Bousse Alexandre, Kandarpa Venkata Sai Sundar, Shi Kuangyu, Gong Kuang, Lee Jae Sung, Liu Chi, Visvikis Dimitris
Univ. Brest, LATIM, INSERM UMR 1101, 29238 Brest, France.
Lab for Artificial Intelligence & Translational Theranostics, Dept. Nuclear Medicine, Inselspital, University of Bern, 3010 Bern, Switzerland.
IEEE Trans Radiat Plasma Med Sci. 2024 Apr;8(4):333-347. doi: 10.1109/trpms.2023.3349194. Epub 2024 Jan 2.
Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the acquisition of functional information for various biological processes while minimizing the patient dose. However, the inherent randomness in the photon counting process is a source of noise which is amplified low-dose ET. This review article provides an overview of existing post-processing techniques, with an emphasis on deep neural network (NN) approaches. Furthermore, we explore future directions in the field of NN-based low-dose ET. This comprehensive examination sheds light on the potential of deep learning in enhancing the quality and resolution of low-dose ET images, ultimately advancing the field of medical imaging.
低剂量发射断层扫描（ET）在医学成像中起着至关重要的作用，它能够获取各种生物过程的功能信息，同时将患者剂量降至最低。然而，光子计数过程中固有的随机性是噪声的一个来源，在低剂量ET中这种噪声会被放大。这篇综述文章概述了现有的后处理技术，重点介绍了深度神经网络（NN）方法。此外，我们还探讨了基于NN的低剂量ET领域的未来发展方向。这一全面的研究揭示了深度学习在提高低剂量ET图像质量和分辨率方面的潜力，最终推动医学成像领域的发展。
