Chair of Biomedical Physics and Department of Physics and Munich School of BioEngineering, Technical University of Munich, 85748, Garching, Germany.
MITOS GmbH, 85748, Garching, Germany.
Sci Rep. 2019 Apr 12;9(1):6016. doi: 10.1038/s41598-019-40837-7.
As iterative reconstruction in Computed Tomography (CT) is an ill-posed problem, additional prior information has to be used to get a physically meaningful result (close to ground truth if available). However, the amount of influence of the regularisation prior is crucial to the outcome of the reconstruction. Therefore, we propose a scheme for tuning the strength of the prior via a certain image metric. In this work, the parameter is tuned for minimal histogram entropy in selected regions of the reconstruction as histogram entropy is a very basic approach to characterise the information content of data. We performed a sweep over different regularisation parameters showing that the histogram entropy is a suitable metric as it is well behaved over a wide range of parameters. The parameter determination is a feedback loop approach we applied to numerically simulated FORBILD phantom data and verified with an experimental measurement of a micro-CT device. The outcome is evaluated visually and quantitatively by means of root mean squared error (RMSE) and structural similarity (SSIM) for the simulation and visually for the measured sample (no ground truth available). The final reconstructed images exhibit noise-suppressed iterative reconstruction. For both datasets, the optimisation is robust where its initial value is concerned. The parameter tuning approach shows that the proposed metric-driven feedback loop is a promising tool for finding a suitable regularisation parameter in statistical iterative reconstruction.
由于计算机断层扫描（CT）中的迭代重建是一个不适定问题，因此必须使用额外的先验信息来获得具有物理意义的结果（如果有则接近真实值）。然而，正则化先验的影响程度对于重建的结果至关重要。因此，我们提出了一种通过特定图像度量来调整先验强度的方案。在这项工作中，通过在重建的选定区域中最小化直方图熵来调整参数，因为直方图熵是一种非常基本的方法，可以描述数据的信息量。我们对不同的正则化参数进行了扫描，结果表明直方图熵是一种合适的度量标准，因为它在很宽的参数范围内表现良好。参数确定是我们应用于数值模拟 FORBILD 体模数据并通过微 CT 设备的实验测量进行验证的反馈回路方法。结果通过均方根误差（RMSE）和结构相似性（SSIM）进行视觉和定量评估，对于模拟数据进行视觉评估（没有真实值）。最终重建的图像显示出具有抑制噪声的迭代重建。对于这两个数据集，优化在其初始值方面是稳健的。参数调整方法表明，所提出的基于度量的反馈回路是在统计迭代重建中找到合适正则化参数的一种有前途的工具。
