With the advent of active acquisition-reconstruction pipelines, this study shows that by exploiting motion, robust intermediate reconstructions can be used to exploit the entire k-space budget and stabilise deep learning methods for accelerated dynamic MRI. The generated intermediate reconstructions are known as data-consistent motion-augmented cines (DC-MAC). A motion-exploiting convolutional neural network (ME-CNN), which incorporates the DC-MAC, is evaluated against a similar model to that used in a recent active acquisition-reconstruction study, the data-consistent convolutional neural network (DC-CNN). We find that the ME-CNN outperforms DC-CNN but also the DC-MAC offers better reconstructions at low acceleration rates.
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