换一批摘要随着PET显像的蓬勃发展与广泛应用，其重建算法也得到了显著改进。目前，深度学习（DL）在高维和高度复杂的数据中表现出卓越的学习能力，在提高PET图像重建的速度、准确性等方面显示出巨大潜力，其已被证实在PET图像重建中具有良好的发展前景。笔者就国内外对DL在静态PET图像重建中的最新研究进展进行综述。
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abstractsWith the vigorous development and wide application of PET imaging, its reconstruction algorithms have been significantly improved. Recently, deep learning (DL) has demonstrated excellent learning ability in high-dimensional and highly complex data, showing great potential in improving the speed and accuracy of PET image reconstruction. DL has been proven to have promising development prospects in PET image reconstruction. The authors review the latest research progress of DL in static PET image reconstruction at home and abroad.
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关键词
图像处理，计算机辅助深度学习正电子发射断层显像术卷积神经网络Image processing, computer-assistedDeep learningPositron-emission tomographyConvolutional neural network
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DOI
10.3760/cma.j.cn121381-202407011-00561
发布时间
2025-10-25（万方平台首次上网日期，不代表论文的发表时间）
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