Engineering Research Center of Metallurgical Automation and Measurement Technology, Wuhan University of Science and Technology, Wuhan, China.
School of Science, Computing and Engineering Technology, Swinburne University of Technology Melbourne, VIC, Australia.
PLoS One. 2021 Dec 2;16(12):e0260374. doi: 10.1371/journal.pone.0260374. eCollection 2021.
Low count PET (positron emission tomography) imaging is often desirable in clinical diagnosis and biomedical research, but its images are generally very noisy, due to the very weak signals in the sinograms used in image reconstruction. To address this issue, this paper presents a novel kernel graph filtering method for dynamic PET sinogram denoising. This method is derived from treating the dynamic sinograms as the signals on a graph, and learning the graph adaptively from the kernel principal components of the sinograms to construct a lowpass kernel graph spectrum filter. The kernel graph filter thus obtained is then used to filter the original sinogram time frames to obtain the denoised sinograms for PET image reconstruction. Extensive tests and comparisons on the simulated and real life in-vivo dynamic PET datasets show that the proposed method outperforms the existing methods in sinogram denoising and image enhancement of dynamic PET at all count levels, especially at low count, with a great potential in real life applications of dynamic PET imaging.
低计数 PET（正电子发射断层扫描）成像在临床诊断和生物医学研究中通常是需要的，但由于在图像重建中使用的正弦图中的信号非常弱，其图像通常非常嘈杂。为了解决这个问题，本文提出了一种用于动态 PET 正弦图去噪的新的核图滤波方法。该方法源自将动态正弦图视为图上的信号，并从正弦图的核主成分学习自适应图，以构建低通核图频谱滤波器。然后，将由此获得的核图滤波器用于滤波原始正弦图时间帧，以获得用于 PET 图像重建的去噪正弦图。在模拟和真实体内动态 PET 数据集上的广泛测试和比较表明，与现有方法相比，该方法在所有计数水平下都能更好地进行正弦图去噪和动态 PET 图像增强，特别是在低计数水平下，在动态 PET 成像的实际应用中具有很大的潜力。
