Department of Radiology, Division of Thoracic Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Biomedical Imaging Center, Rensselaer Polytechnic Institute, New York, USA.
Phys Med. 2020 Nov;79:113-125. doi: 10.1016/j.ejmp.2020.11.012. Epub 2020 Nov 24.
Innovations in CT have been impressive among imaging and medical technologies in both the hardware and software domain. The range and speed of CT scanning improved from the introduction of multidetector-row CT scanners with wide-array detectors and faster gantry rotation speeds. To tackle concerns over rising radiation doses from its increasing use and to improve image quality, CT reconstruction techniques evolved from filtered back projection to commercial release of iterative reconstruction techniques, and recently, of deep learning (DL)-based image reconstruction. These newer reconstruction techniques enable improved or retained image quality versus filtered back projection at lower radiation doses. DL can aid in image reconstruction with training data without total reliance on the physical model of the imaging process, unique artifacts of PCD-CT due to charge sharing, K-escape, fluorescence x-ray emission, and pulse pileups can be handled in the data-driven fashion. With sufficiently reconstructed images, a well-designed network can be trained to upgrade image quality over a practical/clinical threshold or define new/killer applications. Besides, the much smaller detector pixel for PCD-CT can lead to huge computational costs with traditional model-based iterative reconstruction methods whereas deep networks can be much faster with training and validation. In this review, we present techniques, applications, uses, and limitations of deep learning-based image reconstruction methods in CT.
CT 领域的创新在硬件和软件领域的成像和医疗技术方面令人印象深刻。CT 扫描的范围和速度从多排探测器宽探测器和更快的机架旋转速度的多排探测器 CT 扫描仪的引入得到了改善。为了解决由于其使用增加而导致的辐射剂量上升的问题，并提高图像质量，CT 重建技术从滤波反投影发展到商业发布的迭代重建技术，最近还发展到基于深度学习（DL）的图像重建。这些较新的重建技术能够以较低的辐射剂量实现比滤波反投影更好或保留的图像质量。DL 可以在没有对成像过程的物理模型的完全依赖的情况下，使用训练数据辅助图像重建，PCD-CT 由于电荷共享、K 逃逸、荧光 X 射线发射和脉冲堆积而产生的独特伪影可以以数据驱动的方式处理。有了足够的重建图像，设计良好的网络可以经过训练，将图像质量提升到实际/临床阈值以上，或者定义新的/杀手级的应用。此外，对于 PCD-CT 来说，更小的探测器像素会导致传统基于模型的迭代重建方法的计算成本非常高，而深度网络经过训练和验证后可以更快。在这篇综述中，我们介绍了基于深度学习的 CT 图像重建方法的技术、应用、用途和局限性。
