MultiRecon

MultiRecon aims at developing new image reconstruction techniques for multimodal medical imaging (PET/CT and PET/MRI) using machine learning. In multimodal imaging, current image reconstruction techniques reconstruct each modality independently. However, it is possible to exploit inter-modality information in order to “consolidate” the images to reduce noise and ultimately to reduce of the patient the dose. This information can be based on analytical models, but it can also be learned. In this project we explore machine and deep learning methods that can learn and exploit inter-modality information so that images can “talk to each other”.

MultiRecon is funded by the French National Research Agency (ANR) with grant number ANR-20-CE45-0020.

ISBI 2024
March 28, 2024

Two abstracts have been accepted to the 21st ΙΕΕΕ International Symposium on Biomedical Imaging that will be held...

New Publication
January 08, 2024

We are happy to announce that our paper entitled “A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising With...

New Publication
November 05, 2023

We are happy to announce that our paper entitled “Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the...