Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model (2403.18139v1)
Abstract: Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weighted-MRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded than the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to OSEM. Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI.
- A. Mehranian, M. Belzunce, F. Niccolini, M. Politis, C. Prieto, F. Turkheimer, A. Hammers, A. Reader, “PET image reconstruction using multi-parametric anato-functional priors,” in Physics in Medicine and Biology, 2017, 62, pp. 5975.
- G. Schramm, M. Holler, A. Rezai, K. Vunckx, F. Knoll, K. Bredies, F. Boada, J. Nuyts, “Evaluation of Parallel Level Sets and Bowsher’s Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction,” in IEEE Trans Med Imaging. 2018 Feb; 37(2): 590–603.
- Y. J. Tsai, G. Schramm, S. Ahn, A. Bousse, S. Arridge, J. Nuyts, B. F. Hutton, C. W. Stearns, K. Thielemans, “Benefits of Using a Spatially-Variant Penalty Strength With Anatomical Priors in PET Reconstruction,” in IEEE Trans Med Imaging. 2020 Jan; 39(1): 11–22.
- F. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 9, pp. 10850–10869, 2023.
- L. Jiang, Y. Mao, X. Chen, X. Wang, and C. Li, “CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI Synthesis,” arXiv:2303.14081, Mar. 2023.
- K. Gong, K. A. Johnson, G. E. Fakhri, Q. Li, and T. Pan, “PET image denoising based on denoising diffusion probabilistic models,” European Journal of Nuclear Medicine and Molecular Imaging, 2023.
- J. Nuyts, D. Beque, P. Dupont and L. Mortelmans, “A concave prior penalizing relative differences for maximum-a-posteriori reconstruction in emission tomography,” in IEEE Transactions on Nuclear Science, vol. 49, no. 1, pp. 56-60, Feb. 2002, doi: 10.1109/TNS.2002.998681.
- J. Cabello, M. T. Jurkiewicz, A. Andrade, T. L. S. Benzinger, H. An and U. C. Anazodo, “Evaluation of an MRI-Guided PET Image Reconstruction Approach With Adaptive Penalization Strength,” in IEEE Transactions on Radiation and Plasma Medical Sciences, doi: 10.1109/TRPMS.2024.3352983.
- W. Penny, K. Friston, J. Ashburner, S. Kiebel and T. Nichols, “Statistical Parametric Mapping: The Analysis of Functional Brain Images”, Elsevier , 2007.
- P. Dhariwal, and A. Nichol. “Diffusion models beat gans on image synthesis.” Advances in neural information processing systems 34 (2021): 8780-8794.
- A. Q. Nichol, and P. Dhariwal. “Improved denoising diffusion probabilistic models.” International conference on machine learning. PMLR, 2021.
- J. Ho, A. Jain, and P. Abbeel, “Denoising Diffusion Probabilistic Models,” in Proc. Adv. Neural Inf. Process. Syst., 2020, pp. 6840–6851.
- H. Chung, D. Ryu, M. T. McCann, M. L. Klasky, J. C. Ye. “Solving 3d inverse problems using pre-trained 2d diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
- H. Xie, W. Gan, B. Zhou, X. Chen, Q. Liu, X. Guo, L. Guo, H. An, U. S. Kamilov, G. Wang, C. Liu. “DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging,” arXiv:2311.04248, 2023.
- Y. Hu, S.V. Kothapalli, W. Gan, A.L. Sukstanskii, G.F. Wu, M. Goyal, D.A. Yablonskiy and U.S. Kamilov “DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5 D Diffusion Model,” arXiv:2311.18073, 2023.
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