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Abstract #3629

Impact of machine learning in iterative motion corrected reconstructions

Rita G. Nunes1, Santiago Sanz-Estébanez2, Joseph V. Hajnal3, Lucilio Cordero-Grande3, and Carlos Alberola-López2
1ISR-Lisbon/LARSyS and Department of Bioengineering, Instituto Superior Técnico – University of Lisbon, Lisbon, Portugal, Lisbon, Portugal, 2Laboratorio de Procesado de Imagen, Universidad de Valladolid, Valladolid, Spain, Valladolid, Spain, 3Centre for the Developing Brain and Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London,London U.K, London, United Kingdom

Incorporation of machine learning (ML) approaches in MR reconstruction is currently a hot topic because of the enormous potential that deep learning solutions have shown in vision and imaging communities. Recently, a procedure known as NAMER has been proposed; this procedure incorporates a ML module into an iterative reconstruction for multishot acquisitions with inter-shot motion estimation and correction (referred to as aligned reconstruction). In this abstract we provide some insight on the benefits and limitations associated to NAMER by analyzing its behavior both with a steady and a discontinued use of the ML artifact cleaning step.

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