Date Published: February 28, 2019
Publisher: Public Library of Science
Author(s): Uk-Su Choi, Hirokazu Kawaguchi, Yuichiro Matsuoka, Tobias Kober, Ikuhiro Kida, Viktor Vegh.
We proposed a method for segmentation of brain tissues—gray matter, white matter, and cerebrospinal fluid—using multi-contrast images, including a T1 map and a uniform T1-weighted image, from a magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence at 7 Tesla. The proposed method was evaluated with respect to the processing time and the similarity of the segmented masks of brain tissues with those obtained using FSL, FreeSurfer, and SPM12. The processing time of the proposed method (28 ± 0 s) was significantly shorter than those of FSL and SPM12 (444 ± 4 s and 159 ± 2 s for FSL and SPM12, respectively). In the similarity assessment, the tissue mask of the brain obtained by the proposed method showed higher consistency with those obtained using FSL than with those obtained using SPM12. The proposed method misclassified the subcortical structures and large vessels since it is based on the intensities of multi-contrast images obtained using MP2RAGE, which uses a similar segmentation approach as FSL but is not based on a template image or a parcellated brain atlas, which are used for FreeSurfer and SPM12, respectively. However, the proposed method showed good segmentation in the cerebellum and white matter in the medial part of the brain in comparison with the other methods. Thus, because the proposed method using different contrast images of MP2RAGE sequence showed the shortest processing time and similar segmentation ability as the other methods, it may be useful for both neuroimaging research and clinical diagnosis.
Structural information regarding brain tissue is important for both neuroimaging research and clinical diagnosis. Magnetic resonance imaging (MRI) has been widely used to obtain structural information from various types of contrast images. Different MR contrast images can show brain abnormalities via segmentation of subcortical structures in neuronal disorders, such as Parkinson’s or Alzheimer’s diseases . Furthermore, gray matter (GM) segmentation can be used to estimate cortical thickness or volume to evaluate developmental stages or the effects of aging . In functional MRI, white matter (WM) segmentation can provide an inflated brain mesh  to project brain activation maps.
We proposed a segmentation method with a significantly shorter processing time for brain tissues (GM, WM, and CSF). The proposed method employs a simple calculation of normalized signal intensities in the images with differing contrasts produced with an MP2RAGE sequence (UNI, INV1, and T1). The calculations were based on a consistent and specific tissue-dependent pattern of normalized intensities in the masks segmented with FSL, FreeSurfer, and SPM12, which are commonly used in neuroimaging protocols. Most segmentation methods use a single T1w image obtained from an MPRAGE sequence, and numerous groups have attempted to solve the problem of overlapping signal distribution with complex and sophisticated methods that require considerable processing time to classify different brain tissues [5,13]. Although a few segmentation methods using multiple contrast images from an MP2RAGE sequence have been proposed , these require longer processing times because of the complexity of their algorithms. In contrast, the proposed method exhibits superior processing times because it utilizes simple calculations and three different contrast images from an MP2RAGE sequence. Therefore, the proposed method can segment a mask of brain tissues from the images, even with a high spatial-resolution at UHF, with shorter processing times.
We propose a novel brain tissue segmentation method that uses different contrast images from an MP2RAGE sequence. The proposed method allows rapid processing with relevant segmentation of brain tissues. A recent study has reported MP2RAGE-based segmentation using two images with different inversion times, i.e., INV1 and INV2 . The study demonstrated superior segmentation in subcortical structures in comparison with FSL and SPM12, whereas our proposed method produced good segmentation in the cerebellum and WM in the medial part of the brain. Taken together, MP2RAGE-based segmentation is highly dependent on the relationship of the different contrasts between the input images used in the calculations, and this property could be of benefit in acquiring more precise focal segmentation of specific brain structures, such as the subcortical structures and the cerebellum. Thus, the MP2RAGE-based segmentation we proposed here has the potential to be applied, with optimized parameters of the MP2RAGE sequence, to both neuroimaging research and clinical diagnosis.