Abstract #4298

Fast reconstruction of SMS bSSFP myocardial perfusion images using a combination of parallel imaging and AI-based denoising

Naledi Lenah Adam¹, Kowalik Tomasz Grzegorz ¹, Andrew Tyler¹, Ronald Mooiweer^1,2, Alexander Paul Neofytou¹, Sarah McElroy¹, Karl Kunze^1,2, Peter Speier³, Daniel Stäb⁴, Radhouene Neji^1,2, Muhummad Sohaib Nazir¹, Reza Razavi¹, Amedeo Chiribiri¹, and Sébastien Roujol¹

¹School of Biomedical Engineering and Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, United Kingdom, ²MR Research Collaborations, Siemens Healthcare Limited, Camberley, United Kingdom, ³Cardiovascular Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, ⁴MR Research Collaborations, Siemens Healthcare Limited, Melbourne, Australia

Synopsis

Keywords: Heart, PerfusionSimultaneous multi-slice (SMS) imaging enables high spatial coverage and resolution for myocardial perfusion imaging. We developed and evaluated a fast, noise-removing and signal-preserving SMS reconstruction approach combining parallel imaging and deep-learning and evaluated its benefit for myocardial perfusion imaging. In comparison to conventional parallel imaging reconstruction, the proposed SMS reconstruction provides improved perceived SNR and image quality (p<0.008), preserved sharpness and signal fidelity (p>0.05) in a short computation time (+20s for an entire dataset).

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