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基于体素的多光谱磁共振形态测量学在先前 MRI 阴性局灶性癫痫中的系统评估

Systematic Assessment of Multispectral Voxel-Based Morphometry in Previously MRI-Negative Focal Epilepsy.

机构信息

From the Departments of Diagnostic and Interventional Neuroradiology (R.K., T.L., B.B.)

Neurology and Epileptology (R.K., P.M., J.M., N.K.F.), Hertie Institute for Clinical Brain Research, University Hospital Tübingen, Tübingen, Germany.

出版信息

AJNR Am J Neuroradiol. 2018 Nov;39(11):2014-2021. doi: 10.3174/ajnr.A5809. Epub 2018 Oct 18.

Abstract

BACKGROUND AND PURPOSE

Voxel-based morphometry is widely used for detecting gray matter abnormalities in epilepsy. However, its performance with changing parameters, smoothing and statistical threshold, is debatable. More important, the potential yield of combining multiple MR imaging contrasts (multispectral voxel-based morphometry) is still unclear. Our aim was to objectify smoothing and statistical cutoffs and systematically compare the performance of multispectral voxel-based morphometry with existing T1 voxel-based morphometry in patients with focal epilepsy and previously negative MRI.

MATERIALS AND METHODS

3D T1-, T2-, and T2-weighted FLAIR scans were acquired for 62 healthy volunteers and 13 patients with MR imaging negative for focal epilepsy on a Magnetom Skyra 3T scanner with an isotropic resolution of 0.9 mm. We systematically optimized the main voxel-based morphometry parameters, smoothing level and statistical cutoff, with T1 voxel-based morphometry as a reference. As a next step, the performance of multispectral voxel-based morphometry models, T1+T2, T1+FLAIR, and T1+T2+FLAIR, was compared with that of T1 voxel-based morphometry using gray matter concentration and gray matter volume analysis.

RESULTS

We found the best performance of T1 at 12 mm and a T-threshold (statistical cutoff) of 3.7 for gray matter concentration analysis. When we incorporated these parameters, after expert visual interpretation of concordant and discordant findings, we identified T1+FLAIR as the best model with a concordant rate of 46.2% and a concordant rate/discordant rate of 1.20 compared with T1 with 30.8% and 0.67, respectively. Visual interpretation of voxel-based morphometry findings decreased concordant rates from 38.5%-46.2% to 15.4%-46.2% and discordant rates from 53.8%-84.6% to 30.8%-46.2% and increased specificity across models from 33.9%-40.3% to 46.8%-54.8%.

CONCLUSIONS

Multispectral voxel-based morphometry, especially T1+FLAIR, can yield superior results over single-channel T1 in focal epilepsy patients with a negative conventional MR imaging.

摘要

背景与目的

体素形态计量学广泛用于检测癫痫患者的灰质异常。然而,其参数变化、平滑和统计阈值的性能仍存在争议。更重要的是,结合多种磁共振成像对比(多光谱体素形态计量学)的潜在收益尚不清楚。我们的目的是客观化平滑和统计截止值,并系统比较多光谱体素形态计量学与现有 T1 体素形态计量学在局灶性癫痫和先前 MRI 阴性患者中的性能。

材料与方法

在 Magnetom Skyra 3T 扫描仪上对 62 名健康志愿者和 13 名局灶性癫痫 MRI 阴性患者进行了 3D T1、T2 和 T2 加权 FLAIR 扫描,各向同性分辨率为 0.9mm。我们系统地优化了主要体素形态计量学参数、平滑水平和统计截止值,以 T1 体素形态计量学为参考。作为下一步,使用灰质浓度和灰质体积分析比较了多光谱体素形态计量学模型 T1+T2、T1+FLAIR 和 T1+T2+FLAIR 的性能与 T1 体素形态计量学的性能。

结果

我们发现 T1 最佳性能为 12mm,T 阈值(统计截止值)为 3.7,用于灰质浓度分析。当我们合并这些参数后,在专家对一致和不一致发现进行视觉解释后,我们发现 T1+FLAIR 是最好的模型,其一致性率为 46.2%,一致性率/不一致率为 1.20,而 T1 的一致性率为 30.8%,一致性率/不一致率为 0.67。体素形态计量学发现的视觉解释将一致性率从 38.5%-46.2%降低至 15.4%-46.2%,将不一致率从 53.8%-84.6%降低至 30.8%-46.2%,并将各模型的特异性从 33.9%-40.3%提高至 46.8%-54.8%。

结论

多光谱体素形态计量学,特别是 T1+FLAIR,在常规 MRI 阴性的局灶性癫痫患者中可以产生优于单通道 T1 的结果。

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本文引用的文献

1
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Epilepsia. 2017 Sep;58(9):1653-1664. doi: 10.1111/epi.13851. Epub 2017 Jul 26.
2
Multimodal MEMPRAGE, FLAIR, and [Formula: see text] Segmentation to Resolve Dura and Vessels from Cortical Gray Matter.
Front Neurosci. 2017 May 9;11:258. doi: 10.3389/fnins.2017.00258. eCollection 2017.
3
Multimodal FLAIR/MPRAGE segmentation of cerebral cortex and cortical myelin.
Neuroimage. 2017 May 15;152:130-141. doi: 10.1016/j.neuroimage.2017.02.054. Epub 2017 Feb 28.
4
Evaluation of multimodal segmentation based on 3D T1-, T2- and FLAIR-weighted images - the difficulty of choosing.
Neuroimage. 2018 Apr 15;170:210-221. doi: 10.1016/j.neuroimage.2017.02.016. Epub 2017 Feb 7.
5
Post-processing of structural MRI for individualized diagnostics.
Quant Imaging Med Surg. 2015 Apr;5(2):188-203. doi: 10.3978/j.issn.2223-4292.2015.01.10.
6
Voxel-based morphometric magnetic resonance imaging (MRI) postprocessing in MRI-negative epilepsies.
Ann Neurol. 2015 Jun;77(6):1060-75. doi: 10.1002/ana.24407. Epub 2015 Apr 23.
7
The pathology of magnetic-resonance-imaging-negative epilepsy.
Mod Pathol. 2013 Aug;26(8):1051-8. doi: 10.1038/modpathol.2013.52. Epub 2013 Apr 5.
8
Voxel based morphometry of FLAIR MRI in children with intractable focal epilepsy: implications for surgical intervention.
Eur J Radiol. 2012 Jun;81(6):1299-305. doi: 10.1016/j.ejrad.2010.12.043. Epub 2011 Jan 15.
10
Surgical outcomes in lesional and non-lesional epilepsy: a systematic review and meta-analysis.
Epilepsy Res. 2010 May;89(2-3):310-8. doi: 10.1016/j.eplepsyres.2010.02.007. Epub 2010 Mar 15.

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