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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