A Higher-Order Polynomial Method for SPECT Reconstruction
A Higher-Order Polynomial Method for SPECT Reconstruction
复制标题
SPECT 重建的高阶多项式方法
DOI：
10.1109/tmi.2018.2881919
复制
发表时间：
2019
影响因子：
10.6
通讯作者：
Xu Yuesheng
中科院分区：
文献类型：
--
作者：
Jiang Ying;Li Si;Xu Yuesheng
现有的单光子发射计算机层析成像(SPECT)重建方法大多基于离散模型，这些模型可以被视为连续数据采集过程的分段常数近似。由于分段常数逼近的精度阶数较低，传统的离散模型引入了不可约的模型误差，这是临床应用中提高重建图像质量的瓶颈。为了克服这一缺陷，我们提出了一种用于SPECT重建的高次多项式方法。具体地说，我们用积分方程模型表示SPECT成像的数据采集，用高阶分段多项式近似基础积分方程解，从而得到一个新的离散系统，并通过探索适合于近似的示踪剂分布的先验知识，为该系统引入两个新的正则化算子。与传统的基于离散模型的边缘重建方法相比，高次多项式方法在降低模型误差、抑制噪声和减少伪影方面都有明显的优势。特别是，与传统的基于离散模型的方法相比，分段线性多项式方法重建的图像的变异系数降低了10倍。
Existing single-photon emission computed tomography (SPECT) reconstruction methods are mostly based on discrete models that may be viewed as piecewise constant approximations of a continuous data acquisition process. Due to low accuracy order of piecewise constant approximations, a traditional discrete model introduces irreducible model errors which are a bottleneck of the quality improvement of reconstructed images in clinical applications. To overcome this drawback, we develop a higher-order polynomial method for SPECT reconstruction. Specifically, we represent the data acquisition of SPECT imaging by using an integral equation model, approximate the solution of the underlying integral equation by higher-order piecewise polynomials leading to a new discrete system and introduce two novel regularizers for the system, by exploring the a priori knowledge of the radiotracer distribution, suitable for the approximation. The proposed higher-order polynomial method outperforms significantly the cutting edge reconstruction method based on a traditional discrete model in terms of model error reduction, noise suppression, and artifact reduction. In particular, the coefficient of variation of images reconstructed by the piecewise linear polynomial method is reduced by a factor of 10 in comparison to that of a traditional discrete model-based method.