Abstract
Simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) provide complementary information about brain function and structure. Joint reconstruction of MRI and PET images can improve image quality in both modalities, potentially enabling faster MRI and lower-dose PET scans. Current methods for joint MRI-PET reconstruction use priors that model inter-modality dependencies in image gradients. Many methods also ignore the potential in parallel MRI for acceleration. In contrast, we combine accelerated parallel MRI with a joint MRI-PET patch-based dictionary model to infer higher-order dependencies across MRI and PET image neighborhoods. We propose a novel Bayesian framework for joint MRI-PET reconstruction. The results show that our method reconstructs images more accurately, in simulated and in vivo MRI-PET (in parallel MRI) cases, than the state of the art.
Thanks to the Infrastructure Facility for Advanced Research and Education in Diagnostics grant funded by the Department of Biotechnology of the Government of India (RD/0117-DBT0000-002); Reignwood Cultural Foundation; Australian Research Council Linkage grant LP170100494. Thanks to Kamlesh Pawar and Shenpeng Li from MBI for providing reconstructed images using the scanner software.
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References
Awate, S.P., Whitaker, R.T.: Nonparametric neighborhood statistics for MRI denoising. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 677–688. Springer, Heidelberg (2005). https://doi.org/10.1007/11505730_56
Awate, S.P., Whitaker, R.T.: Feature-preserving MRI denoising: a nonparametric empirical-Bayes approach. IEEE Trans. Med. Imaging 26(9), 1242–1255 (2007)
Bilgic, B., Goyal, V., Adalsteinsson, E.: Multi-contrast reconstruction with Bayesian compressed sensing. Magn. Reson. Med. 66(6), 1601 (2011)
Bowsher, J., Johnson, V., Turkington, T., Jaszczak, R., Floyd, C., Coleman, R.: Bayesian reconstruction and use of anatomical a priori information for emission tomography. IEEE Trans. Med. Imaging 15(5), 673–686 (1996)
Ehrhardt, M., et al.: Joint reconstruction of PET-MRI by exploiting structural similarity. Inverse Problems 31(1), 015001 (2014)
Knoll, F., Bredies, K., Pock, T., Stollberger, R.: Second order total generalized variation (TGV) for MRI. Magn. Reson. Med. 65(2), 480–491 (2011)
Knoll, F., Holler, M., Koesters, T., Otazo, R., Bredies, K., Sodickson, D.: Joint MR-PET reconstruction using a multi-channel image regularizer. IEEE Trans. Med. Imaging 36(1), 1–16 (2017)
Leahy, R., Yan, X.: Incorporation of anatomical MR data for improved functional imaging with PET. In: Information Processing in Medical Imaging, pp. 105–120 (1991)
Mehranian, A., et al.: PET image reconstruction using multi-parametric anato-functional priors. Phys. Med. Biol. 62(15), 5975–6007 (2017)
Mehranian, A., Belzunce, M., Prieto, C., Hammers, A., Reader, A.: Synergistic PET and SENSE MR image reconstruction using joint sparsity regularization. IEEE Trans. Med. Imaging 37(1), 20–34 (2018)
Nuyts, J., Fessler, J.: A penalized-likelihood image reconstruction method for emission tomography, compared to post-smoothed maximum-likelihood with matched spatial resolution. IEEE Trans. Med. Imaging 22(9), 1042–1052 (2003)
Pruessmann, K., Weiger, M., Scheidegger, M., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952–962 (1999)
Shepp, L., Vardi, Y.: Maximum likelihood reconstruction for emission tomography. IEEE Trans. Med. Imaging 1(2), 113–122 (1982)
Somayajula, S., Panagiotou, C., Rangarajan, A., Li, Q., Arridge, S., Leahy, R.: PET image reconstruction using information theoretic anatomical priors. IEEE Trans. Med. Imaging 30(3), 537–549 (2011)
Sudarshan, V., Chen, Z., Awate, S.: Joint PET+MRI patch-based dictionary for Bayesian random field PET reconstruction. In: Medical Image Computing and Computer-Assisted Intervention, pp. 338–346 (2018)
Tang, J., Wang, Y., Yao, R., Ying, L.: Sparsity-based PET image reconstruction using MRI learned dictionaries. In: IEEE International Symposium on Biomedical Imaging, p. 1087 (2014)
Vandenberghe, S., Marsden, P.: PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging. Phys. Med. Biol. 60, R115–R154 (2015)
Wang, Z., Bovik, A., Sheikh, H., Simoncelli, E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Proc. 13(4), 600 (2004)
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Sudarshan, V.P., Gupta, K., Egan, G., Chen, Z., Awate, S.P. (2019). Joint Reconstruction of PET + Parallel-MRI in a Bayesian Coupled-Dictionary MRF Framework. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_5
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DOI: https://doi.org/10.1007/978-3-030-32248-9_5
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