Learning Based Reconstruction

Learning-based reconstruction aims to improve the speed and quality of image reconstruction from incomplete or noisy data in various imaging modalities, such as MRI, CT, and PET. Current research emphasizes deep learning architectures, including U-Net variations, variational networks, and recurrent neural networks, often incorporating physics-based models into the reconstruction process (e.g., through back-projection or forward model integration) to enhance accuracy and generalization. These advancements offer significant potential for faster, higher-resolution imaging with reduced radiation exposure or scan times, impacting diverse fields from medical diagnostics to materials science.