Simulated field maps for susceptibility artefact correction in interventional MRI
- 251 Downloads
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.
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.
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.
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.
KeywordsImage-guided neurosurgery Interventional MRI Magnetic field inhomogeneity Tissue segmentation Field map simulation
Conflict of interest
The authors declare that they have no conflict of interest.
- 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 147–154Google Scholar
- 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):2332–2341Google Scholar
- 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 441–449Google 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):1132–1142Google Scholar
- 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):882–891CrossRefPubMedGoogle 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 226–235Google 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 951–959Google Scholar
- 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–S219CrossRefPubMedGoogle 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):311–318Google Scholar