IEEE Trans Med Imaging. 2016 Jan;35(1):158-73. doi: 10.1109/TMI.2015.2459764. Epub 2015 Jul 22.
The presence of high-density objects remains an open problem in medical CT imaging. Data of projections passing through objects of high density, such as metal implants, are dominated by noise and are highly affected by beam hardening and scatter. Reconstructed images become less diagnostically conclusive because of pronounced artifacts that manifest as dark and bright streaks. A new reconstruction algorithm is proposed with the aim to reduce these artifacts by incorporating information about shape and known attenuation coefficients of a metal implant. Image reconstruction is considered as a variational optimization problem. The afore-mentioned prior knowledge is introduced in terms of equality constraints. An augmented Lagrangian approach is adapted in order to minimize the associated log-likelihood function for transmission CT. During iterations, temporally appearing artifacts are reduced with a bilateral filter and new projection values are calculated, which are used later on for the reconstruction. A detailed evaluation in cooperation with radiologists is performed on software and hardware phantoms, as well as on clinically relevant patient data of subjects with various metal implants. Results show that the proposed reconstruction algorithm is able to outperform contemporary metal artifact reduction methods such as normalized metal artifact reduction.
高密度物体的存在仍然是医学 CT 成像中的一个未解决的问题。穿过高密度物体(如金属植入物)的投影数据主要受到噪声的影响,并且受到束硬化和散射的高度影响。由于明显的伪影(表现为暗线和亮线),重建图像的诊断结论变得不那么明确。提出了一种新的重建算法,旨在通过包含有关金属植入物形状和已知衰减系数的信息来减少这些伪影。图像重建被视为变分优化问题。上述先验知识以等式约束的形式引入。为了最小化传输 CT 的相关对数似然函数,采用了增广拉格朗日方法。在迭代过程中,使用双边滤波器减少暂时出现的伪影,并计算新的投影值,稍后用于重建。在软件和硬件体模以及具有各种金属植入物的受试者的临床相关患者数据上与放射科医生合作进行了详细评估。结果表明,所提出的重建算法能够胜过当代的金属伪影减少方法,例如归一化金属伪影减少。