Abstract
Deep learning based PET image reconstruction methods have achieved promising results recently. However, most of these methods follow a supervised learning paradigm, which rely heavily on the availability of high-quality training labels. In particular, the long scanning time required and high radiation exposure associated with PET scans make obtaining these labels impractical. In this paper, we propose a dual-domain unsupervised PET image reconstruction method based on learned descent algorithm, which reconstructs high-quality PET images from sinograms without the need for image labels. Specifically, we unroll the proximal gradient method with a learnable \(l_{2,1}\) norm for PET image reconstruction problem. The training is unsupervised, using measurement domain loss based on deep image prior as well as image domain loss based on rotation equivariance property. The experimental results demonstrate the superior performance of proposed method compared with maximum-likelihood expectation-maximization (MLEM), total-variation regularized EM (EM-TV) and deep image prior based method (DIP).
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
Nordberg, A., Rinne, J., Kadir, A., Langström, B.: The use of PET in Alzheimer disease. Nat. Rev. Neurol. 6, 78–87 (2010)
Cui, J., et al.: PET image denoising using unsupervised deep learning. Eur. J. Nucl. Med. Mol. Imaging 46, 2780–2789 (2019)
Onishi, Y., et al.: Anatomical-guided attention enhances unsupervised PET image denoising performance. Med. Image Anal. 74, 102226 (2021)
Zhu, B., Liu, J., Cauley, S., Rosen, B., Rosen, M.: Image reconstruction by domain-transform manifold learning. Nature 555, 487–492 (2018)
Häggström, I., Schmidtlein, C., Campanella, G., Fuchs, T.: DeepPET: a deep encoder-decoder network for directly solving the PET image reconstruction inverse problem. Med. Image Anal. 54, 253–262 (2019)
Li, Y., et al.: A deep neural network for parametric image reconstruction on a large axial field-of-view PET. Eur. J. Nucl. Med. Mol. Imaging 50, 701–714 (2023)
Gong, K., et al.: Iterative PET image reconstruction using convolutional neural network representation. IEEE Trans. Med. Imaging 38, 675–685 (2018)
Kim, K., et al.: Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans. Med. Imaging 37, 1478–1487 (2018)
Li, S., Wang, G.: Deep kernel representation for image reconstruction in PET. IEEE Trans. Med. Imaging 41, 3029–3038 (2022)
Li, S., Gong, K., Badawi, R., Kim, E., Qi, J., Wang, G.: Neural KEM: a kernel method with deep coefficient prior for PET image reconstruction. IEEE Trans. Med. Imaging 42, 785–796 (2022)
Mehranian, A., Reader, A.: Model-based deep learning PET image reconstruction using forward-backward splitting expectation-maximization. IEEE Trans. Radiat. Plasma Med. Sci. 5, 54–64 (2020)
Lim, H., Chun, I., Dewaraja, Y., Fessler, J.: Improved low-count quantitative PET reconstruction with an iterative neural network. IEEE Trans. Med. Imaging 39, 3512–3522 (2020)
Hu, R., Liu, H.: TransEM: residual swin-transformer based regularized PET image reconstruction. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022, Part IV. LNCS, vol. 13434, pp. 184–193. Springer, Cham (2022)
Gong, K., et al.: MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction. In: 15th International Meeting on Fully Three-dimensional Image Reconstruction in Radiology And Nuclear Medicine, vol. 11072, pp. 109–113 (2019)
Gong, K., Catana, C., Qi, J., Li, Q.: PET image reconstruction using deep image prior. IEEE Trans. Med. Imaging 38, 1655–1665 (2018)
Hashimoto, F., Ote, K., Onishi, Y.: PET image reconstruction incorporating deep image prior and a forward projection model. IEEE Trans. Radiat. Plasma Med. Sci. 6, 841–846 (2022)
Shen, C., et al.: Unsupervised Bayesian PET reconstruction. IEEE Trans. Radiat. Plasma Med. Sci. 7, 75–190 (2022)
Chen, D., Tachella, J., Davies, M.: Robust equivariant imaging: a fully unsupervised framework for learning to image from noisy and partial measurements. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5647–5656 (2022)
Chen, Y., Liu, H., Ye, X., Zhang, Q.: Learnable descent algorithm for nonsmooth nonconvex image reconstruction. SIAM J. Imag. Sci. 14, 1532–1564 (2021)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings Of The IEEE Conference On Computer Vision And Pattern Recognition, pp. 9446–9454 (2018)
Noh, J., Fessler, J., Kinahan, P.: Statistical sinogram restoration in dual-energy CT for PET attenuation correction. IEEE Trans. Med. Imaging 28, 1688–1702 (2009)
Shepp, L., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1, 113–122 (1982)
Jonsson, E., Huang, S., Chan, T.: Total variation regularization in positron emission tomography. CAM Report. 9848 (1998)
Zubal, I., Harrell, C., Smith, E., Rattner, Z., Gindi, G., Hoffer, P.: Computerized three-dimensional segmented human anatomy. Med. Phys. 21, 299–302 (1994)
Qi, J., Leahy, R.: A theoretical study of the contrast recovery and variance of MAP reconstructions from PET data. IEEE Trans. Med. Imaging 18, 293–305 (1999)
Acknowledgements
This work is supported in part by the National Key Technology Research and Development Program of China (No: 2021YFF0501503), the Talent Program of Zhejiang Province (No: 2021R51004), the Key Research and Development Program of Zhejiang Province (No: 2021C03029) and by NSF grants: DMS2152961.
Author information
Authors and Affiliations
Corresponding authors
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
Hu, R., Chen, Y., Kim, K., Rockenbach, M.A.B.C., Li, Q., Liu, H. (2023). DULDA: Dual-Domain Unsupervised Learned Descent Algorithm for PET Image 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_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-43999-5_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43998-8
Online ISBN: 978-3-031-43999-5
eBook Packages: Computer ScienceComputer Science (R0)
