Iterative Reconstruction

Iterative reconstruction methods aim to recover high-quality images from incomplete or noisy data, a crucial task in various imaging modalities like MRI, CT, and photoacoustic tomography. Current research emphasizes incorporating deep learning models, such as diffusion models, convolutional neural networks (CNNs), and generative adversarial networks (GANs), into iterative algorithms, often within a plug-and-play framework, to leverage the strengths of both data-driven and model-based approaches. This focus on hybrid methods improves reconstruction accuracy and efficiency, particularly in challenging scenarios like low-dose or sparse-view acquisitions. The resulting advancements have significant implications for reducing radiation exposure in medical imaging and enhancing the quality of images across diverse scientific and clinical applications.