Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
College of Science and Shanghai Institute of Intelligent Electronics and Systems, Donghua University, 24105 Songjiang District, Shanghai, China.
Comput Math Methods Med. 2022 Jun 16;2022:8750648. doi: 10.1155/2022/8750648. eCollection 2022.
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.
磁共振成像（MRI）和计算机断层扫描（CT）中的图像重建是一个数学过程，它可以在患者周围的许多不同角度生成图像。图像重建对图像质量有根本的影响。近年来，文献主要集中在深度学习及其在医学成像中的应用，特别是图像重建。由于深度学习模型在各种视觉应用中的性能，最近在医学图像的图像重建中进行了大量的工作。在这个技术进步的时代，MRI 和 CT 似乎是识别和诊断不同疾病的最科学的合适成像模式。本研究展示了一些深度学习图像重建方法，并对最广泛使用的不同数据库进行了全面回顾。我们还给出了医学图像重建的挑战和有前途的未来方向。
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