[rprenger]

I generally agree with the assessment that the limiting factor in quality is the signal from the MRI. Just one slight correction to the description of previous projects. We first built models that predict the voxel responses from images using ML. Then recorded the fMRI response on a new image. Then we ran many many images (not just those previously seen that have an fMRI response) through the voxel models and picked the top N images with predicted responses close to the actual response. Basically we used the big list of images as an approximate "natural image prior". Then we'd often show the average of that top N images so you get one picture (which destroys interesting multi-model stuff, but we didn't have good stable diffusion models of images back then that could do a gradient search on or something).

If you wanna see what the reconstructions look like without fancy image priors (to get a better idea of how little signal there is) check out this figure from one of the early papers: https://pubmed.ncbi.nlm.nih.gov/19778517/#&gid=article-figur...