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Abstract #3678

Learning-based attenuation correction for Head and Neck PET/MR

Maya Khalifé1, Romain de Laroche2,3, Sandeep Kaushik4, Brian Sgard2,3, Fernando Pérez-García1, Melika Sahli Amor5, Didier Dormont5, Marie-Odile Habert2,3, Florian Wiesinger6, and Aurélie Kas2,3

1Centre de NeuroImagerie de Recherche (CENIR), Institut du Cerveau et de la Moelle Epinière (ICM), Paris, France, 2Department of Nuclear Medicine, Groupe Hospitalier Pitié-Salpêtrière C. Foix, Paris, France, 3Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France, 4GE Global Research, Bangalore, India, 5Department of diagnostic and functional neuroradiology, Groupe Hospitalier Pitié-Salpêtrière C. Foix, Paris, France, 6GE Healthcare, Munich, Germany

PET/MR in head and neck cancer lacks accurate attenuation correction (AC). In this work we implemented and tested three PET/MR-AC methods: 1) Dixon-based AC as used in clinical routine ignoring facial and cervical bones (Dixon), 2) Zero TE (ZTE)-based AC for segmenting bone and combined with Dixon-based fat-water separation (hZTE), 3) a deep learning approach (DL), trained on CT-ZTE datasets. PET images were reconstructed on six patients testing three AC methods (Dixon, hZTE, DL) and compared to reference CT-AC. PET comparison showed underestimated SUV with Dixon-AC, decreased error with hZTE-AC compared to CT-AC and the lowest error with DL-AC.

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