  It is a common practice in multimodal medical imaging to undersample the
anatomically-derived segmentation images to measure the mean activity of a
co-acquired functional image. This practice avoids the resampling-related Gibbs
effect that would occur in oversampling the functional image. As sides effect,
waste of time and efforts are produced since the anatomical segmentation at
full resolution is performed in many hours of computations or manual work. In
this work we explain the commonly-used resampling methods and give errors bound
in the cases of continuous and discontinuous signals. Then we propose a Fake
Nodes scheme for image resampling designed to reduce the Gibbs effect when
oversampling the functional image. This new approach is compared to the
traditional counterpart in two significant experiments, both showing that Fake
Nodes resampling gives smaller errors.
