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Abstract #1003

Deep Learning for Robust Accelerated Dynamic MRI Reconstruction for Active Acquisition Pipelines

Gavin Seegoolam1, Anthony Price2, Joseph V Hajnal2,3, and Daniel Rueckert1
1BioMedIA, Department of Computing, Imperial College London, London, United Kingdom, 2Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom, 3Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom

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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