Deep Learning Registration with Scan‐temporal Consistency May Improve Detection of Alzheimer’s Disease Progression

Structural changes in Alzheimer’s disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation‐based morphometry (DBM) to longitudinal scans. However, conventional DMB methods are computationally intensive and may not estimate the atrophy accurately due to the noise in the longitudinal images, e.g., head motion or MRI artifacts. We hypothesize that deep learning models can be trained to estimate atrophy from longitudinal MRI scans and are more sensitive to biological changes rather than artifactual.