I am working on a project where I have 1024x1024 brain images over time depicting blood flow. A blood flow parameter image is computed using the brain images over time, and is off the same dimension (1024 x 1024). My goal is to train a neural network to learn the mapping between the brain images over time and the blood flow parameter image. Essentially, I want to feed in the time-series of images (brain scans), and have the neural network output a blood flow parameter image.
I've looked into current CNN architectures, but it seems like most research on CNNs is either done for classification on single images (not images over time) or action recognition on video data, which I'm not sure my problem falls under. If anyone can provide me with any insight or papers I can read on how to train a model on temporal data, with the output being an image (rather than a classification score), that would be immensely helpful.
I also recently ran across a blog article discussing CNN_LSTM networks. It seems like this could potentially be a good fit, if anyone has any input on this that would also be fantastic. Thanks in advance.