Deep Learning-Based Denoising of CEST MR Data: A Feasibility Study on Applying Synthetic Phantoms in Medical Imaging.

Chemical Exchange Saturation Transfer (CEST) magnetic resonance imaging (MRI) provides a novel method for analyzing biomolecule concentrations in tissues without exogenous contrast agents. Despite its potential, achieving a high signal-to-noise ratio (SNR) is imperative for detecting small CEST effects. Traditional metrics such as Magnetization Transfer Ratio Asymmetry (MTR) and Lorentzian analyses are vulnerable to image noise, hampering their precision in quantitative concentration estimations.

Development of river ecosystem models for Flemish watercourses: case studies in the Zwalm river basin.

Only recently, modelling has been accepted as an interesting and powerful tool to support river quality assessment and management. The 'River Invertebrate Prediction and Classification System' (RIVPACS), based on statistical modelling, was one of the first and best known systems in this context. RIVPACS was developed to classify macroinvertebrate community types and to predict the fauna expected to occur in different types of watercourses, based on a small number of environmental variables.