Longitudinal Registration

Longitudinal registration aims to accurately align medical images acquired from the same subject at different time points, enabling the tracking of anatomical changes over time. Current research emphasizes developing robust and unbiased methods, often employing deep learning architectures (e.g., convolutional neural networks) or Bayesian inference within frameworks that account for factors like treatment effects or tissue deformation. These advancements improve the precision of longitudinal analyses, leading to more accurate quantification of disease progression (e.g., tumor growth, brain atrophy) and potentially enabling better treatment planning and personalized medicine. The development of open-source tools further facilitates wider adoption and collaborative research in this crucial area.