Abstract
Positron emission tomography (PET) is a molecular imaging technique relying on a step, namely attenuation correction (AC), to correct radionuclide distribution based on pre-determined attenuation coefficients. Conventional AC techniques require additionally-acquired computed tomography (CT) or magnetic resonance (MR) images to calculate attenuation coefficients, which increases imaging expenses, time costs, or radiation hazards to patients, especially for whole-body scanners. In this paper, considering technological advances in acquiring more anatomical information in raw PET images, we propose to conduct attenuation correction to PET by itself. To achieve this, we design a deep learning based framework, namely anatomical skeleton-enhanced generation (ASEG), to generate pseudo CT images from non-attenuation corrected PET images for attenuation correction. Specifically, ASEG contains two sequential modules, i.e., a skeleton prediction module and a tissue rendering module. The former module first delineates anatomical skeleton and the latter module then renders tissue details. Both modules are trained collaboratively with specific anatomical-consistency constraint to guarantee tissue generation fidelity. Experiments on four public PET/CT datasets demonstrate that our ASEG outperforms existing methods by achieving better consistency of anatomical structures in generated CT images, which are further employed to conduct PET attenuation correction with better similarity to real ones. This work verifies the feasibility of generating pseudo CT from raw PET for attenuation correction without acquising additional images. The associated implementation is available at https://github.com/YongshengPan/ASEG-for-PET2CT.
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References
Armanious, K., et al.: Independent attenuation correction of whole body [18F] FDG-PET using a deep learning approach with generative adversarial networks. EJNMMI Res. 10(1), 1–9 (2020)
Dong, X., et al.: Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging. Phys. Med. Biol. 64(21), 215016 (2019)
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
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., et al.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045–1057 (2013)
Guo, R., Xue, S., Hu, J., Sari, H., Mingels, C., et al.: Using domain knowledge for robust and generalizable deep learning-based CT-free PET attenuation and scatter correction. Nat. Commun. 13, 5882 (2022)
Häggström, M.: Hounsfield units. https://radlines.org/Hounsfield_unit
Liu, F., Jang, H., Kijowski, R., Bradshaw, T., McMillan, A.B.: Deep learning MR imaging-based attenuation correction for PET/MR imaging. Radiology 286(2), 676–684 (2018)
Pan, Y., Liu, M., Xia, Y., Shen, D.: Disease-image-specific learning for diagnosis-oriented neuroimage synthesis with incomplete multi-modality data. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6839–6853 (2021)
Rabinovici, G.D., Gatsonis, C., Apgar, C., Chaudhary, K., Gareen, I., et al.: Association of amyloid positron emission tomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia. JAMA 321(13), 1286–1294 (2019)
Rodríguez Colmeiro, R., Verrastro, C., Minsky, D., Grosges, T.: Towards a whole body [18F] FDG positron emission tomography attenuation correction map synthesizing using deep neural networks. J. Comput. Sci. Technol. 21, 29–41 (2021)
Shiri, I., et al.: Direct attenuation correction of brain PET images using only emission data via a deep convolutional encoder-decoder (Deep-DAC). Eur. Radiol. 29(12), 6867–6879 (2019). https://doi.org/10.1007/s00330-019-06229-1
Spencer, B.A., Berg, E., Schmall, J.P., Omidvari, N., Leung, E.K., et al.: Performance evaluation of the uEXPLORER total-body PET/CT scanner based on NEMA NU 2-2018 with additional tests to characterize PET scanners with a long axial field of view. J. Nucl. Med. 62(6), 861–870 (2021)
Tan, H., et al.: Total-body PET/CT: current applications and future perspectives. Am. J. Roentgenol. 215(2), 325–337 (2020)
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: High-resolution image synthesis and semantic manipulation with conditional GANs. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8798–8807 (2018)
Wasserthal, J., Meyer, M., Breit, H., Cyriac, J., Yang, S., Segeroth, M.: TotalSegmentator: robust segmentation of 104 anatomical structures in CT images. arXiv preprint arXiv:2208.05868 (2022)
Zhao, R., et al.: Rethinking dice loss for medical image segmentation. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 851–860. IEEE (2020)
Acknowledgements
This work was supported in part by The China Postdoctoral Science Foundation (Nos. 2021M703340, BX2021333), National Natural Science Foundation of China (Nos. 62131015, 62203355), Science and Technology Commission of Shanghai Municipality (STCSM) (No. 21010502600), and The Key R &D Program of Guangdong Province, China (No. 2021B0101420006).
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Pan, Y., Liu, F., Jiang, C., Huang, J., Xia, Y., Shen, D. (2023). Revealing Anatomical Structures in PET to Generate CT for Attenuation Correction. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_3
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