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.
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The results reported in our original IPCAI 2014 paper showed slightly smaller displacements due to the voxel size being passed incorrectly.
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Conflict of interest
The authors declare that they have no conflict of interest.
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).
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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
- Image-guided neurosurgery
- Interventional MRI
- Magnetic field inhomogeneity
- Tissue segmentation
- Field map simulation