As far as I know, digital image sensors are a bit more complicated case than the classical sampling theorem would predict. First of all, it is important to understand the full meaning that the captured image is a
twothree dimensional signal (x, y and intensity) and how the eye sees it.
Using classical sampling theorem, a maximum resolvable frequency could be found by taking the inverse of (2*pixel pitch), which would lead to Nyqvist cut-off frequency. However, this is not the case, as in the measurements the image sensor tends to see further, as explained in  and published in 
As a short version, if one is able to align the pixel array exactly in the direction of bar patterns, the classical Nyqvist frequency holds. However, it is very difficult to do this, and thus what is actually seen is a result of sub-pixel sampling, which is then averaged by the eye and interpreted as a distinguishable bar. If one would only take a single line of the image, I'm not sure if the result in that case would be classified as distinguishable.
Add on top of that the fact whether we want to represent the actual shape of the subject at the maximum resolvable frequency despite the fact if it lands between the pixels, it can be seen that there can be a need for three to five times oversampling. I don't unfortunately have a good link to show this, I'll try to look for it and post it whether I can find it. However, this tends to be a way of selling more pixels too.
EDIT: Ah, found it, the PDF was by Andor . What I want to say with all this, is that it is actually not that well defined what is meant by "resolving something" with the image sensors.
Those links have nothing to do with the sampling theorem. The latter does not care whether you image bars, etc., it tells you how to sample an a priori band limited signal (the bars are NOT that), and how to reconstruct it. The modification needed that I mentioned is simple and must have been done by somebody already. In short, if your image is band limited already (this is what the AA filter does, together with the lens), and you have a good estimate what that limit is, you know how many pixels you need.
Do not confuse a convenient resolution test (bars) with the sampling theorem.
There is a misunderstanding somewhere here, for me it sounds like we are talking about different things or use different terms. I'm well aware of the different nature of the problem described in . However, what I meant to say with that is related to your earlier PSF considerations, when characterizing the PSF, the energy in the typical photographic objective spot is typically within the region of 1-3 camera body pixels, with a central core of the energy (something like 80 %) in a single pixel.
So in that case, you would be quite subject to errors in estimating the PSF due to the effect shown in . And you really don't know the PSF beforehand. Only at the proximity of image edges (or using fast lenses) the PSF may become large enough to be sampled well by the camera sensor. If you are using a different bench for estimating the PSF with magnification, you'll then lose the effect of the AA filter as well.
Also, the photographic objective MTF isn't typically evaluated from a PSF (haven't seen this being used in many places), but from an edge or line spread function which then allows sub-pixel sampling and is more robust against positioning with respect to sampling grid. Astronomical telescopes may be a different thing, I don't have experience in designing them.
The point of  was to show that for example, depending on the angle the camera is mounted with respect to the bar chart target, your micro-contrast figures may change slightly.
None of this actually matters to the actual photography, though. I don't know whether we should continue with private messages, I suppose this is going to get technical and lots of people aren't probably interested in seeing this.