Kotasidis F A, Matthews J C, Reader A J, Angelis G I, Zaidi H
Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva, Switzerland. Wolfson Molecular Imaging Centre, MAHSC, University of Manchester, M20 3LJ, Manchester, UK.
Phys Med Biol. 2014 Oct 21;59(20):6061-84. doi: 10.1088/0031-9155/59/20/6061. Epub 2014 Sep 25.
Parametric imaging in thoracic and abdominal PET can provide additional parameters more relevant to the pathophysiology of the system under study. However, dynamic data in the body are noisy due to the limiting counting statistics leading to suboptimal kinetic parameter estimates. Direct 4D image reconstruction algorithms can potentially improve kinetic parameter precision and accuracy in dynamic PET body imaging. However, construction of a common kinetic model is not always feasible and in contrast to post-reconstruction kinetic analysis, errors in poorly modelled regions may spatially propagate to regions which are well modelled. To reduce error propagation from erroneous model fits, we implement and evaluate a new approach to direct parameter estimation by incorporating a recently proposed kinetic modelling strategy within a direct 4D image reconstruction framework. The algorithm uses a secondary more general model to allow a less constrained model fit in regions where the kinetic model does not accurately describe the underlying kinetics. A portion of the residuals then is adaptively included back into the image whilst preserving the primary model characteristics in other well modelled regions using a penalty term that trades off the models. Using fully 4D simulations based on dynamic [(15)O]H2O datasets, we demonstrate reduction in propagation-related bias for all kinetic parameters. Under noisy conditions, reductions in bias due to propagation are obtained at the cost of increased noise, which in turn results in increased bias and variance of the kinetic parameters. This trade-off reflects the challenge of separating the residuals arising from poor kinetic modelling fits from the residuals arising purely from noise. Nonetheless, the overall root mean square error is reduced in most regions and parameters. Using the adaptive 4D image reconstruction improved model fits can be obtained in poorly modelled regions, leading to reduced errors potentially propagating to regions of interest which the primary biologic model accurately describes. The proposed methodology, however, depends on the secondary model and choosing an optimal model on the residual space is critical in improving model fits.
胸部和腹部PET中的参数成像可以提供更多与所研究系统的病理生理学更相关的参数。然而，由于计数统计的限制，体内的动态数据存在噪声，导致动力学参数估计不理想。直接4D图像重建算法有可能提高动态PET全身成像中动力学参数的精度和准确性。然而，构建通用的动力学模型并不总是可行的，与重建后动力学分析不同，建模不佳区域的误差可能会在空间上传播到建模良好的区域。为了减少错误模型拟合带来的误差传播，我们在直接4D图像重建框架内纳入最近提出的动力学建模策略，实施并评估一种直接参数估计的新方法。该算法使用一个更通用的二级模型，以便在动力学模型不能准确描述潜在动力学的区域进行约束较少的模型拟合。然后，一部分残差被自适应地重新纳入图像，同时使用权衡模型的惩罚项在其他建模良好的区域保留主要模型特征。使用基于动态[(15)O]H2O数据集的全4D模拟，我们证明了所有动力学参数的传播相关偏差都有所降低。在噪声条件下，以增加噪声为代价获得了因传播导致的偏差减少，这反过来又导致动力学参数的偏差和方差增加。这种权衡反映了将动力学建模拟合不佳产生的残差与纯粹由噪声产生的残差区分开来的挑战。尽管如此，大多数区域和参数的总体均方根误差都有所降低。使用自适应4D图像重建，可以在建模不佳的区域获得改进的模型拟合，从而减少可能传播到主要生物学模型准确描述的感兴趣区域的误差。然而，所提出的方法依赖于二级模型，在残差空间中选择最优模型对于改善模型拟合至关重要。
