Ce Mri

Contrast-enhanced magnetic resonance imaging (CE-MRI) is a crucial diagnostic tool, but its use can be limited by cost, health risks associated with contrast agents, and the time-consuming nature of manual analysis. Current research focuses on improving CE-MRI through several avenues: synthesizing CE-MRI images from safer, non-contrast modalities using deep learning techniques like deep evidential regression and attention mechanisms; developing more accurate and efficient automated analysis methods for existing CE-MRI data, employing architectures such as Kolmogorov-Arnold Networks and ResNet-50; and incorporating uncertainty quantification to improve the reliability of automated diagnoses. These advancements aim to enhance the accuracy, accessibility, and safety of CE-MRI for various applications, including brain tumor detection and liver tumor segmentation.