| Abstrakt: |
Medical imaging is essential in contemporary healthcare as it assists in the identification of diseases, development of treatment strategies, and ongoing monitoring of patients. Over the years, deep learning (DL)techniques have emerged as a transformative force in medical image reconstruction, enabling the generation of high-quality images from noisy, incomplete, or under-sampled data. This review paper provides a comprehensive survey of recent advancements and applications of deep learning methods in medical image reconstruction. The key challenges in medical image reconstruction include issues related to reconstruction accuracy, noise sensitivity, and data limitation. A variety of deep learning models and their combinations are suitable for medical image reconstruction due to their unique capabilities, such as spatial hierarchy capture, adversarial learning, and other features, which allow them to address the complexities and challenges associated with medical image reconstruction. The paper analyses the key contributions of DL-based approaches in different imaging modalities, including computed tomography (CT) and magnetic resonance imaging (MRI). DL techniques are enabling image reconstruction in specialized medical fields like neuroimaging and cardiac imaging, but practitioners face challenges in training complex models and understanding their results. Finally, future research directions are suggested to improve the key limitations highlighted in this survey study. [ABSTRACT FROM AUTHOR] |
