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WP2 Cardio-thoracic imaging


Cardio-thoracic imaging has shown tremendous progress over the last decade. Rapid (highly subsampled) acquisition techniques and optimized reconstruction algorithms addressed cardiac contraction and respiratory motion issues. Thus, high-resolution 3D cardiac and lung MRI, in complement to CT imaging, has opened up new possibilities for better description of tissue alteration and better therapeutic follow-up.

In this perspective, the WP2 teams aim to develop a new contrast at an early step (WP2-1) while stimulating AI-based image processing to help the methods already available (WP2-2).

WP2-1: Developing manganese-enhanced imaging for heart and lung imaging

  • General objectives – to develop manganese-based MRI for both pulmonary and cardiac imaging and evaluate them in animal models.
  • WP leaders – Yannick Cremillieux (ISM) ; Aurélien Trotier, Julie Magat (CRMSB/IHU liryc)
  • PhD candidateAlexis Rotondi (supervisors : Bruno Quesson, Dounia El Hamrani)
  • Engineer – Georgina Jesuthasan (supervisor : Yannick Crémillieux)

WP2-2: Deep learning to improve MR lung imaging

  • General objectives:
    • to develop an AI algorithm to enable CT-like imaging quality from lung MRI with ultrashort echo times and to generalize the developed algorithm to images from the main MR manufacturers
    • to set AI-based quantifications in such synthetic lung MRI images 
    • to assess the clinical validity of the developed methods in a cohort of patients with chronic airway disease, both cross-sectionally and longitudinally. 
  • WP leaders – Gaël Dournes, François Laurent, Patrick Berger, Llyes Benlala (CRCTB) ; Fabien Baldacci (LaBRI) ; Baudoin Denis de Senneville (IMB)
  • PhD candidate – Amèle Imène Hadj Bouzid (surpervisor : Gaël Dournes)

Research staff