Abstract
In this work, we proposed a novel image-based MRI super-resolution reconstruction (SRR) approach based on anisotropic acquisition schemes. We achieved superior reconstruction to state-of-the-art work by introducing a new multi-scale gradient field prior that guides the reconstruction of the high-resolution (HR) image. The prior improves both spatial smoothness and edge preservation. The inverse of the forward model of image formation is used to propagate the gradient guidance from the low-resolution (LR) images to the HR image space. The gradient fields over multiple scales were exploited for more accurate edge localization in the reconstruction. The proposed SRR allows inter-volume motion during the MRI scans and can incorporate with the LR images with arbitrary orientations and displacements in the frequency space, such as orthogonal and origin-shifted scans. The proposed approach was evaluated on the synthetic data as well as the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 14 subjects. The evaluation results demonstrate that our proposed prior leads to improved SRR as compared to state-of-the-art priors, and that the proposed SRR obtains better results at lower or the same cost in scan time than direct HR acquisition. In particular, the anatomical structures of hippocampus can be clearly shown in our reconstructed images. This is a significant improvement for the in vivo studies of the hippocampus.
This work was supported in part by the National Institutes of Health (NIH) under grants R01 NS079788, R01 EB019483, R01 DK100404, R01 EB018988, R01 NS106030, R44 MH086984, IDDRC U54 HD090255, and by a research grant from the Boston Children’s Hospital Translational Research Program.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brown, R.W., Cheng, Y.C.N., Haacke, E.M., Thompson, M.R., Venkatesan, R.: Magnetic Resonance Imaging: Physical Principles and Sequence Design, 2nd edn. Wiley, New York (2014)
Farsiu, S., Robinson, M.D., Elad, M., Milanfar, P.: Fast and robust multiframe super resolution. IEEE Trans. Image Process. 13(10), 1327–1344 (2004)
Fiat, D.: Method of enhancing an MRI signal. US Patent 6,294,914 (2001)
Gholipour, A., Estroff, J.A., Warfield, S.K.: Robust super-resolution volume reconstruction from slice acquisitions: application to fetal brain MRI. IEEE Trans. Med. Imaging 29(10), 1739–1758 (2010)
Gholipour, A., et al.: Super-resolution reconstruction in frequency, image, and wavelet domains to reduce through-plane partial voluming in MRI. Med. Phys. 42(12), 6919–6932 (2015)
Gudbjartsson, H.P.S.: The rician distribution of noisy MRI data. Magn. Reson. Med. 34, 910–914 (1995)
Lüsebrink, F., Sciarra, A., Mattern, H., Yakupov, R., Speck, O.: T1-weighted in vivo human whole brain MRI dataset with an ultrahigh isotropic resolution of 250 \(\mu \)m. Sci. Data 4, 170032 (2017)
Pipe, J.: Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging. Magn. Reson. Med. 42, 963–969 (1999)
Plenge, E., et al.: Super-resolution methods in MRI: can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn. Reson. Med. 68, 1983–1993 (2012)
Poot, D.H.J., Van Meir, V., Sijbers, J.: General and efficient super-resolution method for multi-slice MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 615–622. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15705-9_75
Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42, 952–962 (1999)
Rousseau, F., Kim, K., Studholme, C., Koob, M., Dietemann, J.-L.: On super-resolution for fetal brain MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 355–362. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15745-5_44
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(7), 1465–1476 (2012)
Shilling, R.Z., Robbie, T.Q., Bailloeul, T., Mewes, K., Mersereau, R.M., Brummer, M.E.: A super-resolution framework for 3-D high-resolution and high-contrast imaging using 2-D multislice MRI. IEEE Trans. Med. Imag. 28(5), 633–644 (2009)
Sun, J., Sun, J., Xu, Z., Shum, H.Y.: Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans. Image Process. 20(6), 1529–1542 (2011)
Tourbier, S., Bresson, X., Hagmann, P., Thiran, J., Meuli, R., Cuadra, M.: An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization. Neuroimage 118, 584–597 (2015)
Tsai, RY, H.T.: Multi-frame image restoration and registration. In: Advances in Computer Vision and Image Processing (1984)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13(1), 600–612 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Sui, Y., Afacan, O., Gholipour, A., Warfield, S.K. (2019). Isotropic MRI Super-Resolution Reconstruction with Multi-scale Gradient Field Prior. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-32248-9_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32247-2
Online ISBN: 978-3-030-32248-9
eBook Packages: Computer ScienceComputer Science (R0)