Abstract
Diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the microstructure of the neuronal tissue. The NODDI model has been a popular approach to the estimation of tissue microstructure in many neuroscience studies. It represents the diffusion signals with three types of diffusion in tissue: intra-cellular, extra-cellular, and cerebrospinal fluid compartments. However, the original NODDI method uses a computationally expensive procedure to fit the model and could require a large number of diffusion gradients for accurate microstructure estimation, which may be impractical for clinical use. Therefore, efforts have been devoted to efficient and accurate NODDI microstructure estimation with a reduced number of diffusion gradients. In this work, we propose a deep network based approach to the NODDI microstructure estimation, which is named Microstructure Estimation using a Deep Network (MEDN). Motivated by the AMICO algorithm which accelerates the computation of NODDI parameters, we formulate the microstructure estimation problem in a dictionary-based framework. The proposed network comprises two cascaded stages. The first stage resembles the solution to a dictionary-based sparse reconstruction problem and the second stage computes the final microstructure using the output of the first stage. The weights in the two stages are jointly learned from training data, which is obtained from training dMRI scans with diffusion gradients that densely sample the q-space. The proposed method was applied to brain dMRI scans, where two shells each with 30 gradient directions (60 diffusion gradients in total) were used. Estimation accuracy with respect to the gold standard was measured and the results demonstrate that MEDN outperforms the competing algorithms.
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References
Alexander, D.C., Hubbard, P.L., Hall, M.G., Moore, E.A., Ptito, M., Parker, G.J., Dyrby, T.B.: Orientationally invariant indices of axon diameter and density from diffusion MRI. NeuroImage 52(4), 1374–1389 (2010)
Assaf, Y., Basser, P.J.: Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. NeuroImage 27(1), 48–58 (2005)
Auría, A., Romascano, D.P.R., Canales-Rodriguez, E., Wiaux, Y., Dirby, T.B., Alexander, D., Thiran, J.P., Daducci, A.: Accelerated microstructure imaging via convex optimisation for regions with multiple fibres (AMICOx). In: IEEE International Conference on Image Processing 2015, pp. 1673–1676. IEEE (2015)
Blumensath, T., Davies, M.E.: Iterative thresholding for sparse approximations. J. Fourier Anal. Appl. 14(5–6), 629–654 (2008)
Daducci, A., Canales-Rodríguez, E.J., Zhang, H., Dyrby, T.B., Alexander, D.C., Thiran, J.P.: Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data. NeuroImage 105, 32–44 (2015)
Golkov, V., Dosovitskiy, A., Sperl, J.I., Menzel, M.I., Czisch, M., Sämann, P., Brox, T., Cremers, D.: q-space deep learning: twelve-fold shorter and model-free diffusion MRI scans. IEEE Trans. Med. Imaging 35(5), 1344–1351 (2016)
Gregor, K., LeCun, Y.: Learning fast approximations of sparse coding. In: International Conference on Machine Learning, pp. 399–406 (2010)
Johansen-Berg, H., Behrens, T.E.J.: Diffusion MRI: From Quantitative Measurement to in Vivo Neuroanatomy. Academic Press, Waltham (2013)
Kamagata, K., Hatano, T., Okuzumi, A., Motoi, Y., Abe, O., Shimoji, K., Kamiya, K., Suzuki, M., Hori, M., Kumamaru, K.K., Hattori, N., Aoki, S.: Neurite orientation dispersion and density imaging in the substantia nigra in idiopathic Parkinson disease. Eur. Radiol. 26(8), 2567–2577 (2016)
Kelly, C.E., Thompson, D.K., Chen, J., Leemans, A., Adamson, C.L., Inder, T.E., Cheong, J.L., Doyle, L.W., Anderson, P.J.: Axon density and axon orientation dispersion in children born preterm. Hum. Brain Mapp. 37(9), 3080–3102 (2016)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Konda, K., Memisevic, R., Krueger, D.: Zero-bias autoencoders and the benefits of co-adapting features. arXiv preprint arXiv:1402.3337 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Landman, B.A., Bogovic, J.A., Wan, H., ElShahaby, F.E.Z., Bazin, P.L., Prince, J.L.: Resolution of crossing fibers with constrained compressed sensing using diffusion tensor MRI. NeuroImage 59(3), 2175–2186 (2012)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of the 27th International Conference on Machine Learning, pp. 807–814 (2010)
Sprechmann, P., Bronstein, A.M., Sapiro, G.: Learning efficient sparse and low rank models. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1821–1833 (2015)
Tariq, M., Schneider, T., Alexander, D.C., Wheeler-Kingshott, C.A.G., Zhang, H.: Bingham-NODDI: mapping anisotropic orientation dispersion of neurites using diffusion MRI. NeuroImage 133, 207–223 (2016)
Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K.: The WU-Minn human connectome project: an overview. NeuroImage 80, 62–79 (2013)
Wang, Z., Ling, Q., Huang, T.S.: Learning deep \(\ell _0\) encoders. In: AAAI Conference on Artificial Intelligence, pp. 2194–2200 (2016)
Xin, B., Wang, Y., Gao, W., Wipf, D.: Maximal sparsity with deep networks? arXiv preprint arXiv:1605.01636 (2016)
Ye, C., Zhuo, J., Gullapalli, R.P., Prince, J.L.: Estimation of fiber orientations using neighborhood information. Med. Image Anal. 32, 243–256 (2016)
Zhang, H., Schneider, T., Wheeler-Kingshott, C.A., Alexander, D.C.: NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61(4), 1000–1016 (2012)
Acknowledgement
This work is supported by NSFC 61601461. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).
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Ye, C. (2017). Estimation of Tissue Microstructure Using a Deep Network Inspired by a Sparse Reconstruction Framework. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_37
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