Ahangari S, Hansen NL, Olin AB, Nøttrup TJ, Ryssel H, Berthelsen AK, Löfgren J, Loft A, Vogelius IR, Schnack T, Jakoby B, Kjaer A, Andersen FL, Fischer BM & Hansen AE (2021) Toward PET/MRI as one-stop shop for radiotherapy planning in cervical cancer patients. Acta Oncol. Aug;60(8):1045-1053. doi: https://doi.org/10.1080/0284186x.2021.1936164


Capobianco N, Sibille L, Chantadisai M, Gafita A, Langbein T, Platsch G, Solari EL, Shah V, Spottiswoode B, Eiber M, Weber WA, Navab N, Nekolla SG (2021) Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning. Eur J Nucl Med Mol Imaging. doi: https://doi.org/10.1007/s00259-021-05473-2. E 


Capobianco N, Meignan MA, Cottereau AS, Vercellino L, Sibille L, Spottiswoode B, Zuehlsdorff S, Casasnovas O, Thieblemont C, and Buvat I (2021) Deep learning FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma. J Nucl Med https://doi.org/10.2967/jnumed.120.242412

Nicolò wrote a summary of his publication that you can find here.


Chalampalakis ZStute S, Filipovic MSureau F, Comtat C (2021) Use of dynamic reconstruction for parametric Patlak imaging in dynamic whole body PET. Phys Med Biol 66 185017 https://doi.org/10.1088/1361-6560/ac2128


Iommi D, Valladares A, Figl M, Grahovac M, Fichtinger G, and Hummel J (2021) 3D ultrasound guided navigation system with hybrid image fusion. Sci Rep 11, 8838. https://doi.org/10.1038/s41598-021-86848-1


Kolinger GD, Vállez García D, Lohith TG, Hostetler ED, Sur C, Struyk A, Boellaard R, and Koole M (2021) A dual-time-window protocol to reduce acquisition time of dynamic tau PET imaging using [18F]MK-6240. EJNMMI Res 11, 49. https://doi.org/10.1186/s13550-021-00790-x


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


Nazari M, Kluge A, Apostolova I, Klutmann S, Kimiaei S, Schroeder M, and Buchert R (2021) Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05569-9


Nazari M, Jiménez-Franco LD, Schroeder M, Kluge A, Bronzel M, and Kimiaei S (2021) Automated and robust organ segmentation for 3D‑based internal dose calculation. EJNMMI Res 11, 53. https://doi.org/10.1186/s13550-021-00796-5


Notohamiprodjo S, Nekolla SG, Robu S, Villagran Asiares A, Kupatt C, Ibrahim T, Laugwitz K-L, Makowski MR, Schwaiger M, Weber WA, and Varasteh Z (2021) Imaging of cardiac fibroblast activation in a patient after acute myocardial infarction using 68Ga-FAPI-04. J. Nucl. Cardiol. https://doi.org/10.1007/s12350-021-02603-z


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. https://doi.org/10.1186/s40658-021-00364-9


Solari EL, Gafita A, Schachoff S, Bogdanović B, Villagran Asiares A, Amiel T, Hui W, Rauscher I, Visvikis D, Maurer T, Schwamborn K, Mustafa M, Weber W, Navab N, Eiber M, Hatt M, and Nekolla SG (2021) The added value of PSMA PET/MR radiomics for prostate cancer staging. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05430-z


Wallis D, Soussan M, Lacroix M, Akl P, Duboucher C, and Buvat I (2021) An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-021-05513-x




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


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


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


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.


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


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



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.


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.

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