Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors - Inserm - Institut national de la santé et de la recherche médicale Accéder directement au contenu
Article Dans Une Revue Journal de Neuroradiologie / Journal of Neuroradiology Année : 2023

Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors

Résumé

The use of relaxometry and Diffusion-Tensor Imaging sequences for brain tumor assessment is limited by their long acquisition time. We aim to test the effect of a denoising algorithm based on a Deep Learning Reconstruction (DLR) technique on quantitative MRI parameters while reducing scan time. In 22 consecutive patients with brain tumors, DLR applied to fast and noisy MR sequences preserves the mean values of quantitative parameters (fractional anisotropy, mean diffusivity, T1 and T2-relaxation time) and produces maps with higher structural similarity compared to long duration sequences. This could promote wider use of these biomarkers in clinical setting.
Fichier principal
Vignette du fichier
Pouliquen et al (Oppenheim) NEURAD.pdf (1.67 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

inserm-04477234 , version 1 (26-02-2024)

Identifiants

Citer

Geoffroy Pouliquen, Clément Debacker, Sylvain Charron, Alexandre Roux, Corentin Provost, et al.. Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors. Journal de Neuroradiologie / Journal of Neuroradiology, 2023, ⟨10.1016/j.neurad.2023.10.008⟩. ⟨inserm-04477234⟩

Collections

INSERM UP-SANTE
4 Consultations
6 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More