Welch A, Gullberg G T
Department of Radiology, The University of Utah, Salt Lake City 84112, USA.
IEEE Trans Med Imaging. 1997 Dec;16(6):717-26. doi: 10.1109/42.650869.
In this paper four scatter-compensation schemes are considered. The four schemes are all based on a previously developed two-dimensional (2-D) scatter model. Reconstruction is achieved using the iterative expectation-maximization maximum-likelihood (EM-ML) algorithm. The schemes consist of: 1) including the model in both the forward and back projector; 2) just including the model in the forward projector; 3) and 4) implementing the model in a subtraction and addition scheme, respectively. Monte Carlo simulated projection data are used to test the accuracy, convergence properties, and noise properties of the four scatter-compensation schemes. Data are simulated for both uniformly and nonuniformly attenuating objects. The results show that all four correction schemes yield images which are similar in terms of accuracy to that obtained from reconstructing scatter-free data. The subtraction scheme is shown to converge faster than the other compensation schemes, both in terms of iterations and actual time required for reconstruction. The scheme in which the model is only used in the forward-projector and the scatter-addition scheme both performs slightly better, in terms of signal-to-noise ratio (SNR), than the subtraction scheme. However, the forward projector scheme requires significantly more CPU time for reconstruction. The correction scheme in which the scatter model was included in both the forward and backprojectors is shown to produce accurate images with SNR's higher than even a perfect scatter rejection scheme. While the scatter correction scheme with the model in both the forward projector and backprojector has superior noise properties to the other algorithms, the results suggest that the faster subtraction/addition schemes will probably prove most useful for routine clinical scatter compensation.
本文考虑了四种散射补偿方案。这四种方案均基于先前开发的二维（2-D）散射模型。使用迭代期望最大化最大似然（EM-ML）算法进行重建。这些方案包括：1）在前向投影仪和后向投影仪中都包含该模型；2）仅在前向投影仪中包含该模型；3）和4）分别在减法和加法方案中实现该模型。使用蒙特卡罗模拟投影数据来测试这四种散射补偿方案的准确性、收敛特性和噪声特性。针对均匀衰减和非均匀衰减的物体都进行了数据模拟。结果表明，所有四种校正方案生成的图像在准确性方面与从无散射数据重建得到的图像相似。减法方案在迭代次数和重建所需的实际时间方面都比其他补偿方案收敛得更快。仅在前向投影仪中使用该模型的方案和散射加法方案在信噪比（SNR）方面都比减法方案略好。然而，前向投影仪方案重建所需的CPU时间要多得多。在前向投影仪和后向投影仪中都包含散射模型的校正方案显示，其生成的准确图像的SNR甚至高于完美的散射抑制方案。虽然在前向投影仪和后向投影仪中都包含模型的散射校正方案在噪声特性方面优于其他算法，但结果表明，更快的减法/加法方案可能对常规临床散射补偿最为有用。
