Imaging Physics Laboratory, Brain and Mind Centre, Camperdown, NSW 2050, Australia. School of Biomedical Engineering, Faculty of Engineering, The University of Sydney, Sydney, Australia.
Phys Med Biol. 2021 Jan 26;66(3):034001. doi: 10.1088/1361-6560/abcdea.
The quality of reconstructed dynamic PET images, as well as the statistical reliability of the estimated pharmacokinetic parameters is often compromised by high levels of statistical noise, particularly at the voxel level. Many denoising strategies have been proposed, both in the temporal and spatial domain, which substantially improve the signal to noise ratio of the reconstructed dynamic images. However, although most filtering approaches are fairly successful in reducing the spatio-temporal inter-voxel variability, they may also average out or completely eradicate the critically important temporal signature of a transient neurotransmitter activation response that may be present in a non-steady state dynamic PET study. In this work, we explore an approach towards temporal denoising of non-steady state dynamic PET images using an artificial neural network, which was trained to identify the temporal profile of a time-activity curve, while preserving any potential activation response. We evaluated the performance of a feed-forward perceptron neural network to improve the signal to noise ratio of dynamic [C]raclopride activation studies and compared it with the widely used highly constrained back projection (HYPR) filter. Results on both simulated Geant4 Application for Tomographic Emission data of a realistic rat brain phantom and experimental animal data of a freely moving animal study showed that the proposed neural network can efficiently improve the noise characteristics of dynamic data in the temporal domain, while it can lead to a more reliable estimation of voxel-wise activation response in target region. In addition, improvements in signal-to-noise ratio achieved by denoising the dynamic data using the proposed neural network led to improved accuracy and precision of the estimated model parameters of the lp-ntPET model, compared to the HYPR filter. The performance of the proposed denoising approach strongly depends on the amount of noise in the dynamic PET data, with higher noise leading to substantially higher variability in the estimated parameters of the activation response. Overall, the feed-forward network led to a similar performance as the HYPR filter in terms of spatial denoising, but led to notable improvements in terms of temporal denoising, which in turn improved the estimation activation parameters.
重建动态 PET 图像的质量以及估计的药代动力学参数的统计可靠性常常受到高水平统计噪声的影响，特别是在体素水平。已经提出了许多降噪策略，包括在时域和空域中，这些策略大大提高了重建动态图像的信噪比。然而，尽管大多数滤波方法在降低体素间时空变异性方面非常成功，但它们也可能平均或完全消除瞬态神经递质激活反应的重要时间特征，这种反应可能存在于非稳态动态 PET 研究中。在这项工作中，我们探索了一种使用人工神经网络对非稳态动态 PET 图像进行时间降噪的方法，该网络经过训练可以识别时间活动曲线的时间轮廓，同时保留任何潜在的激活反应。我们评估了前馈感知器神经网络对提高动态 [C]raclopride 激活研究的信噪比的性能，并将其与广泛使用的高度约束反向投影（HYPR）滤波器进行了比较。在真实大鼠脑模型的模拟 Geant4 断层发射数据和自由移动动物研究的实验动物数据上的结果表明，所提出的神经网络可以有效地改善动态数据在时域中的噪声特性，同时可以更可靠地估计目标区域中体素的激活反应。此外，与 HYPR 滤波器相比，使用所提出的神经网络对动态数据进行降噪可以提高 lp-ntPET 模型的估计模型参数的准确性和精度。与 HYPR 滤波器相比，所提出的降噪方法的性能强烈依赖于动态 PET 数据中的噪声量，噪声越高，激活反应的估计参数的变异性就越大。总体而言，前馈网络在空间降噪方面的性能与 HYPR 滤波器相似，但在时间降噪方面有显著提高，从而改善了激活参数的估计。
