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.

New Publication
November 11, 2024

We are happy to announce that our paper entitled “Bimodal PET/MRI generative reconstruction based on VAE architectures” by...

New Poster
October 20, 2024

Marion Savanier will present a poster at the upcomming 2024 IEEE NSS-MIC conference in Tampa: “MRI-guided PET reconstruction...

EUSIPCO 2024
May 29, 2024

Two abstracts have been accepted to the 32nd European Signal Processing Conference (EUSIPCO) that will be held from 26/08/2024 until...