Visualizing under sampled volume data

Years ago I have a vague recollection of reading a paper about an approach for visualizing surfaces in volume data that is undersampled. e.g. a confocal stack of a cell membrane under sampled such that it would appear as a series of rings under normal interpolation methods. The approach filled these gaps in, in a max projection like visualization -of the image data-, I don’t quite remember how while deliberately avoiding creating an explicit surface representation. No luck searching for it, I remember it being somewhere fairly prominent, does anyone remember it? or have other pointers to literature on this, or know if the problem has a name beyond under sampling or aliasing? It seems like a problem that would come up a lot but this random report is the only place I see it mentioned as a thing (in a more general context): Thanks for any thoughts!