To analyse multimodal 3-dimensional medical images, interpolation is required for resampling which - unavoidably - introduces an interpolation error. In this work we consider three segmented 3-dimensional images resampled with three different neuroimaging software tools for comparing undersampling and oversampling strategies and to identify where the oversampling error lies. The results indicate that undersampling to the lowest image size is advantageous in terms of mean value per segment errors and that the oversampling error is larger where the gradient is steeper, showing a Gibbs effect.
Cite
@article{arxiv.2103.05964,
title = {Oversampling errors in multimodal medical imaging are due to the Gibbs effect},
author = {Davide Poggiali and Diego Cecchin and Cristina Campi and Stefano De Marchi},
journal= {arXiv preprint arXiv:2103.05964},
year = {2021}
}