Gravel Paul, Reader Andrew J
Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
Phys Med Biol. 2015 Jun 7;60(11):4533-49. doi: 10.1088/0031-9155/60/11/4533. Epub 2015 May 20.
This work assesses the one-step late maximum likelihood expectation maximization (OSL-MLEM) 4D PET reconstruction algorithm for direct estimation of parametric images from raw PET data when using the simplified reference tissue model with the basis function method (SRTM-BFM) for the kinetic analysis. To date, the OSL-MLEM method has been evaluated using kinetic models based on two-tissue compartments with an irreversible component. We extend the evaluation of this method for two-tissue compartments with a reversible component, using SRTM-BFM on simulated 3D + time data sets (with use of [(11)C]raclopride time-activity curves from real data) and on real data sets acquired with the high resolution research tomograph. The performance of the proposed method is evaluated by comparing voxel-level binding potential (BPND) estimates with those obtained from conventional post-reconstruction kinetic parameter estimation. For the commonly chosen number of iterations used in practice, our results show that for the 3D + time simulation, the direct method delivers results with lower (%)RMSE at the normal count level (decreases of 9-10 percentage points, corresponding to a 38-44% reduction), and also at low count levels (decreases of 17-21 percentage points, corresponding to a 26-36% reduction). As for the real 3D data set, the results obtained follow a similar trend, with the direct reconstruction method offering a 21% decrease in (%)CV compared to the post reconstruction method at low count levels. Thus, based on the results presented herein, using the SRTM-BFM kinetic model in conjunction with the OSL-MLEM direct 4D PET MLEM reconstruction method offers an improvement in performance when compared to conventional post reconstruction methods.
本研究评估了一步晚期最大似然期望最大化（OSL-MLEM）4D PET重建算法，该算法用于在使用基于基函数方法的简化参考组织模型（SRTM-BFM）进行动力学分析时，直接从原始PET数据估计参数图像。迄今为止，OSL-MLEM方法已使用基于具有不可逆成分的双组织隔室的动力学模型进行了评估。我们将该方法的评估扩展到具有可逆成分的双组织隔室，在模拟的3D +时间数据集（使用来自真实数据的[(11)C]雷氯必利时间-活度曲线）和高分辨率研究断层扫描仪获取的真实数据集上使用SRTM-BFM。通过将体素水平的结合潜能（BPND）估计值与传统重建后动力学参数估计获得的值进行比较，评估了所提出方法的性能。对于实际中常用的迭代次数，我们的结果表明，对于3D +时间模拟，直接法在正常计数水平下提供的结果具有较低的（%）RMSE（降低9-10个百分点，相当于降低38-44%），在低计数水平下也是如此（降低17-21个百分点，相当于降低26-36%）。对于真实的3D数据集，获得的结果遵循类似的趋势，在低计数水平下，直接重建方法与重建后方法相比，（%）CV降低了21%。因此，基于本文给出的结果，与传统的重建后方法相比，将SRTM-BFM动力学模型与OSL-MLEM直接4D PET MLEM重建方法结合使用可提高性能。
