2021
Kolinger GD, Vállez García D, Willemsen ATM, Reesink FE, de Jong BM, Dierckx RAJO, De Deyn PP, and Boellaard R (2021) Amyloid burden quantification depends on PET and MR image processing methodology. PLoS ONE 16(3): e0248122. https://doi.org/10.1371/journal.pone.0248122
Rausch I, Valladares A, Shiyam Sundar LK, Beyer T, Hacker M, Meyerspeer M, and Unger E (2021) Standard MRI-based attenuation correction for PET/MRI phantoms: a novel concept using MRI-visible polymer. EJNMMI Phys 8, 18 (2021). https://doi.org/10.1186/s40658-021-00364-9
2020
Shiyam Sundar LK, Iommi D, Muzik O, Chalampalakis Z, Klebermass EM, Hienert M, Rischka L, Lanzenberger R, Hahn A, Pataraia E, Traub-Weidinger E, and Beyer T (2020) Conditional Generative Adversarial Networks (cGANs) aided motion correction of dynamic 18F-FDG PET brain studies. J Nucl Med. doi: 10.2967/jnumed.120.248856
Villagran Asiares A, Yakushev I, and Nekolla SG (2020) Gating failure can result in underestimation of cardiac function in myocardial perfusion scintigraphy. J Nucl Cardiol. https://doi.org/10.1007/s12350-020-02430-8
Jafargholi Rangraz E, Tang X, van Laeken C, Maleux G, Dekervel J, van Cutsem E, Verslype C, Baete K, Nuyts J, and Deroose CM (2020) Quantitative comparison of pre-treatment predictive and post-treatment measured dosimetry for selective internal radiation therapy using cone-beam CT for tumor and liver perfusion territory definition. EJNMMI Res. 10, 94. https://dx.doi.org/10.1186%2Fs13550-020-00675-5
Capobianco N, Meignan MA, Cottereau AS, Vercellino L, Sibille L, Spottiswoode B, Zuehlsdorff S, Casasnovas O, Thieblemont C, and Buvat I (2020) Deep learning FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma [published online ahead of print, 2020 Jun 12]. J Nucl Med. 2020; jnumed.120.242412. https://doi.org/10.2967/jnumed.120.242412
Nicolò wrote a summary of his publication that you can find here.
Valladares A, Beyer T, and Rausch I (2020) Physical imaging phantoms for simulation of tumor heterogeneity in PET, CT, and MRI: An overview of existing designs. Med. Phys. 47(4), 2023-2037. https://doi.org/10.1002/mp.14045
Iommi D, Hummel J, and Figl ML (2020) Evaluation of 3D ultrasound for image guidance. PLOS ONE 15(3), e0229441. https://doi.org/10.1371/journal.pone.0229441
Tang X, Jafargholi Rangraz E, Coudyzer W, Bertels J, Robben D, Schramm G, Deckers W, Maleux G, Baete K, Verslype C, Gooding MJ, Deroose CM, and Nuyts J (2020) Whole liver segmentation based on deep learning and manual adjustment for clinical use in SIRT. Eur J Nucl Med Mol Imaging 47, 2742–2752. https://doi.org/10.1007/s00259-020-04800-3
You can find a summary of Xikai's publication here.
2019
Kolinger GD, Vállez García D, Kramer GM, Frings V, Smit EF, de Langen AJ, Dierckx RAJO, Hoekstra OS, and Boellaard R (2019) Repeatability of [18F]FDG PET/CT total metabolic active tumour volume and total tumour burden in NSCLC patients. EJNMMI Res 9, 14. https://doi.org/10.1186/s13550-019-0481-1
You can find a summary of Guilherme's publication here.
2018
Valladares A, Ahangari S, Beyer T, Boellaard R, Chalampalakis Z, Comtat C, DalToso L, Hansen AE, Koole M, Mackewn J, Marsden P, Nuyts J, Padormo F, Peeters R, Poth S, Solari E, and Rausch I (2019) Clinically Valuable Quality Control for PET/MRI Systems: Consensus Recommendation From the HYBRID Consortium. Front. Phys. 7, 136. doi: 10.3389/fphy.2019.00136
You can find a summary of Alejandra's publication here.
