Du Yong, Tsui Benjamin M W, Frey Eric C
Division of Medical Imaging Physics, Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
Phys Med Biol. 2006 Mar 7;51(5):1269-82. doi: 10.1088/0031-9155/51/5/016. Epub 2006 Feb 15.
Previously we have developed a model-based method that can accurately estimate downscatter contamination from high-energy photons in 123I imaging. In this work we combined the model-based method with iterative reconstruction-based compensations for other image-degrading factors such as attenuation, scatter, the collimator-detector response function (CDRF) and partial volume effects to form a comprehensive method for performing quantitative 123I SPECT image reconstruction. In the model-based downscatter estimation method, photon scatter inside the object was modelled using the effective source scatter estimation (ESSE) technique, including contributions from all the photon emissions. The CDRFs, including the penetration and scatter components due to the high-energy 123I photons, were estimated using Monte Carlo (MC) simulations of point sources in air at various distances from the face of the collimator. The downscatter contamination was then compensated for during the iterative reconstruction by adding the estimated results to the projection steps. The model-based downscatter compensation (MBDC) was evaluated using MC simulated and experimentally acquired projection data. From the MC simulation, we found about 39% of the total counts in the energy window of 123I were attributed to the downscatter contamination, which reduced image contrast and caused a 1.5% to 10% overestimation of activities in various brain structures. Model-based estimates of the downscatter contamination were in good agreement with the simulated data. Compensation using MBDC removed the contamination and improved the image contrast and quantitative accuracy to that of the images obtained from 159 keV photons. The errors in absolute quantitation were reduced to within +/-3.5%. The striatal specific binding potential calculated based on the activity ratio to the background was also improved after MBDC. The errors were reduced from -4.5% to -10.93% without compensation to -0.55% to 4.87% after compensation. The model-based method provided accurate downscatter estimation and, when combined with iterative reconstruction-based compensations, accurate quantitation was obtained with minimal loss of precision.
此前我们已开发出一种基于模型的方法,该方法能够准确估计123I成像中高能光子的散射污染。在这项工作中,我们将基于模型的方法与基于迭代重建的其他图像退化因素补偿方法相结合,这些因素包括衰减、散射、准直器-探测器响应函数(CDRF)和部分容积效应,从而形成一种用于进行定量123I单光子发射计算机断层显像(SPECT)图像重建的综合方法。在基于模型的散射污染估计方法中,使用有效源散射估计(ESSE)技术对物体内部的光子散射进行建模,包括所有光子发射的贡献。CDRF包括由于高能123I光子引起的穿透和散射分量,通过在空气中对距准直器表面不同距离的点源进行蒙特卡罗(MC)模拟来估计。然后在迭代重建过程中,通过将估计结果添加到投影步骤来补偿散射污染。使用MC模拟和实验获取的投影数据对基于模型的散射补偿(MBDC)进行了评估。从MC模拟中,我们发现123I能量窗中约39%的总计数归因于散射污染,这降低了图像对比度,并导致各种脑结构中的活性高估1.5%至10%。基于模型的散射污染估计与模拟数据高度吻合。使用MBDC进行补偿消除了污染,并将图像对比度和定量准确性提高到从159 keV光子获得的图像水平。绝对定量误差降低到±3.5%以内。基于与背景的活性比计算的纹状体特异性结合潜力在MBDC后也得到了改善。误差从无补偿时的-4.5%至-10.93%降低到补偿后的-0.55%至4.87%。基于模型的方法提供了准确的散射估计,并且与基于迭代重建的补偿相结合时,在精度损失最小的情况下获得了准确的定量结果。