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.
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.
Authors: Pankaj Daga; Gavin Winston; Marc Modat; Mark White; Laura Mancini; M Jorge Cardoso; Mark Symms; Jason Stretton; Andrew W McEvoy; John Thornton; Caroline Micallef; Tarek Yousry; David J Hawkes; John S Duncan; Sebastien Ourselin Journal: IEEE Trans Med Imaging Date: 2011-12-20 Impact factor: 10.048
Authors: Manuel Jorge Cardoso; Matthew J Clarkson; Gerard R Ridgway; Marc Modat; Nick C Fox; Sebastien Ourselin Journal: Med Image Comput Comput Assist Interv Date: 2009
Authors: Stephen M Smith; Mark Jenkinson; Mark W Woolrich; Christian F Beckmann; Timothy E J Behrens; Heidi Johansen-Berg; Peter R Bannister; Marilena De Luca; Ivana Drobnjak; David E Flitney; Rami K Niazy; James Saunders; John Vickers; Yongyue Zhang; Nicola De Stefano; J Michael Brady; Paul M Matthews Journal: Neuroimage Date: 2004 Impact factor: 6.556
Authors: Marc Modat; Gerard R Ridgway; Zeike A Taylor; Manja Lehmann; Josephine Barnes; David J Hawkes; Nick C Fox; Sébastien Ourselin Journal: Comput Methods Programs Biomed Date: 2009-10-08 Impact factor: 5.428
Authors: Xiaolei Chen; Daniel Weigel; Oliver Ganslandt; Michael Buchfelder; Christopher Nimsky Journal: Neuroimage Date: 2008-12-16 Impact factor: 6.556
Authors: Pankaj Daga; Tejas Pendse; Marc Modat; Mark White; Laura Mancini; Gavin P Winston; Andrew W McEvoy; John Thornton; Tarek Yousry; Ivana Drobnjak; John S Duncan; Sebastien Ourselin Journal: Med Image Anal Date: 2014-07-05 Impact factor: 8.545