Use Of Relaxometry Research Articles

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 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.

Expanding connectomics to the laminar level: A perspective.

Despite great progress in uncovering the complex connectivity patterns of the human brain over the last two decades, the field of connectomics still experiences a bias in its viewpoint of the cerebral cortex. Due to a lack of information regarding exact end points of fiber tracts inside cortical gray matter, the cortex is commonly reduced to a single homogenous unit. Concurrently, substantial developments have been made over the past decade in the use of relaxometry and particularly inversion recovery imaging for exploring the laminar microstructure of cortical gray matter. In recent years, these developments have culminated in an automated framework for cortical laminar composition analysis and visualization, followed by studies of cortical dyslamination in epilepsy patients and age-related differences in laminar composition in healthy subjects. This perspective summarizes the developments and remaining challenges of multi-T1 weighted imaging of cortical laminar substructure, the current limitations in structural connectomics, and the recent progress in integrating these fields into a new model-based subfield termed 'laminar connectomics'. In the coming years, we predict an increased use of similar generalizable, data-driven models in connectomics with the purpose of integrating multimodal MRI datasets and providing a more nuanced and detailed characterization of brain connectivity.

The use of relaxometry to evaluate the aging process in hybrid HIPS nanocomposites

Nuclear magnetic resonance allows measurement of proton spin-lattice relaxation times, which reflect the molecular mobility of the sample according to the changes in its chemical structure caused by different mechanisms that can be induced. Nanomaterials and their derivatives are good systems to investigate through NMR relaxometry due to their particular organization and intermolecular interaction. In this study, NMR relaxometry was employed to evaluate UV aging of HIPS nanomaterials formed by the solution method. The results obtained were quite interesting. Regardless of organoclay amount, the aging effect occurred slowly up to 200 h of UV light exposure. However, at 300 h of UV light exposure, competition between chain degradation and chain recombination occurred, and at 400 h the aging effect increased the chain scission.