Department of Applied Mathematics, The Australian National University, Canberra, ACT 0200, Australia.
Med Phys. 2011 Sep;38(9):4934-45. doi: 10.1118/1.3609096.
The authors present a robust algorithm that removes the blurring and double-edge artifacts in high-resolution computed tomography (CT) images that are caused by misaligned scanner components. This alleviates the time-consuming process of physically aligning hardware, which is of particular benefit if components are moved or swapped frequently.
The proposed method uses the experimental data itself for calibration. A parameterized model of the scanner geometry is constructed and the parameters are varied until the sharpest 3D reconstruction is found. The concept is similar to passive auto-focus algorithms of digital optical instruments. The parameters are used to remap the projection data from the physical detector to a virtual aligned detector. This is followed by a standard reconstruction algorithm, namely the Feldkamp algorithm. Feldkamp et al. [J. Opt. Soc. Am. A 1, 612-619 (1984)].
An example implementation is given for a rabbit liver specimen that was collected with a circular trajectory. The optimal parameters were determined in less computation time than that for a full reconstruction. The example serves to demonstrate that (a) sharpness is an appropriate measure for projection alignment, (b) our parameterization is sufficient to characterize misalignments for cone-beam CT, and (c) the procedure determines parameter values with sufficient precision to remove the associated artifacts.
The algorithm is fully tested and implemented for regular use at The Australian National University micro-CT facility for both circular and helical trajectories. It can in principle be applied to more general imaging geometries and modalities. It is as robust as manual alignment but more precise since we have quantified the effect of misalignment.
作者提出了一种强大的算法，可消除因扫描仪组件未对准而导致的高分辨率计算机断层扫描（CT）图像中的模糊和双重边缘伪影。这减轻了物理对齐硬件的耗时过程，如果组件经常移动或更换，这将特别有益。
所提出的方法使用实验数据本身进行校准。构建扫描仪几何形状的参数化模型，并调整参数，直到找到最清晰的 3D 重建。该概念类似于数字光学仪器的被动自动对焦算法。使用这些参数将投影数据从物理探测器重新映射到虚拟对齐的探测器。然后是标准的重建算法，即 Feldkamp 算法。Feldkamp 等人[J. Opt. Soc. Am. A 1, 612-619（1984）]。
对于使用圆形轨迹采集的兔肝标本，给出了一个示例实现。在比完整重建少的计算时间内确定了最佳参数。该示例表明：（a）锐度是投影对准的适当度量；（b）我们的参数化足以描述锥束 CT 的未对准；（c）该过程可以以足够的精度确定参数值，以消除相关的伪影。
该算法已在澳大利亚国立大学微 CT 设施中针对圆形和螺旋轨迹进行了全面测试和实施。原则上，它可以应用于更一般的成像几何形状和模式。它与手动对齐一样强大，但更精确，因为我们已经量化了未对准的影响。
