  Our findings indicate that adopting "advanced" computational elements fails
to significantly improve registration accuracy. Instead, well-established
registration-specific designs offer fair improvements, enhancing results by a
marginal 1.5\% over the baseline. Our findings emphasize the importance of
rigorous, unbiased evaluation and contribution disentanglement of all low- and
high-level registration components, rather than simply following the computer
vision trends with "more advanced" computational blocks. We advocate for
simpler yet effective solutions and novel evaluation metrics that go beyond
conventional registration accuracy, warranting further research across diverse
organs and modalities. The code is available at
\url{https://github.com/BailiangJ/rethink-reg}.
