Quantitative Imaging

Quantitative imaging aims to extract precise, quantitative information about tissue properties from medical images, going beyond simple visual interpretation. Current research heavily utilizes deep learning, including Bayesian frameworks and neural networks informed by physical models of wave propagation, to accelerate and improve the accuracy of parameter estimation from various modalities like ultrasound, radar, and MRI. This focus on robust quantification, often incorporating uncertainty estimation, is crucial for improving diagnostic accuracy in applications such as cancer detection and liver disease assessment, and facilitates the development of more reliable and reproducible medical image analysis tools. The field is also actively developing open-source tools to streamline data processing and analysis, promoting wider adoption and collaboration.