Parametric Map

Parametric maps represent quantitative images encoding multiple tissue properties, crucial for medical diagnosis and scientific understanding. Current research focuses on efficiently generating these maps from undersampled or incomplete data using deep learning, employing architectures like convolutional neural networks and autoencoders, often incorporating regularization techniques such as tensor decomposition to improve accuracy and speed. These advancements enable faster and more robust generation of high-fidelity parametric maps, improving the efficiency and diagnostic capabilities of various medical imaging modalities, particularly MRI.