The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
34244544
PubMed Central
PMC8270902
DOI
10.1038/s41598-021-92804-w
PII: 10.1038/s41598-021-92804-w
Knihovny.cz E-zdroje
- MeSH
- epilepsie parciální diagnostické zobrazování MeSH
- lidé MeSH
- zobrazování difuzních tenzorů metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
The effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.
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Kwan P, et al. Definition of drug resistant epilepsy: Consensus proposal by the ad hoc task force of the ILAE commission on therapeutic strategies. Epilepsia. 2010;51:1069–1077. doi: 10.1111/j.1528-1167.2009.02397.x. PubMed DOI
Leeman-Markowski B. Review of MRI-negative epilepsy. JAMA Neurol. 2016;73:1377. doi: 10.1001/jamaneurol.2016.3698. DOI
Duncan JS, Winston GP, Koepp MJ, Ourselin S. Brain imaging in the assessment for epilepsy surgery. Lancet Neurol. 2016;15:420–433. doi: 10.1016/S1474-4422(15)00383-X. PubMed DOI PMC
Tournier JD. Diffusion MRI in the brain–theory and concepts. Prog. Nucl. Magn. Reson. Spectrosc. 2019;112–113:1–16. doi: 10.1016/j.pnmrs.2019.03.001. PubMed DOI
Chapman K, et al. Seizure outcome after epilepsy surgery in patients with normal preoperative MRI. J. Neurol. Neurosurg. Psychiatry. 2005;76:710–713. doi: 10.1136/jnnp.2003.026757. PubMed DOI PMC
Kabat J, Król P. Focal cortical dysplasia–review. Pol. J. Radiol. 2012;77:35–43. doi: 10.12659/PJR.882968. PubMed DOI PMC
Winston GP, et al. Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy. Epilepsy Res. 2014;108:336–339. doi: 10.1016/j.eplepsyres.2013.11.004. PubMed DOI PMC
Adler S, et al. Towards in vivo focal cortical dysplasia phenotyping using quantitative MRI. NeuroImage Clin. 2017;15:95–105. doi: 10.1016/j.nicl.2017.04.017. PubMed DOI PMC
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000–1016. doi: 10.1016/j.neuroimage.2012.03.072. PubMed DOI
Assaf Y, Basser PJ. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain. Neuroimage. 2005;27:48–58. doi: 10.1016/j.neuroimage.2005.03.042. PubMed DOI
Winston GP. The potential role of novel diffusion imaging techniques in the understanding and treatment of epilepsy. Quant. Imag. Med. Surg. 2015;5:279–27987. PubMed PMC
Kamiya K, Hori M, Aoki S. NODDI in clinical research. J. Neurosci. Methods. 2020;346:108908. doi: 10.1016/j.jneumeth.2020.108908. PubMed DOI
Van Hecke W, Emsell L, Sunaert S. Diffusion Tensor Imaging. Springer; 2016.
Bonilha L, et al. Altered microstructure in temproal lobe epilepsy: A diffusional kurtosis imaging study. Am. J. Neuroradiol. 2015;36:719–724. doi: 10.3174/ajnr.A4185. PubMed DOI PMC
Zhang Y, et al. A preliminary study of epilepsy in children using diffusional kurtosis imaging. Clin. Neuroradiol. 2013;23:293–300. doi: 10.1007/s00062-013-0212-3. PubMed DOI
Rugg-Gunn FJ, Eriksson SH, Symms MR, Barker GJ, Duncan JS. Diffusion tensor imaging of cryptogenic and acquired partial epilepsies. Brain. 2001;124:627–636. doi: 10.1093/brain/124.3.627. PubMed DOI
Muhlhofer W, Tan YL, Mueller SG, Knowlton R. MRI-negative temporal lobe epilepsy—What do we know? Epilepsia. 2017;58:727–742. doi: 10.1111/epi.13699. PubMed DOI
Otte WM, et al. A meta-analysis of white matter changes in temporal lobe epilepsy as studied with diffusion tensor imaging. Epilepsia. 2012;53:659–667. doi: 10.1111/j.1528-1167.2012.03426.x. PubMed DOI
Hatton SN, et al. White matter abnormalities across different epilepsy syndromes in adults: An ENIGMA-Epilepsy study. Brain. 2020;143:2454–2473. doi: 10.1093/brain/awaa200. PubMed DOI PMC
Wang Y, et al. Voxel-based automated detection of focal cortical dysplasia lesions using diffusion tensor imaging and T2-weighted MRI data. Epilepsy Behav. 2018;84:127–134. doi: 10.1016/j.yebeh.2018.04.005. PubMed DOI
Yin X, et al. Inferior frontal white matter asymmetry correlates with executive control of attention. Hum. Brain Mapp. 2013;34:796–813. doi: 10.1002/hbm.21477. PubMed DOI PMC
Zhao X, et al. Reduced interhemispheric white matter asymmetries in medial temporal lobe epilepsy with hippocampal sclerosis. Front. Neurol. 2019;10:1–10. doi: 10.3389/fneur.2019.00001. PubMed DOI PMC
Aparicio J, et al. Combined 18F-FDG-PET and diffusion tensor imaging in mesial temporal lobe epilepsy with hippocampal sclerosis. NeuroImage Clin. 2015;12:976–989. doi: 10.1016/j.nicl.2016.05.002. PubMed DOI PMC
Tae W-S, Ham B-J, Pyun S-B, Kang S-H, Kim B. Current clinical applications of diffusion-tensor imaging in neurological disorders. J. Clin. Neurol. 2018;14:129–140. doi: 10.3988/jcn.2018.14.2.129. PubMed DOI PMC
Campos BM, et al. White matter abnormalities associate with type and localization of focal epileptogenic lesions. Epilepsia. 2015;56:125–132. doi: 10.1111/epi.12871. PubMed DOI
Wellmer J, et al. Proposal for a magnetic resonance imaging protocol for the detection of epileptogenic lesions at early outpatient stages. Epilepsia. 2013;54:1977–1987. doi: 10.1111/epi.12375. PubMed DOI
Bernasconi A, et al. Recommendations for the use of structural magnetic resonance imaging in the care of patients with epilepsy: A consensus report from the international league against epilepsy neuroimaging task force. Epilepsia. 2019 doi: 10.1111/epi.15612. PubMed DOI
Tournier J-D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage. 2019;202:116137. doi: 10.1016/j.neuroimage.2019.116137. PubMed DOI
Smith SM, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208–S219. doi: 10.1016/j.neuroimage.2004.07.051. PubMed DOI
Maximov II, Alnæs D, Westlye LT. Towards an optimised processing pipeline for diffusion magnetic resonance imaging data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank. Hum. Brain Mapp. 2019;40:4146–4162. doi: 10.1002/hbm.24691. PubMed DOI PMC
Veraart J, et al. Denoising of diffusion MRI using random matrix theory. Neuroimage. 2016;142:394–406. doi: 10.1016/j.neuroimage.2016.08.016. PubMed DOI PMC
Veraart J, Fieremans E, Novikov DS. Diffusion MRI noise mapping using random matrix theory. Magn. Reson. Med. 2016;76:1582–1593. doi: 10.1002/mrm.26059. PubMed DOI PMC
Kellner E, Dhital B, Kiselev VG, Reisert M. Gibbs-ringing artifact removal based on local subvoxel-shifts. Magn. Reson. Med. 2016;76:1574–1581. doi: 10.1002/mrm.26054. PubMed DOI
Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063–1078. doi: 10.1016/j.neuroimage.2015.10.019. PubMed DOI PMC
Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. Neuroimage. 2003;20:870–888. doi: 10.1016/S1053-8119(03)00336-7. PubMed DOI
Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 2001;20:45–57. doi: 10.1109/42.906424. PubMed DOI
Ades-Aron B, et al. Evaluation of the accuracy and precision of the diffusion parameter EStImation with Gibbs and NoisE removal pipeline. Neuroimage. 2018;183:532–543. doi: 10.1016/j.neuroimage.2018.07.066. PubMed DOI PMC
Veraart J, et al. More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging. Magn. Reson. Med. 2011;65:138–145. doi: 10.1002/mrm.22603. PubMed DOI
Smith SM, et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487–1505. doi: 10.1016/j.neuroimage.2006.02.024. PubMed DOI
Staljanssens W, et al. EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy. NeuroImage Clin. 2017;16:689–698. doi: 10.1016/j.nicl.2017.09.011. PubMed DOI PMC
Mareček R, et al. Automated fusion of multimodal imaging data for identifying epileptogenic lesions in patients with inconclusive magnetic resonance imaging. Hum. Brain Mapp. 2021 doi: 10.1002/hbm.25413. PubMed DOI PMC
Kanber B, et al. Detection of covert lesions in focal epilepsy using computational analysis of multimodal magnetic resonance imaging data. Epilepsia. 2021;62:807–816. doi: 10.1111/epi.16836. PubMed DOI PMC
Del Gaizo J, et al. Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain Behav. 2017;7:e00801. doi: 10.1002/brb3.801. PubMed DOI PMC
Huang J, Xu J, Kang L, Zhang T. Identifying epilepsy based on deep learning using DKI images. Front. Hum. Neurosci. 2020;14:465. doi: 10.3389/fnins.2020.00465. PubMed DOI PMC
Zijlmans M, Zweiphenning W, van Klink N. Changing concepts in presurgical assessment for epilepsy surgery. Nat. Rev. Neurol. 2019;15:594–606. doi: 10.1038/s41582-019-0224-y. PubMed DOI
Kramer MA, Cash SS. Epilepsy as a disorder of cortical network organization. Neuroscience. 2012;18:360–372. PubMed PMC
Liu M, Concha L, Lebel C, Beaulieu C, Gross DW. Mesial temporal sclerosis is linked with more widespread white matter changes in temporal lobe epilepsy. NeuroImage Clin. 2012;1:99–105. doi: 10.1016/j.nicl.2012.09.010. PubMed DOI PMC