Optimizing parameter choice for FSL-Brain Extraction Tool (BET) on 3D T1 images in multiple sclerosis

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• 作者：V. Popescu^a ; ^{v.popescu@vumc.nl} ; M. Battaglini^b ; W.S. Hoogstrate^a ; S.C.J. Verfaillie^a ; I.C. Sluimer^a ; R.A. van Schijndel^a ; B.W. van Dijk^c ; K.S. Cover^c ; D.L. Knol^d ; M. Jenkinson^e ; F. Barkhof^a ; N. de Stefano^b ; H. Vrenken^a ; ^c ; on behalf of the MAGNIMS Study Group¹
• 关键词：Brain extraction ; FSL ; BET ; Segmentation ; MRI ; Multiple sclerosis
• 刊名：NeuroImage
• 出版年：2012
• 期刊代码：83_10538119
• 类别：neu
• 出版时间：16 July, 2012
• 卷：61
• 期：4
• 页码：1484-1494
• 文件大小：2317 K

摘要

Background

Brain atrophy studies often use FSL-BET (Brain Extraction Tool) as the first step of image processing. Default BET does not always give satisfactory results on 3DT1 MR images, which negatively impacts atrophy measurements. Finding the right alternative BET settings can be a difficult and time-consuming task, which can introduce unwanted variability.

Aim

To systematically analyze the performance of BET in images of MS patients by varying its parameters and options combinations, and quantitatively comparing its results to a manual gold standard.

Methods

Images from 159 MS patients were selected from different MAGNIMS consortium centers, and 16 different 3DT1 acquisition protocols at 1.5 T or 3 T. Before running BET, one of three pre-processing pipelines was applied: (1) no pre-processing, (2) removal of neck slices, or (3) additional N3 inhomogeneity correction. Then BET was applied, systematically varying the fractional intensity threshold (the 鈥渇鈥?parameter) and with either one of the main BET options (鈥淏鈥?鈥?bias field correction and neck cleanup, 鈥淩鈥?鈥?robust brain center estimation, or 鈥淪鈥?鈥?eye and optic nerve cleanup) or none. For comparison, intracranial cavity masks were manually created for all image volumes. FSL-FAST (FMRIB's Automated Segmentation Tool) tissue-type segmentation was run on all BET output images and on the image volumes masked with the manual intracranial cavity masks (thus creating the gold-standard tissue masks). The resulting brain tissue masks were quantitatively compared to the gold standard using Dice overlap coefficient (DOC). Normalized brain volumes (NBV) were calculated with SIENAX. NBV values obtained using for SIENAX other BET settings than default were compared to gold standard NBV with the paired t-test.

Results

The parameter/preprocessing/options combinations resulted in 20,988 BET runs. The median DOC for default BET (f = 0.5, g = 0) was 0.913 (range 0.321-0.977) across all 159 native scans. For all acquisition protocols, brain extraction was substantially improved for lower values of 鈥渇鈥?than the default value. Using native images, optimum BET performance was observed for f = 0.2 with option 鈥淏鈥? giving median DOC = 0.979 (range 0.867-0.994). Using neck removal before BET, optimum BET performance was observed for f = 0.1 with option 鈥淏鈥? giving median DOC 0.983 (range 0.844-0.996). Using the above BET-options for SIENAX instead of default, the NBV values obtained from images after neck removal with f = 0.1 and option 鈥淏鈥?did not differ statistically from NBV values obtained with gold-standard.

Conclusion

Although default BET performs reasonably well on most 3DT1 images of MS patients, the performance can be improved substantially. The removal of the neck slices, either externally or within BET, has a marked positive effect on the brain extraction quality. BET option 鈥淏鈥?with f = 0.1 after removal of the neck slices seems to work best for all acquisition protocols.