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Abstract #3762

Tissue based denoising for MR fingerprinting via long short-term memory networks

Gastao Cruz1, Thomas Kuestner1, Ilkay Oksuz1, Olivier Jaubert1, Niccolo Fuin1, Andy P. King1, Julia A. Schnabel1, René M. Botnar1, and Claudia Prieto1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Conventional Magnetic Resonance Fingerprinting (MRF) relies on pixelwise dictionary matching of highly undersampled time-series images. However, remaining aliasing artefacts in these images can compromise the matching step and thus affect the accuracy of the parametric maps. Dictionary-based compression has been proposed to exploit redundancies in the signal evolution dimension, however these approaches do not exploit tissue redundancies within the images. Here we propose to leverage redundant information between similar tissues and the MRF dictionary to suppress residual artefacts along time, using long short-term memory (LSTM) networks. Preliminary results indicate that proposed MRF-LSTMs can suppress aliasing in highly undersampled scenarios.

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