Kim Soo Mee, Alessio Adam M, De Man Bruno, Kinahan Paul E
Department of Radiology, University of Washington, Seattle, WA 98185, USA, telephone: +1-206-543-0236.
Image Reconstruction Laboratory, General Electric Global Research Center, Niskayuna, NY 12309, USA.
IEEE Trans Nucl Sci. 2017 Mar;64(3):959-968. doi: 10.1109/TNS.2017.2654680. Epub 2017 Jan 17.
Extremely low-dose CT acquisitions used for PET attenuation correction have high levels of noise and potential bias artifacts due to photon starvation. This work explores the use of knowledge for iterative image reconstruction of the CT-based attenuation map. We investigate a maximum framework with cluster-based multinomial penalty for direct iterative coordinate decent (dICD) reconstruction of the PET attenuation map. The objective function for direct iterative attenuation map reconstruction used a Poisson log-likelihood data fit term and evaluated two image penalty terms of spatial and mixture distributions. The spatial regularization is based on a quadratic penalty. For the mixture penalty, we assumed that the attenuation map may consist of four material clusters: air+background, lung, soft tissue, and bone. Using simulated noisy sinogram data, dICD reconstruction was performed with different strengths of the spatial and mixture penalties. The combined spatial and mixture penalties reduced the RMSE by roughly 2 times compared to a weighted least square and filtered backprojection reconstruction of CT images. The combined spatial and mixture penalties resulted in only slightly lower RMSE compared to a spatial quadratic penalty alone. For direct PET attenuation map reconstruction from ultra-low dose CT acquisitions, the combination of spatial and mixture penalties offers regularization of both variance and bias and is a potential method to reconstruct attenuation maps with negligible patient dose. The presented results, using a best-case histogram suggest that the mixture penalty does not offer a substantive benefit over conventional quadratic regularization and diminishes enthusiasm for exploring future application of the mixture penalty.
用于PET衰减校正的极低剂量CT采集由于光子饥饿而具有高水平的噪声和潜在的偏置伪影。这项工作探索了利用知识对基于CT的衰减图进行迭代图像重建。我们研究了一种具有基于聚类的多项式惩罚的最大框架，用于PET衰减图的直接迭代坐标下降（dICD）重建。直接迭代衰减图重建的目标函数使用了泊松对数似然数据拟合项，并评估了空间和混合分布的两个图像惩罚项。空间正则化基于二次惩罚。对于混合惩罚，我们假设衰减图可能由四个物质聚类组成：空气+背景、肺、软组织和骨骼。使用模拟的噪声正弦图数据，以不同强度的空间和混合惩罚进行dICD重建。与CT图像的加权最小二乘和滤波反投影重建相比，空间和混合惩罚的组合将均方根误差（RMSE）降低了约2倍。与单独的空间二次惩罚相比，空间和混合惩罚的组合导致的RMSE仅略低。对于从超低剂量CT采集中直接重建PET衰减图，空间和混合惩罚的组合提供了方差和偏差的正则化，并且是一种在患者剂量可忽略不计的情况下重建衰减图的潜在方法。使用最佳情况直方图给出的结果表明，混合惩罚相对于传统的二次正则化没有提供实质性的好处，并且降低了探索混合惩罚未来应用的热情。
