Abstract
Charge sharing induces spectral and spatial distortions on photon counting detectors which must be corrected using methods such as charge summing circuitry. We propose a method of correction using a spatial-temporal convolutional neural network based on the CycN-Net design. Our results were compared to an analytical scalar matrix correction and a U-Net. We show improvements in two energy channels set to 50 and 60 kev with a mean absolute percentage error reduced from 4.84% and 7.46% to 3.95% and 5.14% respectively when compared to the scalar matrix approach. We believe this shows the potential viability of utilising the spatial-temporal CNN approach for correcting charge sharing distortions in higher energy ranges, where photon counts tend to be lower for photon counting detectors.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atharifard, A., et al.: Per-pixel energy calibration of photon counting detectors. J. Instrum. 12(03), C03085 (2017)
Bai, T., et al.: Deep interactive denoiser (did) for x-ray computed tomography. IEEE Trans. Med. Imaging 40(11), 2965–2975 (2021)
Ballabriga, R., et al.: The medipix3rx: a high resolution, zero dead-time pixel detector readout chip allowing spectroscopic imaging. J. Instrum. 8(02), C02016 (2013)
Dreier, E.S., et al.: Spectral correction algorithm for multispectral CDTE x-ray detectors. Opt. Eng. 57(5), 054117 (2018)
Flohr, T., et al.: Photon-counting CT review. Physica Med. 79, 126–136 (2020)
Gimenez, E., et al.: Study of charge-sharing in medipix3 using a micro-focused synchrotron beam. J. Instrum. 6(01), C01031 (2011)
Hamel, L., et al.: Optimization of single-sided charge-sharing strip detectors. In: 2006 IEEE Nuclear Science Symposium Conference Record. vol. 6, pp. 3759–3761 (2006). https://doi.org/10.1109/NSSMIC.2006.353811
Holbrook, M., Clark, D., Badea, C.: Deep learning based spectral distortion correction and decomposition for photon counting CT using calibration provided by an energy integrated detector. In: Medical Imaging 2021: Physics of Medical Imaging, vol. 11595, p. 1159520. International Society for Optics and Photonics (2021)
Leng, S., et al.: Dose-efficient ultrahigh-resolution scan mode using a photon counting detector computed tomography system. J. Med. Imaging 3(4), 043504 (2016)
Li, M., Rundle, D.S., Wang, G.: X-ray photon-counting data correction through deep learning. arXiv preprint arXiv:2007.03119 (2020)
McGregor, D., Hermon, H.: Room-temperature compound semiconductor radiation detectors. Nucl. Instrum. Methods Phys. Res. Sect. A Acceler. Spectro. Detect. Assoc. Equip. 395(1), 101–124 (1997). https://doi.org/10.1016/S0168-9002(97)00620-7, www.sciencedirect.com/science/article/pii/S0168900297006207
Pellegrini, G., et al.: Direct charge sharing observation in single-photon-counting pixel detector. Nucl. Instrum. Methods Phys. Res. Sect. A 573(1–2), 137–140 (2007)
Symons, R., et al.: Photon-counting CT for simultaneous imaging of multiple contrast agents in the abdomen: an in vivo study. Med. Phys. 44(10), 5120–5127 (2017)
Taguchi, K.: Multi-energy inter-pixel coincidence counters for charge sharing correction and compensation in photon counting detectors. Med. Phys. 47(5), 2085–2098 (2020)
Tao, X., Wang, Y., Lin, L., Hong, Z., Ma, J.: Learning to reconstruct CT images from the VVBP-tensor. IEEE Trans. Med. Imaging 40, 3030–3041 (2021)
Wang, G., Jacob, M., Mou, X., Shi, Y., Eldar, Y.C.: Deep tomographic image reconstruction: yesterday, today, and tomorrow-editorial for the 2nd special issue machine learning for image reconstruction. IEEE Trans. Med. Imaging 40(11), 2956–2964 (2021)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861
Zhi, S., Kachelrieß, M., Pan, F., Mou, X.: CYCN-net: a convolutional neural network specialized for 4D CBCT images refinement. IEEE Trans. Med. Imaging 40(11), 3054–3064 (2021)
Acknowledgement
This project was funded by the Ministry of Business, Innovation and Employment (MBIE), New Zealand under contract number UOCX1404, by MARS Bioimaging Ltd and the Ministry of Education through the MedTech CoRE. The authors would like to acknowledge the Medipix2, Medipix3 and Medipix4 collaborations. Also, we would like to take this opportunity to acknowledge the generous support of the MARS Collaboration. European MARS Collaboration: S. A. Adebileje, S. D. Alexander, M. R. Amma, M. Anjomrouz, F. Asghariomabad, S. T. Bell, R. Senzing, F. O. Bochud, A. P. H. Butler, P. H. Butler, P. Carbonez, C. Chambers, K. M. Chapagain, A. I. Chernoglazov, J. A. Clark, J. S. Crighton, S. Dahal, T. Dapamede, A. Denys, N. J. A. deRuiter, D. Dixit, R. M. N. Doesburg, K. Dombroski, N. Duncan, S. P. Gieseg, A. Gopinathan, B. P. Goulter, J. L. Healy, L. Holmes, K. Jonker, T. Kirkbride, C. Lowe, V. B. H. Mandalika, A. Matanaghi, M. Moghiseh, M. Nowak, B. Paulmier, D. Racine, P. Renaud, D. Rundle, N. Schleich, E. Searle, J. S. Sheeja, L. Vanden Broeke, F. R. Verdun, V. Vitzthum, Vivek V. S., E. P. Walker, M. Wijesooriya, W. R. Younger.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Smith, A., Atlas, J., Atharifard, A. (2023). Correcting Charge Sharing Distortions in Photon Counting Detectors Utilising a Spatial-Temporal CNN. In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-25825-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25824-4
Online ISBN: 978-3-031-25825-1
eBook Packages: Computer ScienceComputer Science (R0)