Brain volumetry with three‐dimensional T1‐weighted magnetic resonance image

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Computational analyses （i.e., Voxel‐based morphometry （VBM）, boundary shift integral, tensor‐based morphometry, and atlas‐based volumetry） with three‐dimensional T1‐weighted image （3D‐T1WI） were used for brain volume estimation. VBM serve as a good tool for assessment of brain volume and can be easily used to study different pathologies（e.g., Alzheimer's disease （AD）, epilepsy, diabetes, and panic disorder, etc.）. To diagnose brain disease, it is important to improve understanding of correlation between cognitive function and local brain atrophy in normal aging. Previous studies report that brain shrinkage occurs disproportionately throughout the whole brain. There has been a rapid increase in research with VBM into clinical disease. However, there are numerous controversial issues associated with VBM, including signal intensity non‐uniformity and image distortion of 3D‐T1WI. Non‐parametric non‐uniform intensity normalization （N3） is the most used method, and reduces estimation error caused by signal intensity non‐uniformity in VBM study. Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra （DARTEL） has recently attracted attention as a group‐wise registration algorithm for forming a template for spatial normalization, and reduces estimation error caused by image distortion in VBM study. However, estimation errors caused by intensity non‐uniformity and image distortion are not completely eliminated. VBM users must pay attention to changes of 3D‐T1WI quality （intensity non‐uniformity, image distortion, signal‐to‐noise ratio, and contrast）.

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