Abstract
Dual-tracer positron emission tomography (PET) imaging can provide the concentration distribution of two tracers in the body in a single scan, helping to better diagnose and understand diseases. However dual-tracer PET imaging separation is a challenging problem because of indistinguishable gamma photon pairs. In this work, we propose a two-dimensional convolutional network to separate the reconstructed mixed activity images, with the aid of channel attention modules to pay attention to both spatial and temporal information, which play an important role in the separation. Simulation experiments with different tracer pairs, scanning times, and phantoms are conducted to verify the generalization and robustness of the method to noise and individual differences. And its performance is also evaluated with real datasets. These results demonstrate the proposed method might have strong potential for the dual-tracer PET 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
Kadrmas, D.J., Hoffman, J.M.: Methodology for quantitative rapid multi-tracer pet tumor characterizations. Theranostics 3(10), 757 (2013)
Huang, S., Carson, R., Hoffman, E., Kuhl, D., Phelps, M.: An investigation of a double-tracer technique for positron computerized tomography. J. Nucl. Med. 23(9), 816–822 (1982)
Koeppe, R.A., Raffel, D.M., Snyder, S.E., Ficaro, E.P., Kilbourn, M.R., Kuhl, D.E.: Dual-[11c] tracer single-acquisition positron emission tomography studies. J. Cereb. Blood Flow Metab. 21(12), 1480–1492 (2001)
Kudomi, N., Hayashi, T., Teramoto, N., Watabe, H., Kawachi, N., Ohta, Y., Kim, K.M., Iida, H.: Rapid quantitative measurement of CMRO2 and CBF by dual administration of 15o-labeled oxygen and water during a single pet scan - a validation study and error analysis in anesthetized monkeys. J. Cereb. Blood Flow Metab. 25(9), 1209–1224 (2005)
Ruan, D., Liu, H.: Separation of a mixture of simultaneous dual-tracer pet signals: a data-driven approach. IEEE Trans. Nucl. Sci. 64(9), 2588–2597 (2017)
Xu, J., Liu, H.: Deep-learning-based separation of a mixture of dual-tracer single-acquisition pet signals with equal half-lives: a simulation study. IEEE Trans. Radiat. Plasma Med. Sci. 3(6), 649–659 (2019)
Qing, M., Wan, Y., Huang, W., Xu, Y., Liu, H.: Separation of dual-tracer pet signals using a deep stacking network. Nucl. Instrum. Methods Phys. Res. Sect. A 1013, 165681 (2021)
Tong, J., Wang, C., Liu, H.: Temporal information-guided dynamic dual-tracer pet signal separation network. Med. Phys. (2022)
Xu, J., Liu, H.: Three-dimensional convolutional neural networks for simultaneous dual-tracer pet imaging. Phys. Med. Biol. 64(18), 185016 (2019)
Zeng, F., Liu, H.: Dual-tracer pet image direct reconstruction and separation based on three-dimensional encoder-decoder network. In: Optics in Health Care and Biomedical Optics X, vol. 11553, p. 115530X. International Society for Optics and Photonics (2020)
Wang, B., Liu, H.: FBP-net for direct reconstruction of dynamic pet images. Phys. Med. Biol. 65(23), 235008 (2020)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Wang, Z., Simoncelli, E.P., Bovik, A.C.: Multiscale structural similarity for image quality assessment. In: 2003 The Thirty-Seventh Asilomar Conference on Signals, Systems and Computers, vol. 2, pp. 1398–1402. IEEE (2003)
Zubal, I.G., Harrell, C.R., Smith, E.O., Rattner, Z., Gindi, G., Hoffer, P.B.: Computerized three-dimensional segmented human anatomy. Med. Phys. 21(2), 299–302 (1994)
Cheng, X., et al.: Direct parametric image reconstruction in reduced parameter space for rapid multi-tracer pet imaging. IEEE Trans. Med. Imaging 34(7), 1498–1512 (2015)
Koeppe, R., Holthoff, V., Frey, K., Kilbourn, M., Kuhl, D.: Compartmental analysis of [11c] flumazenil kinetics for the estimation of ligand transport rate and receptor distribution using positron emission tomography. J. Cereb. Blood Flow Metab. 11(5), 735–744 (1991)
Chen, S., Ho, C., Feng, D., Chi, Z.: Tracer kinetic modeling of/sup 11/c-acetate applied in the liver with positron emission tomography. IEEE Trans. Med. Imaging 23(4), 426–432 (2004)
Terada, T., et al.: In vivo mitochondrial and glycolytic impairments in patients with Alzheimer disease. Neurology 94(15), e1592–e1604 (2020)
Acknowledgements
This work was supported by the Talent Program of Zhejiang Province (2021R51004).
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
Lian, D., Li, Y., Liu, H. (2022). Spatiotemporal Attention Constrained Deep Learning Framework for Dual-Tracer PET Imaging. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-031-12053-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12052-7
Online ISBN: 978-3-031-12053-4
eBook Packages: Computer ScienceComputer Science (R0)