Abstract
The plug-and-play prior (P\(^{3}\)) is known as denoising prior which has been successfully applied to various imaging problems. In this work for accelerated dynamic cardiac cine magnetic resonance imaging (Dcc-MRI), we introduce a Spatio-tEmporal correlAtion based hyBrid plUg-and-play priorS (SEABUS) integrating local P\(^{3}\) and nonlocal P\(^{3}\), which further help both suppress aliasing artifacts and capture dynamic features. Specifically, the local P\(^{3}\) enforces the pixel-wise edge-orientation consistency by reference frame guided multi-scale orientation projection (MSOP) in a subset of few adjacent frames. The nonlocal P\(^{3}\) constrains the cube-wise anatomic-structure similarity by cube matching and 4D filtering (CM4D) in all frames. By composite splitting algorithm (CSA), the SEABUS is coupled into fast iterative shrinkage-thresholding algorithm (FISTA) and then a new Dcc-MRI approach that is named as SEABUS-FCSA is proposed. The experimental results on the in-vivo cardiac MR datasets demonstrated the efficiency and potential of the proposed SEABUS-FCSA approach.
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References
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Candes, E.J., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
Wright, J., Ganesh, A., Rao, S., Ma, Y.: Robust principal component analysis: exact recovery of corrupted low-rank matrices (2009). https://arxiv.org/abs/0905.0233
Candès, E., Recht, B.: Exact matrix completion via convex optimization. Found. Comput. Math. 9(6), 717 (2009)
Angshul, M., Ward, R.K., Tyseer, A.: Compressed sensing based real-time dynamic MRI reconstruction. IEEE Trans. Med. Imaging 31(12), 2253–2266 (2012)
Chen, C., Li, Y.Q., Axel, L., Huang, J.Z.: Real time dynamic MRI by exploiting spatial and temporal sparsity. Magn. Reson. Imaging 34(4), 473–482 (2016)
Fan, L.A., Dl, A., Xj, A., Wq, A., Qi, X.B., Bs, A.: Dynamic cardiac MRI reconstruction using motion aligned locally low rank tensor (mallrt) - sciencedirect. Magn. Reson. Imaging 66, 104–115 (2020)
Otazo, R., Candès, E., Sodickson, D.K.: “Low” rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. In: Magnetic Resonance in Medicine, vol. 73, no. 3, pp. 1125–1136 (2015)
Miao, X., et al.: Accelerated cardiac cine MRI using locally low rank and finite difference constraints. Magn. Reson. Imaging 34(6), 707–714 (2016)
Ravishankar, S., Moore, B.E., Nadakuditi, R.R., Fessler, J.A.: Low-rank and adaptive sparse signal (LASSI) models for highly accelerated dynamic imaging. IEEE Trans. Med. Imaging 36(5), 1116–1128 (2017)
Liu, Y., Liu, T., Liu, J., Zhu, C.: Smooth robust tensor principal component analysis for compressed sensing of dynamic MRI. Pattern Recogn. 102(6), 107252 (2020)
Lingala, S.G., Jacob, M.: Blind compressive sensing dynamic MRI. IEEE Trans. Med. Imaging 32(6), 1132–1145 (2013)
Shetty, G.N., Slavakis, K., Bose, A., Nakarmi, U., Scutari, G.: Bi-linear modeling of data manifolds for dynamic-MRI recovery. IEEE Trans. Med. Imaging 39(3), 688–702 (2019)
Venkatakrishnan, S.V., Bouman, C.A., Wohlberg, B.: Plug-and-play priors for model based reconstruction. In: IEEE Global Conference on Signal and Information Processing, pp. 945–948 (2013)
Kamilov, U.S., Mansour, H., Wohlberg, B.: A plug-and-play priors approach for solving nonlinear imaging inverse problems. IEEE Signal Process. Lett. 24(12), 1872–1876 (2017)
Eksioglu, E.M., Tanc, A.K.: Denoising AMP for MRI reconstruction: BM3D-AMP-MRI. SIAM J. Imaging Sci. 11(3), 2090–2109 (2018)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
Song, C., Yoon, S., Pavlovic, V.: Fast admm algorithm for distributed optimization with adaptive penalty. arXiv preprint arXiv:1506.08928 (2015)
Salim, A., Korba, A. , Luise, G.: The wasserstein proximal gradient algorithm (2020). https://arxiv.org/abs/2002.03035v3
Coll, B.: A review of image denoising algorithms, with a new one. Multiscale Model Simul. 4(2), 490–530 (2005)
Dabov, K., Foi, A., Katkovnik, V., Karen, E.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Huang, J., Zhang, S., Li, H., Metaxas, D.: Composite splitting algorithms for convex optimization. Comput. Vis. Image Underst. 115(12), 1610–1622 (2011)
Ahmad, R., Bouman, C.A., Buzzard, G.T., Chan, S., Schniter, P.: Plug-and-play methods for magnetic resonance imaging: using denoisers for image recovery. IEEE Signal Process. Mag. 37(1), 105–116 (2020)
Zhu, Q.Y., Wang, W., Cheng, J., Peng, X.: Incorporating reference guided priors into calibrationless parallel imaging reconstruction. Magn. Reson. Imaging 57, 347–358 (2019)
Lustig, M., Donoho, D.L., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58(6), 1182–1195 (2007)
Maggioni, M., Katkovnik, V., Egiazarian, K., Foi, A.: Nonlocal transform-domain filter for volumetric data denoising and reconstruction. IEEE Trans. Image Process. 22(1), 119–133 (2013)
Ahmad, R., Hui, X., Giri, S., Yu, D., Simonetti, O.P.: Variable density incoherent spatiotemporal acquisition (VISTA) for highly accelerated cardiac MRI. Magn. Reson. Med. 74(5), 1266–1278 (2015)
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Zhu, Q., Liang, D. (2021). Using Spatio-Temporal Correlation Based Hybrid Plug-and-Play Priors (SEABUS) for Accelerated Dynamic Cardiac Cine MRI. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_46
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