Department of Radiology, Johns Hopkins University, Baltimore, MD, 21287, USA.
Med Phys. 2018 Apr;45(4):1481-1490. doi: 10.1002/mp.12793. Epub 2018 Mar 6.
The 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.
In 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.
Results 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.
In 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.
本研究旨在开发和评估四种用于心脏 4D PET 数据的重建后呼吸和心脏（R＆C）运动向量场（MVF）估计方法。
在方法 1 中，直接从双 R＆C 门图像中估计双 R＆C 运动。在方法 2 中，从仅呼吸门控图像和仅心脏门控图像中分别估计呼吸运动（RM）和心脏运动（CM）。在方法 3 中，通过在 CM 估计之前将基于图像的 RM 校正应用于心脏门控图像，来对 CM 估计中的 RM 效应进行建模，而忽略了 CM 对 RM 估计的影响。方法 4 在双 R＆C 运动估计过程中迭代地模拟 RM 和 CM 的相互作用。使用蒙特卡罗模拟从具有真实 R＆C MVF 的 4D XCAT 体模生成逼真的模拟数据，用于对四种方法进行定量评估。从 4D XCAT 体模生成的具有真实 R＆C MVF 的 PET 投影数据使用蒙特卡罗模拟生成，几乎没有噪声。将比例化的投影数据添加到泊松噪声中，以生成另外两个噪声水平的两个更多数据集。所有投影数据均使用 4D 图像重建方法进行重建，以获得双 R＆C 门控图像。将四种双 R＆C MVF 估计方法应用于双 R＆C 门控图像，并使用估计的 MVF 的均方根误差（RMSE）对运动估计的准确性进行定量评估。
结果表明，在四种估计方法中，在无噪声情况下，方法 2 的性能最差，而在噪声情况下，方法 1 的性能最差。方法 4 和方法 3 的表现相当，在噪声情况下，与方法 1 相比，RMSE 可降低高达 35％。
总之，我们已经开发并评估了用于 4D PET 成像的四种不同的重建后 R＆C MVF 估计方法。在模拟数据上对四种方法的性能进行比较表明，在临床情况下，在 CM 估计之前对 RM 进行建模的单独 R＆C 估计（方法 3）是准确估计双 R＆C 运动的最佳选择。
