noising layers you have your data consistency layer which then forces your measurement to be consistent with K space and effectively that links to your k space measurements so and then you can sort of in analog to a sort of iterative optimization you can on you can cascade these networks to an arbitrary depth we typically use five different of these cascades so here's an example what you end up with when you see on the left hand side is the image as acquired with six fold under sampling so if it's six fold on the sampling that means the