Point-Spread-Function-Aware Slice-to-Volume Registration: Application to Upper Abdominal MRI Super-Resolution

  • Michael Ebner
  • Manil Chouhan
  • Premal A. Patel
  • David Atkinson
  • Zahir Amin
  • Samantha Read
  • Shonit Punwani
  • Stuart Taylor
  • Tom Vercauteren
  • Sébastien Ourselin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10129)

Abstract

MR image acquisition of moving organs remains challenging despite the advances in ultra-fast 2D MRI sequences. Post-acquisition techniques have been proposed to increase spatial resolution a posteriori by combining acquired orthogonal stacks into a single, high-resolution (HR) volume. Current super-resolution techniques classically rely on a two-step procedure. The volumetric reconstruction step leverages a physical slice acquisition model. However, the motion correction step typically neglects the point spread function (PSF) information. In this paper, we propose a PSF-aware slice-to-volume registration approach and, for the first time, demonstrate the potential benefit of Super-Resolution for upper abdominal imaging. Our novel reconstruction pipeline takes advantage of different MR acquisitions clinically used in routine MR cholangio-pancreatography studies to guide the registration. On evaluation of clinically relevant image information, our approach outperforms state-of-the-art reconstruction toolkits in terms of visual clarity and preservation of raw data information. Overall, we achieve promising results towards replacing currently required CT scans.

Keywords

Super-resolution reconstruction Point spread function Registration Scattered data approximation MRCP study 

References

  1. 1.
    Barish, M.A., Yucel, E.K., Ferrucci, J.T.: Magnetic resonance cholangiopancreatography. N. Engl. J. Med. 341(4), 258–264 (1999)CrossRefGoogle Scholar
  2. 2.
    Cardoso, M.J., Modat, M., Vercauteren, T., Ourselin, S.: Scale factor point spread function matching: beyond aliasing in image resampling. In: Navab, N., et al. (eds.) MICCAI 2015. LNCS, vol. 9350, pp. 675–683. Springer, Cham (2015)CrossRefGoogle Scholar
  3. 3.
    Chacko, N., Chan, K.G., Liebling, M.: Intensity-based point-spread-function-aware registration for multi-view applications in optical microscopy. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp. 306–309. IEEE (2015)Google Scholar
  4. 4.
    Diamond, S., Boyd, S.: Convex optimization with abstract linear operators. In: IEEE International Conference on Computer Vision (ICCV), pp. 675–683, no. 1. IEEE (2015)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    Jiang, S., Xue, H., Glover, A., Rutherford, M., Rueckert, D., Hajnal, J.V.: MRI of moving subjects using multislice snapshot images with volume reconstruction (SVR): application to fetal, neonatal, and adult brain studies. IEEE Trans. Med. Imaging 26(7), 967–980 (2007)CrossRefGoogle Scholar
  7. 7.
    Kainz, B., Steinberger, M., Wein, W., Kuklisova-Murgasova, M., Malamateniou, C., Keraudren, K., Torsney-Weir, T., Rutherford, M., Aljabar, P., Hajnal, J.V., Rueckert, D.: Fast volume reconstruction from motion corrupted stacks of 2D slices. IEEE Trans. Med. Imaging 34(9), 1901–1913 (2015)CrossRefGoogle Scholar
  8. 8.
    McClelland, J.R., Hawkes, D.J., Schaeffter, T., King, A.P.: Respiratory motion models: a review. Med. Image Anal. 17(1), 19–42 (2013)CrossRefGoogle Scholar
  9. 9.
    Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Comput. Methods Program. Biomed. 98(3), 278–284 (2010)CrossRefGoogle Scholar
  10. 10.
    Rousseau, F., Glenn, O.A., Iordanova, B., Rodriguez-Carranza, C., Vigneron, D.B., Barkovich, J.A., Studholme, C.: Registration-based approach for reconstruction of high-resolution in utero fetal MR brain images. Acad. Radiol. 13(9), 1072–1081 (2006)CrossRefGoogle Scholar
  11. 11.
    Rousseau, F., Oubel, E., Pontabry, J., Schweitzer, M., Studholme, C., Koob, M., Dietemann, J.L.: BTK: an open-source toolkit for fetal brain MR image processing. Comput. Methods Program. Biomed. 109(1), 65–73 (2013)CrossRefGoogle Scholar
  12. 12.
    Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)CrossRefGoogle Scholar
  13. 13.
    Van Reeth, E., Tan, C.H., Tham, I.W., Poh, C.L.: Isotropic reconstruction of a 4-D MRI thoracic sequence using super-resolution. Magn. Reson. Med. 73(2), 784–793 (2015)CrossRefGoogle Scholar
  14. 14.
    Vercauteren, T., Perchant, A., Malandain, G., Pennec, X., Ayache, N.: Robust mosaicing with correction of motion distortions and tissue deformations for in vivo fibered microscopy. Med. Image Anal. 10(5), 673–692 (2006)CrossRefGoogle Scholar
  15. 15.
    Woo, J., Murano, E.Z., Stone, M., Prince, J.L.: Reconstruction of high-resolution tongue volumes from MRI. IEEE Trans. Biomed. Eng. 59(12), 3511–3524 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Michael Ebner
    • 1
  • Manil Chouhan
    • 2
    • 3
  • Premal A. Patel
    • 1
    • 4
  • David Atkinson
    • 2
  • Zahir Amin
    • 3
  • Samantha Read
    • 3
  • Shonit Punwani
    • 2
    • 3
  • Stuart Taylor
    • 2
    • 3
  • Tom Vercauteren
    • 1
  • Sébastien Ourselin
    • 1
  1. 1.Translational Imaging Group, CMICUniversity College LondonLondonUK
  2. 2.Centre for Medical ImagingUniversity College LondonLondonUK
  3. 3.Radiology DepartmentUniversity College London Hospitals NHS Foundation TrustLondonUK
  4. 4.Radiology DepartmentGreat Ormond Street Hospital for Children NHS Foundation TrustLondonUK

Personalised recommendations