T2 Weighted

T2-weighted imaging in magnetic resonance imaging (MRI) focuses on measuring the transverse relaxation time (T2), reflecting tissue properties crucial for diagnosis. Current research emphasizes improving T2 quantification accuracy and efficiency through advanced deep learning models, such as convolutional neural networks (CNNs) and transformer-based architectures, often integrated with model-based optimization techniques like nonlinear conjugate gradient methods. These advancements aim to accelerate acquisition times, enhance image quality, and enable more accurate and robust quantification of T2, ultimately improving the diagnostic capabilities of MRI in various clinical applications, including cardiac and neurological imaging.