The recent driven equilibrium single-pulse observation of T1 (DESPOT1) approach permits real-time clinical
acquisition of large-volume and high-isotropic-resolution T1 mapping of MR tissue parameters with improved
uniformity. It is assumed that the quantitative nature of maps will facilitate clinical applications such as disease
diagnosis and comparison across subjects. However, there is not yet enough quantitative evidence on the
actual benefit of adopting T1 maps, especially in computer-aided medical image analysis tasks. In this study, we
compare methods with respect to image types, T1-weighted images or T1 maps, in automatic brain MRI segmentation.
Our experimental results demonstrate that, using T1 maps, different segmentation algorithms show
better agreement with each other, compared to that from using T1-weighted images. Furthermore, through
multi-dimensional-scaling projection, we are able to visualize the relative affinity among segmentation results,
which reveals that the projections of those segmentations using two different types of input images tend to form
two separate clusters. Finally, by comparing to expert segmented reference segmentation of brain sub-regions,
our results clearly indicate a better agreement between the manual reference and those automatic ones on T1
maps. In other words, our study provides an evidence for the hypothesis that compared to the conventionally
used T1-weighted images, T1 maps lead to improved reliability in automatic brain MRI segmentation task.