Oversampling errors in multimodal medical imaging are due to the Gibbs effect
This addresses interpolation accuracy issues for medical imaging researchers, though it appears incremental as it focuses on characterizing known errors rather than introducing new solutions.
The study investigated interpolation errors in multimodal 3D medical imaging by comparing undersampling and oversampling strategies across three neuroimaging software tools, finding that undersampling to the lowest image size reduces mean segment errors and oversampling errors correlate with steeper gradients due to the Gibbs effect.
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.