Department of Radiology, The Johns Hopkins University, Baltimore, MD 21287, USA.
Phys Med Biol. 2009 Dec 7;54(23):7063-75. doi: 10.1088/0031-9155/54/23/002. Epub 2009 Nov 11.
We developed a maximum a posterior (MAP) reconstruction method for positron emission tomography (PET) image reconstruction incorporating magnetic resonance (MR) image information, with the joint entropy between the PET and MR image features serving as the regularization constraint. A non-parametric method was used to estimate the joint probability density of the PET and MR images. Using realistically simulated PET and MR human brain phantoms, the quantitative performance of the proposed algorithm was investigated. Incorporation of the anatomic information via this technique, after parameter optimization, was seen to dramatically improve the noise versus bias tradeoff in every region of interest, compared to the result from using conventional MAP reconstruction. In particular, hot lesions in the FDG PET image, which had no anatomical correspondence in the MR image, also had improved contrast versus noise tradeoff.Corrections were made to figures 3, 4 and 6, and to the second paragraph of section 3.1 on 13 November 2009. The corrected electronic version is identical to the print version.
我们开发了一种基于最大后验 (MAP) 的重建方法，用于将磁共振 (MR) 图像信息纳入正电子发射断层扫描 (PET) 图像重建中，将 PET 和 MR 图像特征之间的联合熵作为正则化约束。我们使用非参数方法来估计 PET 和 MR 图像的联合概率密度。使用真实模拟的 PET 和 MR 人脑体模，研究了所提出算法的定量性能。与使用传统 MAP 重建的结果相比，通过该技术纳入解剖学信息（经过参数优化），在每个感兴趣区域都显著改善了噪声与偏差之间的权衡。特别是，在 FDG PET 图像中没有解剖对应物的热点病变，其对比度与噪声之间的权衡也得到了改善。对图 3、4 和 6 进行了修正，并于 2009 年 11 月 13 日修正了第 3.1 节第二段的内容。修正后的电子版与印刷版完全一致。
