Deep Learning-Based Image Reconstruction for Different Medical Imaging Modalities

作者：Muhammad Yaqub, Jinchao Feng, Kaleem Arshid, Shahzad Ahmed, Wenqian Zhang, Muhammad Zubair Nawaz, Tariq Mahmood · 发表于：Computational and Mathematical Methods in Medicine · 年份：2022 · DOI：10.1155/2022/8750648 · 被引用次数：66 · 研究领域：Medical Imaging Techniques and Applications、Medical Image Segmentation Techniques、Advanced X-ray and CT Imaging

Image reconstruction in magnetic resonance imaging (MRI) and computed tomography (CT) is a mathematical process that generates images at many different angles around the patient. Image reconstruction has a fundamental impact on image quality. In recent years, the literature has focused on deep learning and its applications in medical imaging, particularly image reconstruction. Due to the performance of deep learning models in a wide variety of vision applications, a considerable amount of work has recently been carried out using image reconstruction in medical images. MRI and CT appear as the ultimate scientifically appropriate imaging mode for identifying and diagnosing different diseases in this ascension age of technology. This study demonstrates a number of deep learning image reconstruction approaches and a comprehensive review of the most widely used different databases. We also give the challenges and promising future directions for medical image reconstruction.