Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging–Based Radiation Therapy Planning of Patients With Head and Neck Cancer

Robustness and Generalizability of Deep Learning Synthetic Computed Tomography for Positron Emission Tomography/Magnetic Resonance Imaging–Based Radiation Therapy Planning of Patients With Head and Neck Cancer

Purpose: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients wit...

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Bibliographic Details
Main Authors: Anders B. Olin, MSc, PhD, Christopher Thomas, MPhys, MSc, Adam E. Hansen, MSc, PhD, Jacob H. Rasmussen, MD, PhD, Georgios Krokos, MSc, PhD, Teresa Guerrero Urbano, PhD, FRCR, MRCPI, LMS, Andriana Michaelidou, MBBS, MSc, FRCR, MD, Björn Jakoby, MSc, PhD, Claes N. Ladefoged, MSc, PhD, Anne K. Berthelsen, MD, Katrin Håkansson, MSc, PhD, Ivan R. Vogelius, MSc, PhD, DMSc, Lena Specht, MD, PhD, DMSc, Sally F. Barrington, MBBS, MSc, FRCP, FRCR, MD, Flemming L. Andersen, MSc, PhD, Barbara M. Fischer, MD, PhD, DMSc
Format: Article
Language:English
Published: Elsevier 2021-11-01
Series:Advances in Radiation Oncology
Online Access:http://www.sciencedirect.com/science/article/pii/S2452109421001202

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http://www.sciencedirect.com/science/article/pii/S2452109421001202

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