Machine learning for denoising MRI images
My personal feeling is that you should do each things separately and compare the results.  For example, take your MRI dataset and denoise using "standard algorithm 1", "standard algorithm 2" and "neural network algorithm 1".  I would keep things reasonably simple unless you have a good justification of doing "standard algorithm 2 and neural network algorithm 1".   Doing two in a row is going to be harder to justify in a paper.

(I think the other question that is difficult is how to tell if one denoising algorithm is "better" than another.  I have written a couple papers on standard image processing denoising in MRI and it is always a difficult question to answer.  Also, if the output of the denoising is a dataset that is to be interpreted by a radiologist then other consideration would have to happen vs denoising as a pre-step to some scientific quantification.)

One thing you could do is look through papers, for example at https://arxiv.org/ to see if there are other neural network denoising examples that can help guide you in parameter selection.