Abstract
X-ray and gamma-ray photons are widely used for imaging but require a mathematical reconstruction step, known as tomography, to produce cross-sectional images from the measured data. Theoretically, the back-to-back annihilation photons produced by positron–electron annihilation can be directly localized in three-dimensional space using time-of-flight information without tomographic reconstruction; however, this has not yet been demonstrated due to the insufficient timing performance of available radiation detectors. Here we develop techniques based on detecting prompt Cherenkov photons, which, when combined with a convolutional neural network for timing estimation, resulted in an average timing precision of 32 ps, corresponding to a spatial precision of 4.8 mm. We show this is sufficient to produce cross-sectional images of a positron-emitting radionuclide directly from the detected coincident annihilation photons, without using any tomographic reconstruction algorithm. The reconstruction-free imaging demonstrated here directly localizes positron emission and frees the design of an imaging system from the geometric and sampling constraints that are normally present for tomographic reconstruction.
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Data and code availability
The data and code used to produce the results presented in this study are available online at ref. 28.
References
Clackdoyle, R. & Defrise, M. Tomographic reconstruction in the 21st Century. IEEE Signal Proc. Mag. 27, 60–80 (2010).
Lange, K. & Carson, R. EM reconstruction algorithms for emission and transmission tomography. J. Comput. Assist. Tomogr. 8, 306–316 (1984).
Wright, G. A. Magnetic resonance imaging. IEEE Signal Proc. Mag. 14, 56–66 (1997).
Lecoq, P. et al. Roadmap toward the 10 ps time-of-flight PET challenge. Phys. Med. Biol. 65, 21RM01 (2020).
van Sluis, J. et al. Performance characteristics of the digital biograph vision PET/CT system. J. Nucl. Med. 60, 1031–1036 (2019).
Budinger, T. F. Time-of-flight positron emission tomography: status relative to conventional PET. J. Nucl. Med. 24, 73–78 (1983).
Schaart, D. R. Physics and technology of time-of-flight PET detectors. Phys. Med. Biol. 66, 09TR01 (2021).
Lecoq, P. et al. Factors influencing time resolution of scintillators and ways to improve them. IEEE Trans. Nucl. Sci. 57, 2411–2416 (2010).
Nemallapudi, M. V., Gundacker, S., Lecoq, P. & Auffray, E. Single photon time resolution of state of the art SiPMs. J. Inst. 11, P10016–P10016 (2016).
Cates, J. W., Gundacker, S., Auffray, E., Lecoq, P. & Levin, C. S. Improved single photon time resolution for analog SiPMs with front end readout that reduces influence of electronic noise. Phys. Med. Biol. 63, 185022 (2018).
Čerenkov, P. A. Visible radiation produced by electrons moving in a medium with velocities exceeding that of light. Phys. Rev. 52, 378–379 (1937).
Jelley, J. V. Čerenkov radiation and its applications. Br. J. Appl. Phys. 6, 227–232 (1955).
Korpar, S., Dolenec, R., Križan, P., Pestotnik, R. & Stanovnik, A. Study of TOF PET using Cherenkov light. Nucl. Instrum. Methods Phys. Res. A 654, 532–538 (2011).
Brunner, S. E., Gruber, L., Marton, J., Suzuki, K. & Hirtl, A. Studies on the Cherenkov effect for improved time resolution of TOF-PET. IEEE Trans. Nucl. Sci. 61, 443–447 (2014).
Kwon, S. I., Gola, A., Ferri, A., Piemonte, C. & Cherry, S. R. Bismuth germanate coupled to near ultraviolet silicon photomultipliers for time-of-flight PET. Phys. Med. Biol. 61, L38–L47 (2016).
Kume, H., Koyama, K., Nakatsugawa, K., Suzuki, S. & Fatlowitz, D. Ultrafast microchannel plate photomultipliers. Appl. Optics. 27, 1170–1178 (1988).
Ota, R. et al. Coincidence time resolution of 30 ps FWHM using a pair of Cherenkov-radiator-integrated MCP-PMTs. Phys. Med. Biol. 64, 07LT01 (2019).
Ota, R. et al. Lead-free MCP to improve coincidence time resolution and reduce MCP direct interactions. Phys. Med. Biol. 66, 064006 (2021).
Berg, E. & Cherry, S. R. Using convolutional neural networks to estimate time-of-flight from PET detector waveforms. Phys. Med. Biol. 63, 02LT01 (2018).
Hendee, W. R. Cross sectional medical imaging: a history. RadioGraphics 9, 1155–1180 (1989).
Jan, S. et al. GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy. Phys. Med. Biol. 56, 881–901 (2011).
Sarrut, D. et al. Advanced Monte Carlo simulations of emission tomography imaging systems with GATE. Phys. Med. Biol. 66, 10TR03 (2021).
Ota, R. et al. Precise analysis of the timing performance of Cherenkov-radiator-integrated MCP-PMTs: analytical deconvolution of MCP direct interactions. Phys. Med. Biol. 65, 10NT03 (2020).
Performance Measurements of Small Animal Positron Emission Tomographs NEMA Standards Publication NU 4-2008 (National Electrical Manufacturers Association, 2008).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
He, K., Zhang, X., Ren, S. & Sun, J. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).
D’Ambrosio, D., Zagni, F., Spinelli, A. E. & Marengo, M. Attenuation correction for small animal PET images: a comparison of two methods. Comp. Math. Methods. Med. 2013, 103476 (2013).
Berg, E., Kwon, S. I., Ota, R. & Cherry, S. R. Waveform Data for Direct Positron Imaging (Dryad, 2021); https://doi.org/10.25338/B89623
Acknowledgements
We thank H. Ohba, S. Nishiyama and M. Kanazawa at Hamamatsu Photonics for their technical support, and G. Burkett and S. Lucero at the University of California Davis for fabricating the spatial resolution phantom and three-dimensionally printed holders used in this study. This study was supported by National Institutes of Health grants R35 CA197608 and R03 EB027268.
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This study was conceived of by S.R.C, R.O., S.I.K., E.B., F.H., T.O. and T.H. The methodology was designed by all authors. R.O. and T.O. provided specific resources. Experiments were conducted by S.I.K., R.O., E.B. and F.H. Data analysis was conducted by S.I.K., R.O. and E.B., with supervision by S.R.C., T.O. and T.H. The original draft of the manuscript was written by S.R.C., S.I.K., R.O. and E.B., and reviewed and edited by all authors.
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Peer review information Nature Photonics thanks Paul Lecoq, Stefaan Vandenberghe and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Accuracy of source localization based on measured timing difference.
Location of a radioactive point source as determined by the timing difference tA–tB (equation 1) versus the known location of the source, across a distance of 10 cm, using the data in Fig. 2c. Error bar represents ± (FWHM of the distribution at each location ÷ 2). The source location is accurately determined over the entire range.
Extended Data Fig. 2 Effect of number of detected events on dPEI images.
dPEI images of the 2-D Hoffman brain phantom generated using a different number of events: a, ~10,000 events, b, ~20,000 events, c, ~30,000 events, and d, ~40,000 events. Each acquisition was performed over 44 different x-positions (4-mm intervals) and each scan took a total of 6 hours and used ~850 MBq (~23 mCi) of 18F-FDG activity. All images were post-processed (analytical attenuation correction, Gaussian smoothing (𝜎=0.8), and 4-fold up-sampling) as shown in Extended Data Fig. 6. This image demonstrates the relatively modest number of detected events needed to form an image of a slice representing the human brain, with little improvement above 20,000 events.
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Supplementary Figs. 1–4, and Tables 1 and 2.
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Kwon, S.I., Ota, R., Berg, E. et al. Ultrafast timing enables reconstruction-free positron emission imaging. Nat. Photon. 15, 914–918 (2021). https://doi.org/10.1038/s41566-021-00871-2
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DOI: https://doi.org/10.1038/s41566-021-00871-2
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