Abstract
Multi-sequence MRI protocols are used in comprehensive examinations of various pathologies in both clinical diagnosis and medical research. Various MRI techniques provide complementary information about living tissue. However, a comprehensive examination covering all modalities is rarely achieved due to considerations of cost, patient comfort, and scanner time availability. This may lead to incomplete records owing to image artifacts or corrupted or lost data. In this paper, we explore the problem of synthesizing images for one MRI modality from an image of another MRI modality of the same subject using a novel geometry regularized joint dictionary learning framework for non-local patch reconstruction. Firstly, we learn a cross-modality joint dictionary from a multi-modality image database. Training image pairs are first co-registered. A cross-modality dictionary pair is then jointly learned by minimizing the cross-modality divergence via a Maximum Mean Discrepancy term in the objective function of the learning scheme. This guarantees that the distribution of both image modalities is taken jointly into account when building the resulting sparse representation. In addition, in order to preserve intrinsic geometrical structure of the synthesized image patches, we further introduced a graph Laplacian regularization term into the objective function. Finally, we present a patch-based non-local reconstruction scheme, providing further fidelity of the synthesized images. Experimental results demonstrate that our method achieves significant performance gains over previously published techniques.
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Notes
- 1.
Here, we use the word modality in the sense of a specific kind of MRI sequence. Note that the proposed technique would equally be applicable when the protocol involves different imaging modalities in a more classical sense (e.g. MRI, CT, US, SPECT, and PET).
References
Roy, S., Carass, A., Prince, J.L.: Magnetic resonance image example-based contrast synthesis. IEEE TMI 32(12), 2348–2363 (2013)
Shao, L., Zhu, F., Li, X.: Transfer learning for visual categorization: a survey. IEEE TNNLS 26(5), 1019–1034 (2015)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE TKDE 26(5), 1345–1359 (2010)
Davis, G., Mallat, S., Avellaneda, M.: Adaptive greedy approximations. Constr. Approx. 13(1), 57–98 (1997)
Chen, S.S., Donoho, L.D., Saunders, A.M.: Atomic decomposition by basis pursuit. SIAM Rev. 43(1), 129–159 (2001)
Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE TIP 19(11), 2861–2873 (2010)
Gretton, A., Borgwardt, K., Rasch, M.J., Scholkopf, B., Smola, A.J.: A kernel two-sample test. JMLR 13, 723–773 (2012)
Long, M., Wang, J., Ding, G., Shen, D., Yang, Q.: Transfer learning with graph co-regularization. IEEE TKDE 26(7), 1805–1818 (2014)
Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. JMLR 2, 67–93 (2002)
Zheng, M., Bu, J., Chen, C., Wang, C., Zhang, L., Qiu, G., Cai, D.: Graph regularized sparse coding for image representation. IEEE TIP 20(5), 1327–1336 (2011)
Buades, A., Coll, B., Morel, J.M.: Image denoising methods. A new nonlocal principle. SIAM Rev. 52(1), 113–147 (2010)
Rowland, A.L., Burns, M., Hartkens, T., Hajnal, J. V., Rueckert, D., Hill, D.L.G.: Information eXtraction from Images (IXI). In: DiDaMIC, pp. 55–6 (2004)
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Huang, Y., Beltrachini, L., Shao, L., Frangi, A.F. (2016). Geometry Regularized Joint Dictionary Learning for Cross-Modality Image Synthesis in Magnetic Resonance Imaging. In: Tsaftaris, S., Gooya, A., Frangi, A., Prince, J. (eds) Simulation and Synthesis in Medical Imaging. SASHIMI 2016. Lecture Notes in Computer Science(), vol 9968. Springer, Cham. https://doi.org/10.1007/978-3-319-46630-9_12
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DOI: https://doi.org/10.1007/978-3-319-46630-9_12
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