Self Super-Resolution for Magnetic Resonance Images

  • Amod JogEmail author
  • Aaron Carass
  • Jerry L. Prince
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


It is faster and therefore cheaper to acquire magnetic resonance images (MRI) with higher in-plane resolution than through-plane resolution. The low resolution of such acquisitions can be increased using post-processing techniques referred to as super-resolution (SR) algorithms. SR is known to be an ill-posed problem. Most state-of-the-art SR algorithms rely on the presence of external/training data to learn a transform that converts low resolution input to a higher resolution output. In this paper an SR approach is presented that is not dependent on any external training data and is only reliant on the acquired image. Patches extracted from the acquired image are used to estimate a set of new images, where each image has increased resolution along a particular direction. The final SR image is estimated by combining images in this set via the technique of Fourier Burst Accumulation. Our approach was validated on simulated low resolution MRI images, and showed significant improvement in image quality and segmentation accuracy when compared to competing SR methods. SR of FLuid Attenuated Inversion Recovery (FLAIR) images with lesions is also demonstrated.


Super-resolution MRI Self-generated training data 


  1. 1.
    Ballester, M.A.G., et al.: Estimation of the partial volume effect in MRI. Med. Image Anal. 6(4), 389–405 (2002)CrossRefGoogle Scholar
  2. 2.
    Delbracio, M., Sapiro, G.: Removing camera shake via weighted fourier burst accumulation. IEEE Trans. Image Proc. 24(11), 3293–3307 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Freeman, W.T., et al.: Example-based super-resolution. IEEE Comput. Graph. Appl. 22(2), 56–65 (2002)CrossRefGoogle Scholar
  4. 4.
    Huang, J.B., et al.: Single image super-resolution from transformed self-exemplars. In: IEEE Conference on Computer Vision and Pattern Recognition) (2015)Google Scholar
  5. 5.
    Konukoglu, E., van der Kouwe, A., Sabuncu, M.R., Fischl, B.: Example-based restoration of high-resolution magnetic resonance image acquisitions. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 131–138. Springer, Heidelberg (2013)Google Scholar
  6. 6.
    Manjón, J.V., et al.: MRI superresolution using self-similarity and image priors. Int. J. Biomed. Imaging 425891, 11 (2010)Google Scholar
  7. 7.
    Manjón, J.V., et al.: Non-local MRI upsampling. Med. Image Anal. 14(6), 784–792 (2010)CrossRefGoogle Scholar
  8. 8.
    Rousseau, F.: Brain hallucination. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 497–508. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Rueda, A., et al.: Single-image super-resolution of brain MR images using overcomplete dictionaries. Med. Image Anal. 17(1), 113–132 (2013)CrossRefGoogle Scholar
  10. 10.
    Shiee, N., et al.: A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions. Neuroimage 49(2), 1524–1535 (2010)CrossRefGoogle Scholar
  11. 11.
    Timofte, R., et al.: Anchored neighborhood regression for fast example-based super-resolution. ICCV 2013, 1920–1927 (2013)Google Scholar
  12. 12.
    Van Leemput, K., et al.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. Med. Imag. 18(10), 897–908 (1999)CrossRefGoogle Scholar
  13. 13.
    Vu, C.T., et al.: S3: a spectral and spatial measure of local perceived sharpness in natural images. IEEE Trans. Image Proc. 21(3), 934–945 (2012)CrossRefGoogle Scholar
  14. 14.
    Yang, J., et al.: Image super-resolution via sparse representation. IEEE Trans. Image Proc. 19(11), 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar

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© Springer International Publishing AG 2016

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Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringBaltimoreUSA
  2. 2.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA

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