| Title: |
Deep learning-based noise reduction preserves quantitative MRI biomarkers in patients with brain tumors |
| Authors: |
Pouliquen, Geoffroy; Debacker, Clément; Charron, Sylvain; Roux, Alexandre; Provost, Corentin; Benzakoun, Joseph; de Graaf, Wolter; Prevost, Valentin; Pallud, Johan; Oppenheim, Catherine |
| Contributors: |
Groupe hospitalier universitaire Paris psychiatrie & neurosciences Paris (GHU Paris Psychiatrie et Neurosciences); Centre Hospitalier Sainte Anne Paris; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP); Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm); Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité); Canon Medical Systems Europe The Netherlands (CMSE); Canon Medical Systems Corporation |
| Source: |
ISSN: 0150-9861 ; Journal de Neuroradiologie / Journal of Neuroradiology ; https://inserm.hal.science/inserm-04477234 ; Journal de Neuroradiologie / Journal of Neuroradiology, 2023, ⟨10.1016/j.neurad.2023.10.008⟩. |
| Publisher Information: |
CCSD; Elsevier Masson |
| Publication Year: |
2023 |
| Subject Terms: |
[SDV.CAN]Life Sciences [q-bio]/Cancer; [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC] |
| Description: |
International audience ; 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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1016/j.neurad.2023.10.008 |
| Availability: |
https://inserm.hal.science/inserm-04477234; https://inserm.hal.science/inserm-04477234v1/document; https://inserm.hal.science/inserm-04477234v1/file/Pouliquen%20et%20al%20%28Oppenheim%29%20NEURAD.pdf; https://doi.org/10.1016/j.neurad.2023.10.008 |
| Rights: |
info:eu-repo/semantics/OpenAccess |
| Accession Number: |
edsbas.3A2AC13F |
| Database: |
BASE |