# Oversampling errors in multimodal medical imaging are due to the Gibbs effect

arXiv: https://arxiv.org/abs/2103.05964 · HTML: https://mlsanity.com/paper/2103.05964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data](https://mlsanity.com/paper/2010.08357/) (2020)
- [Scale factor point spread function matching: Beyond aliasing in image resampling](https://mlsanity.com/paper/2101.06440/) (2021)

## Built on by (most cited first, 2 of 2)

- [Image scaling by de la Vall'ee-Poussin filtered interpolation](https://mlsanity.com/paper/2109.13897/) (2021)
- [Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes Approach](https://mlsanity.com/paper/2202.10325/) (2022)

## Abstract (arXiv)

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.