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

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 2...

Full description

Saved in:
Bibliographic Details
Published in:Journal of neuroradiology 2024-06, Vol.51 (4), p.101163, Article 101163
Main Authors: Pouliquen, Geoffroy, Debacker, Clément, Charron, Sylvain, Roux, Alexandre, Provost, Corentin, Benzakoun, Joseph, de Graaf, Wolter, Prevost, Valentin, Pallud, Johan, Oppenheim, Catherine
Format: Article
Language:English
Subjects:
Brain tumors
Cancer
Deep learning
Denoising
Diffusion tensor imaging
Life Sciences
Magnetic resonance imaging
Neurons and Cognition
Relaxometry
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0150-9861
DOI:10.1016/j.neurad.2023.10.008