Stayman J W, Fessler J A
EECS Department, University of Michigan, Ann Arbor, 48109 USA.
IEEE Trans Med Imaging. 2000 Jun;19(6):601-15. doi: 10.1109/42.870666.
Traditional space-invariant regularization methods in tomographic image reconstruction using penalized-likelihood estimators produce images with nonuniform spatial resolution properties. The local point spread functions that quantify the smoothing properties of such estimators are space-variant, asymmetric, and object-dependent even for space-invariant imaging systems. We propose a new quadratic regularization scheme for tomographic imaging systems that yields increased spatial uniformity and is motivated by the least-squares fitting of a parameterized local impulse response to a desired global response. We have developed computationally efficient methods for PET systems with shift-invariant geometric responses. We demonstrate the increased spatial uniformity of this new method versus conventional quadratic regularization schemes in simulated PET thorax scans.
在使用惩罚似然估计器的断层图像重建中，传统的空间不变正则化方法会产生具有非均匀空间分辨率特性的图像。即使对于空间不变的成像系统，量化此类估计器平滑特性的局部点扩散函数也是空间可变、不对称且依赖于对象的。我们为断层成像系统提出了一种新的二次正则化方案，该方案可提高空间均匀性，其灵感来自于将参数化局部脉冲响应与期望的全局响应进行最小二乘拟合。我们已经为具有平移不变几何响应的PET系统开发了计算效率高的方法。在模拟的PET胸部扫描中，我们展示了这种新方法相对于传统二次正则化方案在空间均匀性方面的提升。
