Simulated field maps for susceptibility artefact correction in interventional MRI | International Journal of Computer Assisted Radiology and Surgery Skip to main content
Springer Nature Link
Log in

Simulated field maps for susceptibility artefact correction in interventional MRI

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Intraoperative MRI (iMRI) is a powerful modality for acquiring images of the brain to facilitate precise image-guided neurosurgery. Diffusion-weighted MRI (DW-MRI) provides critical information about location, orientation and structure of nerve fibre tracts, but suffers from the “susceptibility artefact” stemming from magnetic field perturbations due to the step change in magnetic susceptibility at air–tissue boundaries in the head. An existing approach to correcting the artefact is to acquire a field map by means of an additional MRI scan. However, to recover true field maps from the acquired field maps near air–tissue boundaries is challenging, and acquired field maps are unavailable in historical MRI data sets. This paper reports a detailed account of our method to simulate field maps from structural MRI scans that was first presented at IPCAI 2014 and provides a thorough experimental and analysis section to quantitatively validate our technique.

Methods

We perform automatic air–tissue segmentation of intraoperative MRI scans, feed the segmentation into a field map simulation step and apply the acquired and the simulated field maps to correct DW-MRI data sets.

Results

We report results for 12 patient data sets acquired during anterior temporal lobe resection surgery for the surgical management of focal epilepsy. We find a close agreement between acquired and simulated field maps and observe a statistically significant reduction in the susceptibility artefact in DW-MRI data sets corrected using simulated field maps in the vicinity of the resection. The artefact reduction obtained using acquired field maps remains better than that using the simulated field maps in all evaluated regions of the brain.

Conclusions

The proposed simulated field maps facilitate susceptibility artefact reduction near the resection. Accurate air–tissue segmentation is key to achieving accurate simulation. The proposed simulation approach is adaptable to different iMRI and neurosurgical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Japan)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. The results reported in our original IPCAI 2014 paper showed slightly smaller displacements due to the voxel size being passed incorrectly.

    Fig. 4
    figure 4

    Scatter plot of the acquired and the simulated field map in corresponding voxels inside the brain for subject #3

    Full size image
    Fig. 5
    figure 5

    Field maps expressed as millimetres of displacement along the phase-encode direction. The view is centred at anterior temporal lobe resection cavity. The brain surface outlined using a surface extractor is shown for reference (red outline). a–c A phase-wrapped acquired field map for subject #3, showing a step change in phase value close to the resection margin. d–f The acquired field map after phase-unwrapping; only the volume inside the brain mask is shown, because the phase-unwrapping was restricted to the brain only. g–i The proposed simulated field map. j–l A voxel-wise absolute difference between the simulated and the phase-unwrapped acquired field maps, only considered within the brain. Left to right: coronal (a, d, g, j), sagittal (b, e, h, k) and axial sections (c, f, i, l), respectively. Slice orientations are close to the standard anatomical planes. We used a brain surface extractor included in NiftyView (http://cmic.cs.ucl.ac.uk/home/software)

    Full size image
    Fig. 6
    figure 6

    Several axial slices through absolute difference between the simulated and phase-unwrapped acquired field maps, expressed as millimetres of displacement along the phase-encode direction shown for subject #3. a Contralateral temporal lobe level. b Eye level. c Superior frontal and parietal lobe level

    Full size image

References

  1. Andersson JL, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2):870–888

    Article  PubMed  Google Scholar 

  2. Burgos N, Cardoso MJ, Modat M, Pedemonte S, Dickson J, Barnes A, Duncan JS, Atkinson D, Arridge SR, Hutton BF, Ourselin S (2013) Attenuation correction synthesis for hybrid PET-MR scanners. In: Medical image computing and computer-assisted intervention—(MICCAI 2013). Springer, Berlin, pp 147–154

  3. Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, Barnes A, Ahmed R, Mahoney CJ, Schott JM, Duncan JS, Atkinson D, Arridge SR, Hutton BF, Ourselin S (2014) Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging 33(12):2332–2341

  4. Cardoso MJ, Clarkson MJ, Ridgway GR, Modat M, Fox NC, Ourselin S (2009) Improved maximum a posteriori cortical segmentation by iterative relaxation of priors. In: Medical image computing and computer-assisted intervention—(MICCAI 2009). Springer, Berlin, pp 441–449

  5. Chen X, Weigel D, Ganslandt O, Buchfelder M, Nimsky C (2009) Prediction of visual field deficits by diffusion tensor imaging in temporal lobe epilepsy surgery. Neuroimage 45(2):286–297

    Article  PubMed  Google Scholar 

  6. Clare S, Evans J, Jezzard P (2006) Requirements for room temperature shimming of the human brain. Magn Reson Med 55(1):210–214

    Article  PubMed  Google Scholar 

  7. Daga P, Pendse T, Modat M, White M, Mancini L, Winston G, McEvoy AW, Thornton J, Yousry T, Drobnjak I, Duncan JS, Ourselin S (2014) Susceptibility artefact correction using dynamic graph cuts: application to neurosurgery. Med Image Anal 18(7):1132–1142

  8. Daga P, Winston G, Modat M, White M, Mancini L, Cardoso MJ, Symms M, Stretton J, McEvoy AW, Thornton J, Micallef C, Yousry T, Hawkes DJ, Duncan JS, Ourselin S (2012) Accurate localization of optic radiation during neurosurgery in an interventional MRI suite. IEEE Trans Med Imaging 31(4):882–891

    Article  PubMed  Google Scholar 

  9. Gasser T, Ganslandt O, Sandalcioglu E, Stolke D, Fahlbusch R, Nimsky C (2005) Intraoperative functional MRI: implementation and preliminary experience. Neuroimage 26(3):685–693

    Article  PubMed  Google Scholar 

  10. Gruetter R, Boesch C (1992) Fast, noniterative shimming of spatially localized signals. In vivo analysis of the magnetic field along axes. J Magn Reson (1969) 96(2):323–334

    Article  CAS  Google Scholar 

  11. Hutton C, Bork A, Josephs O, Deichmann R, Ashburner J, Turner R (2002) Image distortion correction in fMRI: a quantitative evaluation. Neuroimage 16(1):217–240

    Article  PubMed  Google Scholar 

  12. Jenkinson M (2003) Fast, automated, n-dimensional phase-unwrapping algorithm. Magn Reson Med 49(1):193–197

    Article  PubMed  Google Scholar 

  13. Jenkinson M, Wilson JL, Jezzard P (2004) Perturbation method for magnetic field calculations of nonconductive objects. Magn Reson Med 52(3):471–477

    Article  PubMed  Google Scholar 

  14. Jezzard P, Balaban RS (1995) Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med 34(1):65–73

    Article  CAS  PubMed  Google Scholar 

  15. Kim DJ, Park HJ, Kang KW, Shin YW, Kim JJ, Moon WJ, Chung EC, Kim IY, Kwon JS, Kim SI (2006) How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study. Magn Reson Imaging 24(10):1369–1376

    Article  PubMed  Google Scholar 

  16. Kochan M, Daga P, Burgos N, White M, Cardoso MJ, Mancini L, Winston GP, McEvoy AW, Thornton J, Yousry T, Duncan JS, Stoyanov D, Ourselin S (2014) Simulated field maps: toward improved susceptibility artefact correction in interventional MRI. In: Information processing in computer-assisted interventions. Springer, Berlin, pp 226–235

  17. Modat M, Ridgway GR, Taylor ZA, Lehmann M, Barnes J, Hawkes DJ, Fox NC, Ourselin S (2010) Fast free-form deformation using graphics processing units. Comput Methods Program Biomed 98(3):278–284

    Article  Google Scholar 

  18. Nilsson D, Starck G, Ljungberg M, Ribbelin S, Jönsson L, Malmgren K, Rydenhag B (2007) Intersubject variability in the anterior extent of the optic radiation assessed by tractography. Epilepsy Res 77(1):11–16

    Article  PubMed  Google Scholar 

  19. Papadakis NG, Martin KM, Mustafa MH, Wilkinson ID, Griffiths PD, Huang CLH, Woodruff PW (2002) Study of the effect of CSF suppression on white matter diffusion anisotropy mapping of healthy human brain. Magn Reson Med 48(2):394–398

    Article  PubMed  Google Scholar 

  20. Poynton C, Jenkinson M, Wells W III (2009) Atlas-based improved prediction of magnetic field inhomogeneity for distortion correction of EPI data. In: Medical image computing and computer-assisted intervention—(MICCAI 2009). Springer, Berlin, pp 951–959

  21. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazya RK, Saundersa J, Vickersa J, Zhanga Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Supplement 1):S208–S219

    Article  PubMed  Google Scholar 

  22. Wiebe S, Blume WT, Girvin JP, Eliasziw M (2001) A randomized, controlled trial of surgery for temporal-lobe epilepsy. N Engl J Med 345(5):311–318

  23. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  CAS  PubMed  Google Scholar 

Download references

Conflict of interest

The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Centre for Medical Image Computing, University College London, London, UK

    Martin Kochan, Pankaj Daga, Ninon Burgos, M. Jorge Cardoso, Danail Stoyanov & Sébastien Ourselin

  2. National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK

    Mark White, Laura Mancini, Andrew W. McEvoy, John Thornton & Tarek Yousry

  3. Dementia Research Centre, Institute of Neurology, University College London, London, UK

    M. Jorge Cardoso & Sébastien Ourselin

  4. Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, UK

    Gavin P. Winston & John S. Duncan

Authors
  1. Martin Kochan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Pankaj Daga
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Ninon Burgos
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Mark White
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. M. Jorge Cardoso
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Laura Mancini
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Gavin P. Winston
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Andrew W. McEvoy
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. John Thornton
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Tarek Yousry
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. John S. Duncan
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Danail Stoyanov
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Sébastien Ourselin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Martin Kochan.

Additional information

This work was supported by the UCL Doctoral Training Programme in Medical and Biomedical Imaging studentship funded by the EPSRC. Danail Stoyanov would like to thank for the support of The Royal Academy of Engineering/EPSRC Research Fellowship. Sébastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/J020990/1, EP/K005278), the MRC (MR/J01107X/1), the EU-FP7 project VPH-DARE@IT (FP7-ICT-2011-9-601055), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kochan, M., Daga, P., Burgos, N. et al. Simulated field maps for susceptibility artefact correction in interventional MRI. Int J CARS 10, 1405–1416 (2015). https://doi.org/10.1007/s11548-015-1253-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-015-1253-7

Keywords