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

Autor: 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

Publikováno v: Advances in Radiation Oncology, Vol 6, Iss 6, Pp 100762- (2021)

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 cr

Externí odkaz: https://doaj.org/article/9150efecbfb245fc843b7348e9d49ade

RESOLUTE PET/MRI Attenuation Correction for O-(2-18F-fluoroethyl)-L-tyrosine (FET) in Brain Tumor Patients with Metal Implants

Autor: Claes N. Ladefoged, Flemming L. Andersen, Andreas Kjær, Liselotte Højgaard, Ian Law

Publikováno v: Frontiers in Neuroscience, Vol 11 (2017)

Aim: Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes, and the radiolabeled amino acid analog tracer O-(2-18F-fluoroethyl)-L-tyrosine (FET)-PET is amongst

Externí odkaz: https://doaj.org/article/137f01e7fb454f04b53011b10857d070

Contributors

Autor: Oscar Acosta, Simon Arridge, Anais Barateau, Riccardo Barbano, Julien Bert, Ninon Burgos, Hu Chen, Kevin T. Chen, Xiaoran Chen, Li Cheng, Jae Hyuk Choi, Hilda Chourak, Walter J. Curran, Renaud de Crevoisier, Srijay Deshpande, Blake E. Dewey, Jason Dowling, Ivana Drobnjak, Jan Ehrhardt, Dennis Eschweiler, Alejandro F. Frangi, Mark Graham, Peter Greer, Shuo Han, Yufan He, Juan Eugenio Iglesias, Mark Jenkinson, Bangti Jin, Wenchi Ke, Charles Kervrann, Ender Konukoglu, Violeta Kovacheva, Claes N. Ladefoged, Ina Laube, Yang Lei, Andrea Leo, Bowen Li, Huiqi Li, Tian Liu, Yihao Liu, Matteo Mancini, Fayyaz Minhas, Tereza Nečasová, Dong Nie, Jean-Claude Nunes, Laura O'Connor, Ilkay Oksuz, Anders B. Olin, Jerry L. Prince, Richard L.J. Qiu, Nasir Rajpoot, Parnesh Raniga, Nishant Ravikumar, Samuel W. Remedios, Pekka Ruusuvuori, David Sarrut, Johannes Stegmaier, David Svoboda, Ryutaro Tanno, Sotirios A. Tsaftaris, Vladimír Ulman, Gabriele Valvano, Tonghe Wang, Xuyun Wen, David Wiesner, Matthias Wilms, Yan Xia, Xiaofeng Yang, Greg Zaharchuk, Hui Zhang, Yi Zhang, Can Zhao, He Zhao, Lianrui Zuo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b44b27cc340c3c75307c529f2c431a63
https://doi.org/10.1016/b978-0-12-824349-7.00006-2

Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients

Autor: Anders B. Olin, Adam E. Hansen, Jacob H. Rasmussen, Björn Jakoby, Anne K. Berthelsen, Claes N. Ladefoged, Andreas Kjær, Barbara M. Fischer, Flemming L. Andersen

Publikováno v: Olin, A B, Hansen, A E, Rasmussen, J H, Jakoby, B, Berthelsen, A K, Ladefoged, C N, Kjær, A, Fischer, B M & Andersen, F L 2022, ' Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients ', EJNMMI Physics, vol. 9, no. 1, 20 . https://doi.org/10.1186/s40658-022-00449-z

Background Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b994ea3c10b7b81ec46a41432f209e90
https://pubmed.ncbi.nlm.nih.gov/35294629

Comparison of simultaneous arterial spin labeling MRI and

Autor: Oriol, Puig, Otto M, Henriksen, Mark B, Vestergaard, Adam E, Hansen, Flemming L, Andersen, Claes N, Ladefoged, Egill, Rostrup, Henrik Bw, Larsson, Ulrich, Lindberg, Ian, Law

Publikováno v: Journal of Cerebral Blood Flow & Metabolism

Arterial spin labelling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that may provide fully quantitative regional cerebral blood flow (rCBF) images. However, before its application in clinical routine, ASL needs to be validated

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::0b52bb0d85ef582546f7198915fc00fa
https://pubmed.ncbi.nlm.nih.gov/31500521