V., Muzi, M., Doot, R.K., Mankoff, D.A.: Principles of tracer kinetic analysis in oncology, part I: principles and overview of methodology. J. Nucl. Med. 63(3), 342–352 (2022)
Pantel, A.R., Viswanath, V., Muzi, M., Doot, R.K., Mankoff, D.A.: Principles of tracer kinetic analysis in oncology, part II: examples and future directions. J. Nucl. Med. 63(4), 514–521 (2022)
Sari, H., et al.: First results on kinetic modelling and parametric imaging of dynamic \(^{18}\)F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur. J. Nucl. Med. Mol. Imaging 49, 1997–2009 (2022). https://doi.org/10.1007/s00259-021-05623-6
Snyman, J.A., Wilke, D.N., et al.: Practical Mathematical Optimization. Springer, New York (2005). https://doi.org/10.1007/b105200
Surti, S., Pantel, A.R., Karp, J.S.: Total body PET: why, how, what for? IEEE Trans. Radiat. Plasma Med. Sci. 4(3), 283–292 (2020)
Wang, G., et al.: Total-body PET multiparametric imaging of cancer using a voxelwise strategy of compartmental modeling. J. Nucl. Med. 63(8), 1274–1281 (2022)
Watabe, H.: Compartmental modeling in PET kinetics. In: Khalil, M.M. (ed.) Basic Science of PET Imaging, pp. 323–352. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-40070-9_14
Zuo, Y., Sarkar, S., Corwin, M.T., Olson, K., Badawi, R.D., Wang, G.: Structural and practical identifiability of dual-input kinetic modeling in dynamic PET of liver inflammation. Phys. Med. Biol. 64(17), 175023 (2019)
Acknowledgements
This work was partially funded by the German Research Foundation (DFG, grant NA 620/51-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
De Benetti, F. et al. (2023). Self-supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14220. Springer, Cham. https://doi.org/10.1007/978-3-031-43907-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-43907-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43906-3
Online ISBN: 978-3-031-43907-0
eBook Packages: Computer ScienceComputer Science (R0)