Lasio Giovanni M, Whiting Bruce R, Williamson Jeffrey F
Division of Medical Physics, Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA.
Phys Med Biol. 2007 Apr 21;52(8):2247-66. doi: 10.1088/0031-9155/52/8/014. Epub 2007 Apr 2.
Statistical image reconstruction (SR) algorithms have the potential to significantly reduce x-ray CT image artefacts because they use a more accurate model than conventional filtered backprojection and can incorporate effects such as noise, incomplete data and nonlinear detector response. Most SR algorithms assume that the CT detectors are photon-counting devices and generate Poisson-distributed signals. However, actual CT detectors integrate energy from the x-ray beam and exhibit compound Poisson-distributed signal statistics. This study presents the first assessment of the impact on image quality of the resultant mismatch between the detector and signal statistics models assumed by the sinogram data model and the reconstruction algorithm. A 2D CT projection simulator was created to generate synthetic polyenergetic transmission data assuming (i) photon-counting with simple Poisson-distributed signals and (ii) energy-weighted detection with compound Poisson-distributed signals. An alternating minimization (AM) algorithm was used to reconstruct images from the data models (i) and (ii) for a typical abdominal scan protocol with incident particle fluence levels ranging from 10(5) to 1.6 x 10(6) photons/detector. The images reconstructed from data models (i) and (ii) were compared by visual inspection and image-quality figures of merit. The reconstructed image quality degraded significantly when the means were mismatched from the assumed model. However, if the signal means are appropriately modified, images from data models (i) and (ii) did not differ significantly even when SNR is very low. While data-mean mismatches characteristic of the difference between particle-fluence and energy-fluence transmission can cause significant streaking and cupping artefacts, the mismatch between the actual and assumed CT detector signal statistics did not significantly degrade image quality once systematic data means mismatches were corrected.
统计图像重建（SR）算法有潜力显著减少X射线CT图像伪影，因为它们使用了比传统滤波反投影更精确的模型，并且可以纳入噪声、数据不完整和探测器非线性响应等影响因素。大多数SR算法假定CT探测器是光子计数设备，并生成泊松分布信号。然而，实际的CT探测器会对X射线束的能量进行积分，并呈现复合泊松分布的信号统计特性。本研究首次评估了由正弦图数据模型和重建算法所假定的探测器与信号统计模型之间的不匹配对图像质量的影响。创建了一个二维CT投影模拟器，以生成合成的多能透射数据，假定（i）具有简单泊松分布信号的光子计数，以及（ii）具有复合泊松分布信号的能量加权检测。使用交替最小化（AM）算法从数据模型（i）和（ii）重建图像，用于典型的腹部扫描协议，入射粒子注量水平范围为10^5至1.6×10^6光子/探测器。通过视觉检查和图像质量评价指标对从数据模型（i）和（ii）重建的图像进行比较。当均值与假定模型不匹配时，重建图像质量显著下降。然而，如果信号均值得到适当修正，即使信噪比非常低，来自数据模型（i）和（ii）的图像也没有显著差异。虽然粒子注量和能量注量传输差异所特有的数据均值不匹配会导致显著的条纹和杯状伪影，但一旦校正了系统的数据均值不匹配，实际与假定的CT探测器信号统计之间的不匹配并不会显著降低图像质量。
