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

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