Assessing multiple MRI sequences in deep learning-based synthetic CT generation for MR-only radiation therapy of head and neck cancers.

Jacob Antunes¹, Tony Young², Dane Pittock³, Paul Jacobs³, Aaron Nelson³, Jon Piper³, Shrikant Deshpande⁴

¹MIM Software Inc, Cleveland, OH, United States. Electronic address: jantunes@mimsoftware.com.

²Liverpool and Macarthur Cancer Therapy Centres, Sydney, Australia; Ingham Institute, Sydney, Australia.

³MIM Software Inc, Cleveland, OH, United States.

⁴Ingham Institute, Sydney, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, Australia.

PubMed DOI

Abstract

PURPOSE: This study investigated the effect of multiple magnetic resonance (MR) sequences on the quality of deep-learning-based synthetic computed tomography (sCT) generation in the head and neck region. MATERIALS AND METHODS: 12 MR series (T1pre-, T1post-contrast, T2 each with 4 Dixon images) were collected from 26 patients with head and neck cancers. 14 unique deep-learning models using the U-N…

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