Dept. of Diagnostic Radiol., Yale Univ., New Haven, CT.
IEEE Trans Med Imaging. 1993;12(4):670-80. doi: 10.1109/42.251117.
Proposes a Bayesian method whereby maximum a posteriori (MAP) estimates of functional (PET and SPECT) images may be reconstructed with the aid of prior information derived from registered anatomical MR images of the same slice. The prior information consists of significant anatomical boundaries that are likely to correspond to discontinuities in an otherwise spatially smooth radionuclide distribution. The authors' algorithm, like others proposed recently, seeks smooth solutions with occasional discontinuities; the contribution here is the inclusion of a coupling term that influences the creation of discontinuities in the vicinity of the significant anatomical boundaries. Simulations on anatomically derived mathematical phantoms are presented. Although computationally intense in its current implication, the reconstructions are improved (ROI-RMS error) relative to filtered backprojection and EM-ML reconstructions. The simulations show that the inclusion of position-dependent anatomical prior Information leads to further improvement relative to Bayesian reconstructions without the anatomical prior. The algorithm exhibits a certain degree of robustness with respect to errors in the location of anatomical boundaries.
提出了一种贝叶斯方法，通过该方法可以借助于从同一切片的已注册解剖磁共振图像中提取的先验信息，对功能（PET 和 SPECT）图像进行最大后验（MAP）估计重建。先验信息包括可能对应于放射性核素分布在空间上平滑的其他情况下的不连续性的显著解剖边界。作者的算法与最近提出的其他算法一样，寻求具有偶尔不连续性的平滑解；这里的贡献是包含一个耦合项，该耦合项影响在显著解剖边界附近创建不连续性。本文还展示了基于解剖学的数学体模的仿真。尽管当前的算法实现计算强度较大，但与滤波反投影和 EM-ML 重建相比，重建结果得到了改善（感兴趣区域均方根误差）。仿真结果表明，与没有解剖先验信息的贝叶斯重建相比，包含位置相关的解剖先验信息会进一步提高重建效果。该算法在解剖边界位置的误差方面具有一定的稳健性。
