School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, People's Republic of China.
Guangdong Artificial Intelligence and Digital Economy Laboratory Guangzhou, Guangzhou 510335, People's Republic of China.
Phys Med Biol. 2022 May 31;67(11). doi: 10.1088/1361-6560/ac6d9b.
The radiation dose of cerebral perfusion computed tomography (CPCT) imaging can be reduced by lowering the milliampere-second or kilovoltage peak. However, dose reduction can decrease image quality due to excessive x-ray quanta fluctuation and reduced detector signal relative to system electronic noise, thereby influencing the accuracy of hemodynamic parameters for patients with acute stroke. Existing low-dose CPCT denoising methods, which mainly focus on specific temporal and spatial prior knowledge in low-dose CPCT images, not take the noise distribution characteristics of low-dose CPCT images into consideration. In practice, the noise of low-dose CPCT images can be much more complicated. This study first investigates the noise properties in low-dose CPCT images and proposes a perfusion deconvolution model based on the noise properties.To characterize the noise distribution in CPCT images properly, we analyze noise properties in low-dose CPCT images and find that the intra-frame noise distribution may vary in the different areas and the inter-frame noise also may vary in low-dose CPCT images. Thus, we attempt the first-ever effort to model CPCT noise with a non-independent and identical distribution (i.i.d.) mixture-of-Gaussians (MoG) model for noise assumption. Furthermore, we integrate the noise modeling strategy into a perfusion deconvolution model and present a novel perfusion deconvolution method by using self-relative structural similarity information and MoG model (named as SR-MoG) to estimate the hemodynamic parameters accurately. In the presented SR-MoG method, the self-relative structural similarity information is obtained from preprocessed low-dose CPCT images.The results show that the presented SR-MoG method can achieve promising gains over the existing deconvolution approaches. In particular, the average root-mean-square error (RMSE) of cerebral blood flow (CBF), cerebral blood volume, and mean transit time was improved by 40.3%, 69.1%, and 40.8% in the digital phantom study, and the average RMSE of CBF can be improved by 81.0% in the clinical data study, compared with tensor total variation regularization deconvolution method.The presented SR-MoG method can estimate high-accuracy hemodynamic parameters andachieve promising gains over the existing deconvolution approaches.
脑灌注 CT（CPCT）成像的辐射剂量可以通过降低毫安秒或千伏峰值来降低。然而，剂量降低会导致图像质量下降，因为 X 射线量子的波动过大，探测器信号相对于系统电子噪声减少，从而影响急性脑卒中患者的血流动力学参数的准确性。现有的低剂量 CPCT 去噪方法主要集中在低剂量 CPCT 图像的特定时空先验知识上，没有考虑低剂量 CPCT 图像的噪声分布特征。在实践中，低剂量 CPCT 图像的噪声可能更加复杂。本研究首先研究了低剂量 CPCT 图像中的噪声特性，并提出了一种基于噪声特性的灌注反卷积模型。为了正确描述 CPCT 图像中的噪声分布，我们分析了低剂量 CPCT 图像中的噪声特性，发现帧内噪声分布在不同区域可能不同，帧间噪声在低剂量 CPCT 图像中也可能不同。因此，我们首次尝试使用非独立同分布（i.i.d.）混合高斯（MoG）模型对 CPCT 噪声进行建模。此外，我们将噪声建模策略集成到灌注反卷积模型中，并提出了一种新的灌注反卷积方法，该方法通过使用自相关结构相似性信息和 MoG 模型（称为 SR-MoG）来准确估计血流动力学参数。在提出的 SR-MoG 方法中，自相关结构相似性信息是从预处理的低剂量 CPCT 图像中获得的。结果表明，与现有的反卷积方法相比，提出的 SR-MoG 方法可以取得有希望的增益。特别是，在数字体模研究中，脑血流量（CBF）、脑血容量和平均通过时间的平均均方根误差（RMSE）分别提高了 40.3%、69.1%和 40.8%，在临床数据研究中，与张量全变分正则化反卷积方法相比，CBF 的平均 RMSE 可以提高 81.0%。提出的 SR-MoG 方法可以估计高精度的血流动力学参数，并在现有的反卷积方法中取得有希望的增益。
