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Daga P, Pendse T, Modat M, White M, Mancini L, Winston GP, McEvoy AW, Thornton J, Yousry T, Drobnjak I, Duncan JS, Ourselin S. Susceptibility artefact correction using dynamic graph cuts: application to neurosurgery. Med Image Anal 2014; 18:1132-42. [PMID: 25047865 PMCID: PMC6742505 DOI: 10.1016/j.media.2014.06.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2013] [Revised: 04/18/2014] [Accepted: 06/23/2014] [Indexed: 11/25/2022]
Abstract
Echo Planar Imaging (EPI) is routinely used in diffusion and functional MR imaging due to its rapid acquisition time. However, the long readout period makes it prone to susceptibility artefacts which results in geometric and intensity distortions of the acquired image. The use of these distorted images for neuronavigation hampers the effectiveness of image-guided surgery systems as critical white matter tracts and functionally eloquent brain areas cannot be accurately localised. In this paper, we present a novel method for correction of distortions arising from susceptibility artefacts in EPI images. The proposed method combines fieldmap and image registration based correction techniques in a unified framework. A phase unwrapping algorithm is presented that can efficiently compute the B0 magnetic field inhomogeneity map as well as the uncertainty associated with the estimated solution through the use of dynamic graph cuts. This information is fed to a subsequent image registration step to further refine the results in areas with high uncertainty. This work has been integrated into the surgical workflow at the National Hospital for Neurology and Neurosurgery and its effectiveness in correcting for geometric distortions due to susceptibility artefacts is demonstrated on EPI images acquired with an interventional MRI scanner during neurosurgery.
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Affiliation(s)
- Pankaj Daga
- Centre for Medical Image Computing, University College London, London, UK.
| | - Tejas Pendse
- Centre for Medical Image Computing, University College London, London, UK
| | - Marc Modat
- Centre for Medical Image Computing, University College London, London, UK
| | - Mark White
- National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Laura Mancini
- National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, UK
| | - Andrew W McEvoy
- National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - John Thornton
- National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Tarek Yousry
- National Hospital for Neurology and Neurosurgery, UCLH NHS Foundation Trust, London, UK
| | - Ivana Drobnjak
- Centre for Medical Image Computing, University College London, London, UK
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, University College London, London, UK
| | - Sebastien Ourselin
- Centre for Medical Image Computing, University College London, London, UK; Dementia Research Centre, Institute of Neurology, University College London, London, UK
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