  Brain atrophy is an important biomarker for monitoring neurodegeneration and
disease progression in conditions such as multiple sclerosis (MS). An accurate
and robust quantitative measurement of brain volume change is paramount for
translational research and clinical applications. This paper presents a deep
learning based method, DeepBVC, for longitudinal brain volume change
measurement using 3D T1-weighted MRI scans. Trained with the intermediate
outputs from SIENA, DeepBVC is designed to take into account the variance
caused by different scanners and acquisition protocols. Compared with SIENA,
DeepBVC demonstrates higher consistency in terms of volume change estimation
across multiple time points in MS subjects; and greater stability and superior
performance in scan-rescan experiments. Moreover, the results also show that
DeepBVC is insensitive to acquisition variance in terms of imaging contrast,
voxel resolution, random bias field and signal-to-noise ratio. Measurement
robustness, automation and processing speed suggest a broad potential of
DeepBVC in both research and clinical quantitative neuroimaging applications.
