Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes Approach

TL;DR

This paper introduces the Fake Nodes approach to reduce the Gibbs effect in oversampled multimodal medical images, improving accuracy and efficiency in image resampling.

Contribution

The paper proposes a novel Fake Nodes resampling scheme that minimizes Gibbs artifacts in oversampled images, outperforming traditional methods.

Findings

Fake Nodes resampling yields smaller errors than traditional methods.

The approach effectively reduces Gibbs artifacts in oversampled images.

Experimental results confirm improved accuracy in medical image resampling.

Abstract

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.