title: Simulated field maps for susceptibility artefact correction in interventional MRI.
abstract: OBJECTIVE
  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.
authors.affiliation: Centre for Medical Image Computing, University College London, London, UK, m.kochan.12@ucl.ac.uk.
authors.forename: Martin
authors.initials: M
authors.lastname: Kochan
authors.forename: Pankaj
authors.initials: P
authors.lastname: Daga
authors.forename: Ninon
authors.initials: N
authors.lastname: Burgos
authors.forename: Mark
authors.initials: M
authors.lastname: White
authors.forename: M Jorge
authors.initials: MJ
authors.lastname: Cardoso
authors.forename: Laura
authors.initials: L
authors.lastname: Mancini
authors.forename: Gavin P
authors.initials: GP
authors.lastname: Winston
authors.forename: Andrew W
authors.initials: AW
authors.lastname: McEvoy
authors.forename: John
authors.initials: J
authors.lastname: Thornton
authors.forename: Tarek
authors.initials: T
authors.lastname: Yousry
authors.forename: John S
authors.initials: JS
authors.lastname: Duncan
authors.forename: Danail
authors.initials: D
authors.lastname: Stoyanov
authors.forename: Sébastien
authors.initials: S
authors.lastname: Ourselin
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001331:Automation; D001921:Brain / Q000473:pathology; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D004827:Epilepsy / Q000601:surgery; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D053783:Magnetic Resonance Imaging, Interventional / Q000379:methods*; D008280:Magnetics; D015233:Models, Statistical; D064847:Multimodal Imaging / Q000379:methods; D009493:Neurosurgery / Q000379:methods; D015203:Reproducibility of Results; D025321:Surgery, Computer-Assisted; D013702:Temporal Lobe / Q000473:pathology / Q000601:surgery*; D014025:Titanium / Q000737:chemistry; D014057:Tomography, X-Ray Computed
chemical_list: D014025:Titanium