Abstract
To obtain high-quality positron emission tomography (PET) scans while reducing radiation exposure to the human body, various approaches have been proposed to reconstruct standard-dose PET (SPET) images from low-dose PET (LPET) images. One widely adopted technique is the generative adversarial networks (GANs), yet recently, diffusion probabilistic models (DPMs) have emerged as a compelling alternative due to their improved sample quality and higher log-likelihood scores compared to GANs. Despite this, DPMs suffer from two major drawbacks in real clinical settings, i.e., the computationally expensive sampling process and the insufficient preservation of correspondence between the conditioning LPET image and the reconstructed PET (RPET) image. To address the above limitations, this paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM). The CPM generates a coarse PET image via a deterministic process, and the IRM samples the residual iteratively. By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Furthermore, two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process, which can enhance the correspondence between the LPET image and the RPET image, further improving clinical reliability. Extensive experiments on two human brain PET datasets demonstrate that our method outperforms the state-of-the-art PET reconstruction methods. The source code is available at https://github.com/Show-han/PET-Reconstruction.
Z. Han and Y. Wang—These authors contributed equally to this work.
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References
Chen, N., Zhang, Y., Zen, H., Weiss, R.J., Norouzi, M., Chan, W.: WaveGrad: estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713 (2020)
Chung, H., Sim, B., Ye, J.C.: Come-closer-diffuse-faster: accelerating conditional diffusion models for inverse problems through stochastic contraction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12413–12422 (2022)
Cui, J., et al.: Pet denoising and uncertainty estimation based on NVAE model using quantile regression loss. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part IV. LNCS, vol. 13434, pp. 173–183. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16440-8_17
MICCAI challenges: Ultra-low dose pet imaging challenge 2022 (2022). https://doi.org/10.5281/zenodo.6361846
Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780–8794 (2021)
Fei, Y., et al.: Classification-aided high-quality pet image synthesis via bidirectional contrastive GAN with shared information maximization. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part VI. LNCS, vol. 13436, pp. 527–537. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16446-0_50
Gong, K., Guan, J., Liu, C.C., Qi, J.: Pet image denoising using a deep neural network through fine tuning. IEEE Trans. Radiat. Plasma Med. Sci. 3(2), 153–161 (2018)
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Häggström, I., Schmidtlein, C.R., Campanella, G., Fuchs, T.J.: DeepPET: a deep encoder-decoder network for directly solving the pet image reconstruction inverse problem. Med. Image Anal. 54, 253–262 (2019)
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840–6851 (2020)
Kang, S.K., Choi, H., Lee, J.S., Initiative, A.D.N., et al.: Translating amyloid pet of different radiotracers by a deep generative model for interchangeability. Neuroimage 232, 117890 (2021)
Kaplan, S., Zhu, Y.M.: Full-dose pet image estimation from low-dose pet image using deep learning: a pilot study. J. Digit. Imaging 32(5), 773–778 (2019)
Kim, K., et al.: Penalized pet reconstruction using deep learning prior and local linear fitting. IEEE Trans. Med. Imaging 37(6), 1478–1487 (2018)
Lei, Y., et al.: Whole-body pet estimation from low count statistics using cycle-consistent generative adversarial networks. Phys. Med. Biol. 64(21), 215017 (2019)
Luo, Y., et al.: 3D transformer-GAN for high-quality PET reconstruction. In: de Bruijne, M., et al. (eds.) MICCAI 2021, Part VI. LNCS, vol. 12906, pp. 276–285. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-87231-1_27
Luo, Y., et al.: Adaptive rectification based adversarial network with spectrum constraint for high-quality pet image synthesis. Med. Image Anal. 77, 102335 (2022)
Metz, L., Poole, B., Pfau, D., Sohl-Dickstein, J.: Unrolled generative adversarial networks. arXiv preprint arXiv:1611.02163 (2016)
Ouyang, J., Chen, K.T., Gong, E., Pauly, J., Zaharchuk, G.: Ultra-low-dose pet reconstruction using generative adversarial network with feature matching and task-specific perceptual loss. Med. Phys. 46(8), 3555–3564 (2019)
Ren, M., Delbracio, M., Talebi, H., Gerig, G., Milanfar, P.: Image deblurring with domain generalizable diffusion models. arXiv preprint arXiv:2212.01789 (2022)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D.J., Norouzi, M.: Image super-resolution via iterative refinement. IEEE Trans. Pattern Anal. Mach. Intell. 45, 4713–4726 (2022)
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. Advances in Neural Inf. Process. Syst. 29 (2016)
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256–2265. PMLR (2015)
Song, Y., Shen, L., Xing, L., Ermon, S.: Solving inverse problems in medical imaging with score-based generative models. arXiv preprint arXiv:2111.08005 (2021)
Ulhaq, A., Akhtar, N., Pogrebna, G.: Efficient diffusion models for vision: a survey. arXiv preprint arXiv:2210.09292 (2022)
Wang, Y., et al.: 3D conditional generative adversarial networks for high-quality pet image estimation at low dose. Neuroimage 174, 550–562 (2018)
Wang, Y., et al.: 3D auto-context-based locality adaptive multi-modality GANs for pet synthesis. IEEE Trans. Med. Imaging 38(6), 1328–1339 (2018)
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A.G., Milanfar, P.: Deblurring via stochastic refinement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16293–16303 (2022)
Xiang, L., et al.: Deep auto-context convolutional neural networks for standard-dose pet image estimation from low-dose PET/MRI. Neurocomputing 267, 406–416 (2017)
Xu, J., Gong, E., Pauly, J., Zaharchuk, G.: 200x low-dose pet reconstruction using deep learning. arXiv preprint arXiv:1712.04119 (2017)
Yu, B., Zhou, L., Wang, L., Shi, Y., Fripp, J., Bourgeat, P.: EA-GANs: edge-aware generative adversarial networks for cross-modality mr image synthesis. IEEE Trans. Med. Imaging 38(7), 1750–1762 (2019)
Zeng, P., et al.: 3D CVT-GAN: a 3D convolutional vision transformer-GAN for pet reconstruction. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part VI. LNCS, vol. 13436, pp. 516–526. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16446-0_49
Zhu, Y., Wu, Y., Olszewski, K., Ren, J., Tulyakov, S., Yan, Y.: Discrete contrastive diffusion for cross-modal and conditional generation. arXiv preprint arXiv:2206.07771 (2022)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (NSFC 62371325, 62071314), Sichuan Science and Technology Program 2023YFG0263, 2023YFG0025, 2023NSFSC0497.
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Han, Z. et al. (2023). Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction. 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_23
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