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基于 infimal 卷积的 SPECT 重建正则化。

Infimal convolution-based regularization for SPECT reconstruction.

机构信息

Department of Radiation Oncology, Duke University Medical Center, Durham, NC, 27713, USA.

School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, 510006, China.

出版信息

Med Phys. 2018 Dec;45(12):5397-5410. doi: 10.1002/mp.13226. Epub 2018 Oct 25.

Abstract

PURPOSE

Total variation (TV) regularization is efficient in suppressing noise, but is known to suffer from staircase artifacts. The goal of this work was to develop a regularization method using the infimal convolution of the first- and the second-order derivatives to reduce or even prevent staircase artifacts in the reconstructed images, and to investigate if the advantage in noise suppression by this TV-type regularization can be translated into dose reduction.

METHODS

In the present work, we introduce the infimal convolution of the first- and the second-order total variation (ICTV) as the regularization term in penalized maximum likelihood reconstruction. The preconditioned alternating projection algorithm (PAPA), previously developed by the authors of this article, was employed to produce the reconstruction. Using Monte Carlo-simulated data, we evaluate noise properties and lesion detectability in the reconstructed images and compare the results with conventional total variation (TV) and clinical EM-based methods with Gaussian post filter (GPF-EM). We also evaluate the quality of ICTV regularized images obtained for lower photon number data, compared with clinically used photon number, to verify the feasibility of radiation-dose reduction to patients by use of the ICTV reconstruction method.

RESULTS

By comparison with GPF-EM reconstructed images, we have found that the ICTV-PAPA method can achieve a lower background variability level while maintaining the same level of contrast. Images reconstructed by the ICTV-PAPA method with 80,000 counts per view exhibit even higher channelized Hotelling observer (CHO) signal-to-noise ratio (SNR), as compared to images reconstructed by the GPF-EM method with 120,000 counts per view.

CONCLUSIONS

In contrast to the TV-PAPA method, the ICTV-PAPA reconstruction method avoids substantial staircase artifacts, while producing reconstructed images with higher CHO SNR and comparable local spatial resolution. Simulation studies indicate that a 33% dose reduction is feasible by switching to the ICTV-PAPA method, compared with the GPF-EM clinical standard.

摘要

目的

全变差(Total Variation,TV)正则化在抑制噪声方面非常有效,但已知会出现阶梯伪影。本工作的目的是开发一种使用一阶和二阶导数的 infimal 卷积的正则化方法,以减少甚至防止重建图像中的阶梯伪影,并研究这种 TV 型正则化在噪声抑制方面的优势是否可以转化为剂量降低。

方法

在本工作中,我们将一阶和二阶全变差(ICTV)的 infimal 卷积引入到惩罚最大似然重建的正则项中。作者先前开发的预条件交替投影算法(PAPA)被用于产生重建。使用蒙特卡罗模拟数据,我们评估了重建图像中的噪声特性和病灶可检测性,并将结果与传统的全变差(TV)和具有高斯后滤波器(GPF-EM)的临床 EM 方法进行比较。我们还评估了对于较低光子数数据获得的 ICTV 正则化图像的质量,与临床使用的光子数进行比较,以验证通过使用 ICTV 重建方法降低患者辐射剂量的可行性。

结果

与 GPF-EM 重建图像相比,我们发现 ICTV-PAPA 方法可以在保持相同对比度的同时实现更低的背景变异性水平。与使用 120,000 个计数/视图重建的 GPF-EM 方法相比,使用 80,000 个计数/视图重建的 ICTV-PAPA 方法甚至可以获得更高的通道化 Hotelling 观察者(CHO)信噪比(SNR)。

结论

与 TV-PAPA 方法相比,ICTV-PAPA 重建方法避免了明显的阶梯伪影,同时产生了具有更高 CHO SNR 和可比局部空间分辨率的重建图像。模拟研究表明,与 GPF-EM 临床标准相比,切换到 ICTV-PAPA 方法可以降低 33%的剂量。

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