论文标题
心脏磁共振中基于人工智能的图像重建
Artificial Intelligence-Based Image Reconstruction in Cardiac Magnetic Resonance
论文作者
论文摘要
人工智能（AI）和机器学习（ML）在改善医学成像工作流程中显示出很大的潜力，从图像获取和重建到疾病诊断和治疗。特别是，近年来，用于医学图像重建的AI和ML算法（尤其是基于深度学习（DL）的方法）的使用情况有了显着增长。就重建质量和计算效率而言，DL技术已证明具有竞争力，并且通常比常规重建方法优越。基于DL的图像重建的使用还为改变心脏图像的获取和重建方式提供了有希望的机会。在本章中，我们将回顾用于心脏成像的基于DL的重建技术的最新进展，重点是心脏磁共振（CMR）图像重建。我们主要专注于用于应用程序的监督DL方法，包括图像后处理技术，模型驱动方法和基于K空间的方法。还讨论了DL对心脏图像重建的当前局限性，挑战和未来机会。
Artificial intelligence (AI) and Machine Learning (ML) have shown great potential in improving the medical imaging workflow, from image acquisition and reconstruction to disease diagnosis and treatment. Particularly, in recent years, there has been a significant growth in the use of AI and ML algorithms, especially Deep Learning (DL) based methods, for medical image reconstruction. DL techniques have shown to be competitive and often superior over conventional reconstruction methods in terms of both reconstruction quality and computational efficiency. The use of DL-based image reconstruction also provides promising opportunities to transform the way cardiac images are acquired and reconstructed. In this chapter, we will review recent advances in DL-based reconstruction techniques for cardiac imaging, with emphasis on cardiac magnetic resonance (CMR) image reconstruction. We mainly focus on supervised DL methods for the application, including image post-processing techniques, model-driven approaches and k-space based methods. Current limitations, challenges and future opportunities of DL for cardiac image reconstruction are also discussed.
