Addendum to “A white matter lesion-filling approach to improve braintissue volume measurements” [NeuroImage Clin. vol. 6, 2014, pages 86–92]

In their interesting paper, Valverde and colleagues (Valverde et al., 2014) have proposed a new method for filling white matter lesions to reduce their impact on brain tissue classification and compared it with several other available tools/approaches. This comparison aimed at including a method we previously presented at the European Committee for Treatment and Research in Multiple Sclerosis meeting (ECTRIMS, Magon et al., 2013; for a detailed description see Magon et al., 2014, published after Valverde's paper). Overall, Valverde et al. (2014) showed that lesion filling is a fundamental step to correctly estimate white and gray matter volumes using magnetic resonance data. Indeed, all tested methods strongly improved the accuracy of tissue volume computation by both FSL and SPM.

In the paper, our method is referred to as “MAGON” method. We would like to clarify here that, as applied by Valverde et al. (2014), a crucial step of our method was missed. Specifically the voxels belonging to the gray matter were not excluded from the computation of white matter intensity values. Our method consists of the following steps. First, white matter lesions were semi-automatically delineated on proton density/T2-weighted images in order to obtain binary lesion masks. To determine the signal intensity later applied for filling of the lesions on high-resolution 3D T1-weighted images, the lesion masks were expanded to the neighboring two voxels in each direction. The border voxels were then identified by subtracting the original lesion mask from the expanded lesion mask. Valverde et al. (2014) note in the discussion of their paper that in case of juxtacortical lesions, voxels that belong to the gray matter could be included in the expanded border and may decrease the values, which are later used for filling of white matter lesions. As a consequence, the gray matter/white matter border could shift towards gray matter intensity values leading to overestimation of the white matter volume and underestimation of the gray matter volume. We agree that this could be a potential source of error and therefore, indeed, we have subtracted the gray matter mask generated before the lesion filling to the expanded lesion border. Moreover, to reduce partial volume effects due to the resampling of the low-resolution lesion mask to higher resolution images, we excluded 10% of voxels with the lowest signal intensities from the computation of the mean signal intensity used to fill the lesions. These last two steps of our method are fundamental in order to avoid a biased computation of intensity values used for lesion filling and to avoid a subsequent misclassification due to the influence of gray matter.

In conclusion, we congratulate Valverde and colleagues to their very interesting and important paper, however, the method referred to as “MAGON” method in their paper does miss one key step of the methodology, which we have previously proposed for lesion filling (Magon et al., 2013, 2014) and hence the results determined for the “MAGON” method in their paper may not reflect the full potential performance of the method we have previously suggested.