Abstract
Diffusion magnetic resonance imaging (dMRI) can provide quantitative information with which to visualize and study connectivity and continuity of neural pathways in nervous systems. However, the very subtle regions and multiple intra-voxel orientations of water diffusion in brain cannnot accurately be represented in low spatial resolution imaging with tensor model. Yet, the ability to trace and describe such regions is critical for some applications such as neurosurgery and pathologic diagnosis. In this paper, we proposed a new single image acquisition super-resolution method to increase both the spatial and angular resolution of dMRI. The proposed approach called single dMRI super-resolution reconstruction with compressed sensing (SSR-CS), uses a low number of single diffusion MRI in different gradients. This acquisition scheme is effectively in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method combines the two strategies of nonlocal similarity reconstruction and compressed sensing reconstruction in a sparse basis of spherical ridgelets to reconstruct high resolution image in k-space with complex orientations. The split Bregman approach is introduced for solving the SSR-CS problem. The performance of the proposed method is quantitatively evaluated on simulated diffusion MRI, using both spatial and angular reconstruction evaluating indexes. We also compared our method with some other dMRI super resolution methods.
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References
Stieltjes, B., Brunner, R.M., Fritzsche, K., Laun, F.: Diffusion Tensor Imaging. Springer, Heidelberg (2013)
Eriksson, S.H., Rugg-Gunn, F.J.: Diffusion tensor imaging in patients with epilepsy and malformations of cortical development. Brain 124, 617–626 (2001)
Mori, S., van Zijl, P.: Fiber tracking: principles and strategies-a technical review. NMR Biomed. 15, 468–480 (2002)
Coup, P., Manjn, J.V.: Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage 83, 245–261 (2013)
Heidemann, R.M., Anwander, A.: k-space and q-space: combining ultra-high spatial and angular resolution in diffusion imaging using ZOOPPA at 7 T. NeuroImage 60(2), 967–978 (2012)
Landman, B.A.: Resolution of crossing fibers with constrained compressed sensing using traditional diffusion tensor MRI. NeuroImage 59(3), 2175–2186 (2012)
Scherrer, B., Gholipour, A., Warfield, S.K.: Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med. Image Anal. 16, 1465–1476 (2012)
Poot, D.H.J., Jeurissen, B.: Super-resolution for multislice diffusion tensor imaging. Magn. Reson. Med. 69, 103–113 (2013)
Van Steenkiste, G., Jeurissen, B.: Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations. Magn. Reson. Med. 75, 181–195 (2015)
Ning, L., Setsompop, K., Michailovich, O.: A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. NeuroImage 125, 386–400 (2016)
Stejskal, E., Tanner, J.: Spin diffusion measurements: spin echoes in the presence of a time-dependent field gradient. J. Chem. Phys. 42, 288–292 (1965)
Jansons, K.M., Alexander, D.C.: Persistent angular structure: new insights from diffusion MRI data. Dummy version. In: Taylor, C., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 672–683. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45087-0_56
Michailovich, O., Rathi, Y.: On approximation of orientation distributions by means of spherical ridgelets. IEEE Trans. Image Process. 19(2), 461–477 (2010)
Michailovich, O., Rathi, Y., Dolui, S.: Spatially regularized compressed sensing for high angular resolution diffusion imaging. IEEE Trans. Med. Imaging, 30(5), 1100–1115 (2011)
Manjn, J.V., Coup, P.: Non-local MRI upsampling. Med. Image Anal. 14(6), 784–792 (2010)
Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms for l1-minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1, 143–168 (2008)
Barmpoutis, A., Jian, B., Vemuri, B.C.: Adaptive kernels for multi-fiber reconstruction. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 338–349. Springer, Heidelberg (2009). doi:10.1007/978-3-642-02498-6_28
Barmpoutis, A., Hwang, M.S.: Regularized positive-definite fourth order tensor field estimation from DW-MRI. NeuroImage 45, 153–162 (2009)
Barmpoutis, A., Vemuri, B.C.: A unified framework for estimating diffusion tensors of any order with symmetric positive-definite constraints. In: 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 1385–1388. IEEE (2010)
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Yin, S. et al. (2016). A Unified Approach for Spatial and Angular Super-Resolution of Diffusion Tensor MRI. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 663. Springer, Singapore. https://doi.org/10.1007/978-981-10-3005-5_26
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DOI: https://doi.org/10.1007/978-981-10-3005-5_26
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