Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation
Dual respiratory and cardiac motion estimation in PET imaging: Methods design and quantitative evaluation
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DOI：
10.1002/mp.12793
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发表时间：
2018-04-01
期刊：
影响因子：
3.8
通讯作者：
Tsui, Benjamin M. W.
中科院分区：
文献类型：
--
作者：
Feng, Tao;Wang, Jizhe;Tsui, Benjamin M. W.
目的
本研究的目的是针对心脏4D正电子发射断层扫描（PET）数据开发并评估四种重建后的呼吸和心脏（R&C）运动矢量场（MVF）估计方法。
方法
在方法1中，直接从呼吸和心脏双门控图像估计呼吸和心脏的双重运动。在方法2中，分别从仅呼吸门控图像和仅心脏门控图像估计呼吸运动（RM）和心脏运动（CM）。在方法3中，通过在估计心脏运动之前对心脏门控图像进行基于图像的呼吸运动校正来模拟呼吸运动对心脏运动估计的影响，而忽略心脏运动对呼吸运动估计的影响。方法4在呼吸和心脏双重运动估计过程中迭代地模拟呼吸运动和心脏运动的相互影响。生成了逼真的模拟数据以对四种方法进行定量评估。使用蒙特卡罗模拟从具有逼真的呼吸和心脏运动矢量场的4D XCAT体模生成几乎无噪声的PET投影数据。将泊松噪声添加到缩放后的投影数据中，以生成另外两个不同噪声水平的数据集。所有投影数据均使用4D图像重建方法进行重建，以获得呼吸和心脏双门控图像。将四种呼吸和心脏双运动矢量场估计方法应用于呼吸和心脏双门控图像，并使用估计的运动矢量场的均方根误差（RMSE）对运动估计的准确性进行定量评估。
结果
结果表明，在四种估计方法中，就估计的运动矢量场的定量准确性而言，方法2在无噪声情况下表现最差，而方法1在有噪声情况下表现最差。方法4和方法3显示出可比的结果，并且在有噪声情况下，其均方根误差比方法1低多达35%。
结论
总之，我们针对4D PET成像开发并评估了4种不同的重建后呼吸和心脏运动矢量场估计方法。对四种方法在模拟数据上的性能比较表明，在估计心脏运动之前对呼吸运动进行建模的呼吸和心脏分别估计（方法3）是在临床情况下准确估计呼吸和心脏双重运动的最佳选择。
PurposeThe goal of this study was to develop and evaluate four post-reconstruction respiratory and cardiac (R&C) motion vector field (MVF) estimation methods for cardiac 4D PET data.MethodIn Method 1, the dual R&C motions were estimated directly from the dual R&C gated images. In Method 2, respiratory motion (RM) and cardiac motion (CM) were separately estimated from the respiratory gated only and cardiac gated only images. The effects of RM on CM estimation were modeled in Method 3 by applying an image-based RM correction on the cardiac gated images before CM estimation, the effects of CM on RM estimation were neglected. Method 4 iteratively models the mutual effects of RM and CM during dual R&C motion estimations. Realistic simulation data were generated for quantitative evaluation of four methods. Almost noise-free PET projection data were generated from the 4D XCAT phantom with realistic R&C MVF using Monte Carlo simulation. Poisson noise was added to the scaled projection data to generate additional datasets of two more different noise levels. All the projection data were reconstructed using a 4D image reconstruction method to obtain dual R&C gated images. The four dual R&C MVF estimation methods were applied to the dual R&C gated images and the accuracy of motion estimation was quantitatively evaluated using the root mean square error (RMSE) of the estimated MVFs.ResultsResults show that among the four estimation methods, Methods 2 performed the worst for noise-free case while Method 1 performed the worst for noisy cases in terms of quantitative accuracy of the estimated MVF. Methods 4 and 3 showed comparable results and achieved RMSE lower by up to 35% than that in Method 1 for noisy cases.ConclusionIn conclusion, we have developed and evaluated 4 different post-reconstruction R&C MVF estimation methods for use in 4D PET imaging. Comparison of the performance of four methods on simulated data indicates separate R&C estimation with modeling of RM before CM estimation (Method 3) to be the best option for accurate estimation of dual R&C motion in clinical situation.