HYBRID 2017-2021

We are part of the H2020-MSCA-ITN-2017 HYBRID (Healthcare Yearns for Bright Researchers for Imaging Data, 2017-2021) project in collaboration with 10 partners from Germany, Austria, United Kingdom, the Netherlands, Danemark, and Belgium.

The HYBRID project supports 15 PhD students that are all working towards more powerful in vivo molecular imaging, especially involving PET/MR, for personnalised medicine, using the most advanced technologies and processing approaches, including dynamic imaging and artificial intelligence approaches. To know more about the HYBRID project, please go to the dedicated web site.

People involved in the lab : David Wallis, Irène Buvat (responsible)

Publications :

  1. Wallis D, Buvat I. Clever Hans effect found in a widely used brain tumour MRI dataset. Med Image Anal. 77:102368, 2022. DOI: 10.1016/j.media.2022.102368
  2. Wallis D, Soussan M, Lacroix M, Akl P, Duboucher C, Buvat I. An 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. 49(3):881-888, 2022. DOI: https://doi.org/10.1007/s00259-021-05513-x
  3. Dal Toso L, Chalampalakis Z, Buvat I, Comtat C, Cook G, Goh V, Schnabel JA, Marsden PK. Improved 3D Tumour Definition and Quantification of Uptake in Simulated Lung Tumours Using Deep Learning. Phys Med Biol. 67:095013, 2022. DOI: 10.1088/1361-6560/ac65d6
  4. Kolinger GD, García DV, Kramer GM, Frings V, Zwezerijnen GJC, Smit EF, De Langen AJ, Buvat I, Boellaard R. Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics. J Nucl Med. 63(6):919-924, 2022. DOI: 10.2967/jnumed.121.262660
  5. Capobianco N, Meignan M, Cottereau AS, Vercellino L, Sibille L, Spottiswoode B, Zuehlsdorff S, Casasnovas O, Thieblemont C, Buvat I. Deep learning FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma. J Nucl Med. 62: 30-36, 2021. DOI: 10.2967/jnumed.120.242412
  6. Sundar LKS , Muzik O, Buvat I, Bidaut L, Beyer T. Potentials and caveats of artificial intelligence in hybrid imaging. Methods. 188: 4-19, 2021. DOI: 10.1016/j.ymeth.2020.10.004
  7. Beyer T, Bailey DL, Birk UJ, Buvat I, Catana C, Cheng Z, Fang Q, Giove F, Kuntner C, Laistler E, Moscato F, Nekolla SG, Rausch I, Ronen I, van Elmpt W, Saarakkala S, Thielemans K, Moser E. Medical Physics and Imaging – A timely perspective. Frontiers in Physics. 9:634693, 2021. DOI: https://doi.org/10.3389/fphy.2021.634693