Asma Evren, Leahy Richard M
Signal & Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA.
IEEE Trans Med Imaging. 2006 Jan;25(1):42-54. doi: 10.1109/TMI.2005.859716.
We derive computationally efficient methods for the estimation of the mean and variance properties of penalized likelihood dynamic positron emission tomography (PET) images. This allows us to predict the accuracy of reconstructed activity estimates and to compare reconstruction algorithms theoretically. We combine a bin-mode approach in which data is modeled as a collection of independent Poisson random variables at each spatiotemporal bin with the space-time separabilities in the imaging equation and penalties to derive rapidly computable analytic mean and variance approximations. We use these approximations to compare bias/variance properties of our dynamic PET image reconstruction algorithm with those of multiframe static PET reconstructions.
我们推导了用于估计惩罚似然动态正电子发射断层扫描(PET)图像均值和方差特性的计算高效方法。这使我们能够预测重建活动估计的准确性,并从理论上比较重建算法。我们将一种双模式方法相结合,在该方法中,数据在每个时空箱中被建模为独立泊松随机变量的集合,并结合成像方程中的时空可分离性和惩罚项,以得出快速可计算的解析均值和方差近似值。我们使用这些近似值来比较动态PET图像重建算法与多帧静态PET重建算法的偏差/方差特性。