Abstract
Dynamic perfusion magnetic resonance (MR) imaging is a commonly used imaging technique that allows to measure the tissue perfusion in an organ of interest via assessment of various hemodynamic parameters such as blood flow, blood volume, and mean transit time. In this paper, we tackle the problem of recovering perfusion MR images from undersampled k-space data. We propose a novel reconstruction model that jointly penalizes spatial (local) incoherence on temporal differences obtained based on a reference image and the patch-wise (nonlocal) dissimilarities between spatio-temporal neighborhoods of MR sequence. Furthermore, we introduce an efficient iterative algorithm based on a proximal-splitting scheme that solves the joint minimization problem with fast convergence. We evaluate our method on dynamic susceptibility contrast (DSC)-MRI brain perfusion datasets as well as on a publicly available dataset of in-vivo breath-hold cardiac perfusion. Our proposed method demonstrates superior reconstruction performance over the state-of-the-art methods and yields highly accurate estimation of perfusion time profiles, which is very essential for the precise quantification of clinically relevant perfusion parameters.
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Notes
- 1.
Available at: http://web.engr.illinois.edu/~cchen156/SSMRI.html.
References
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imag. Sci. 2(1), 183–202 (2009)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 60–65 (2005)
Caballero, J., Price, A.N., Rueckert, D., Hajnal, J.: Dictionary learning and time sparsity for dynamic MR data reconstruction. IEEE Trans. Med. Imag. 33(4), 979–994 (2014)
Chen, C., Li, Y., Axel, L., Huang, J.: Real time dynamic MRI with dynamic total variation. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8673, pp. 138–145. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10404-1_18
Chen, C., et al.: Preconditioning for accelerated iteratively reweighted least squares in structured sparsity reconstruction. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2713–2720 (2014)
Conturo, T.E., et al.: Arterial input functions for dynamic susceptibility contrast MRI: requirements and signal options. J. Mag. Reson. Imag. 22, 697–703 (2005)
Coupé, P., Yger, P., Prima, S., Hellier, P., Kervrann, C., Barillot, C.: An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans. Med. Imag. 27(4), 425–441 (2008)
Deshmane, A., Gulani, V., Griswold, M.A., Seiberlich, N.: Parallel MR imaging. J. Mag. Reson. Imag. 36(1), 55–72 (2012)
Huang, J., Zhang, S., Metaxas, D.N.: Efficient MR image reconstruction for compressed MR imaging. Med. Imag. Anal. 15(5), 670–679 (2011)
Lingala, S.G., Hu, Y., DiBella, E., Jacob, M.: Accelerated dynamic MRI exploiting sparsity and low-rank structure: k-t SLR. IEEE Trans. Med. Imag. 30(5), 1042–1054 (2011)
Marks, R.J.: Alternating projections onto convex sets. In: Jansson, P.A. (ed.) Deconvolution of Images and Spectra, 2nd edn. Academic Press, Orlando (1996)
Protter, M., Elad, M., Takeda, H., Milanfar, P.: Generalizing the nonlocal-means to super-resolution reconstruction. IEEE Trans. Imag. Proc. 18(1), 36–51 (2009)
Raguet, H., Fadili, J., Peyré, G.: A generalized forward-backward splitting. SIAM J. Imag. Sci. 6(3), 1199–1226 (2013)
Trémoulhéac, B., Dikaios, N., Atkinson, D., Arridge, S.R.: Dynamic MR image reconstruction-separation from undersampled (k-t)-space via low-rank plus sparse prior. IEEE Trans. Med. Imag. 33(8), 1689–1701 (2014)
Ulas, C., Gómez, P., Sperl, J.I., Preibisch, C., Menze, B.H.: Spatio-temporal MRI reconstruction by enforcing local and global regularity via dynamic total variation and nuclear norm minimization. In: IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 306–309 (2016)
Yang, Z., Jacob, M.: Nonlocal regularization of inverse problems: a unified variational framework. IEEE Trans. Imag. Proc. 22(8), 3192–3203 (2013)
Yao, J., Xu, Z., Huang, X., Huang, J.: Accelerated dynamic MRI reconstruction with total variation and nuclear norm regularization. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 635–642. Springer, Heidelberg (2015). doi:10.1007/978-3-319-24571-3_76
Acknowledegments
The research leading to these results has received funding from the European Union’s H2020 Framework Programme (H2020-MSCA-ITN-2014) under grant agreement no 642685 MacSeNet. We also thank Dr. Christine Preibisch (Neuroradiology, Klinikum rechts der Isar der TU München) for providing brain perfusion datasets.
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Ulas, C., Gómez, P.A., Krahmer, F., Sperl, J.I., Menzel, M.I., Menze, B.H. (2017). Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints. In: Zuluaga, M., Bhatia, K., Kainz, B., Moghari, M., Pace, D. (eds) Reconstruction, Segmentation, and Analysis of Medical Images. RAMBO HVSMR 2016 2016. Lecture Notes in Computer Science(), vol 10129. Springer, Cham. https://doi.org/10.1007/978-3-319-52280-7_4
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