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Abstract #0299

The deep SECRET to accelerated first-pass perfusion cardiac MRI

Elena Martín-González1, Ebraham Alskaf2, Amedeo Chiribiri 2, Pablo Casaseca-de-la-Higuera1, Carlos Alberola-López1, Rita G. Nunes3,4, and Teresa Correia2,5
1Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain, 2School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom, 3Institute for Systems and Robotics, Lisbon, Portugal, 4Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, 5Centre for Marine Sciences - CCMAR, Faro, Portugal


First-pass perfusion cardiac magnetic resonance (FPP-CMR) is becoming essential to detect blow flow anomalies. However, the need for real-time acquisitions limits the achievable spatial resolution and coverage of the heart. To keep both within a reasonable range, FPP-CMR needs to be accelerated. A SElf-Supervised aCcelerated REconsTruction (SECRET) DL framework is presented to speed-up reconstruction of FPP-CMR images from undersampled (k,t)-space data. The physical reconstruction models are used to train deep neural networks without requiring fully sampled images. SECRET achieves good quality reconstructions at a variety of acceleration rates, with significant speed-ups compared to the state-of-the-art.

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