Medical Physics Graduate Program, Duke University, Durham, North Carolina 27705 and Carl E. Ravin Advanced Imaging Laboratories, Duke University, Durham, North Carolina 27705.
Med Phys. 2013 Nov;40(11):111902. doi: 10.1118/1.4823463.
Volume quantifications of lung nodules with multidetector computed tomography (CT) images provide useful information for monitoring nodule developments. The accuracy and precision of the volume quantification, however, can be impacted by imaging and reconstruction parameters. This study aimed to investigate the impact of iterative reconstruction algorithms on the accuracy and precision of volume quantification with dose and slice thickness as additional variables.
Repeated CT images were acquired from an anthropomorphic chest phantom with synthetic nodules (9.5 and 4.8 mm) at six dose levels, and reconstructed with three reconstruction algorithms [filtered backprojection (FBP), adaptive statistical iterative reconstruction (ASiR), and model based iterative reconstruction (MBIR)] into three slice thicknesses. The nodule volumes were measured with two clinical software (A: Lung VCAR, B: iNtuition), and analyzed for accuracy and precision.
Precision was found to be generally comparable between FBP and iterative reconstruction with no statistically significant difference noted for different dose levels, slice thickness, and segmentation software. Accuracy was found to be more variable. For large nodules, the accuracy was significantly different between ASiR and FBP for all slice thicknesses with both software, and significantly different between MBIR and FBP for 0.625 mm slice thickness with Software A and for all slice thicknesses with Software B. For small nodules, the accuracy was more similar between FBP and iterative reconstruction, with the exception of ASIR vs FBP at 1.25 mm with Software A and MBIR vs FBP at 0.625 mm with Software A.
The systematic difference between the accuracy of FBP and iterative reconstructions highlights the importance of extending current segmentation software to accommodate the image characteristics of iterative reconstructions. In addition, a calibration process may help reduce the dependency of accuracy on reconstruction algorithms, such that volumes quantified from scans of different reconstruction algorithms can be compared. The little difference found between the precision of FBP and iterative reconstructions could be a result of both iterative reconstruction's diminished noise reduction at the edge of the nodules as well as the loss of resolution at high noise levels with iterative reconstruction. The findings do not rule out potential advantage of IR that might be evident in a study that uses a larger number of nodules or repeated scans.
使用多排螺旋 CT 图像对肺结节进行体积定量分析可为监测结节的发展提供有用信息。然而，成像和重建参数会影响体积定量的准确性和精密度。本研究旨在探讨迭代重建算法对体积定量的准确性和精密度的影响，并将剂量和层厚作为附加变量进行研究。
在六个剂量水平下，对一个带有合成结节（9.5 和 4.8mm）的人体胸部模型进行重复 CT 扫描，并使用三种重建算法[滤波反投影（FBP）、自适应统计迭代重建（ASiR）和基于模型的迭代重建（MBIR）]分别对三种层厚进行重建。使用两种临床软件（A：Lung VCAR，B：iNtuition）对结节体积进行测量，并对准确性和精密度进行分析。
精密度在 FBP 和迭代重建之间通常具有可比性，不同剂量水平、层厚和分割软件之间没有统计学差异。准确性则更为多变。对于大结节，在两种软件中，ASiR 与 FBP 之间的准确性在所有层厚上均存在显著差异，在软件 A 中，MBIR 与 FBP 之间在 0.625mm 层厚上存在显著差异，在软件 B 中，在所有层厚上均存在显著差异。对于小结节，FBP 和迭代重建之间的准确性更为相似，但软件 A 中的 1.25mm 层厚时 ASiR 与 FBP 之间以及软件 A 中的 0.625mm 层厚时 MBIR 与 FBP 之间存在例外。
FBP 和迭代重建之间准确性的系统差异突出表明，需要扩展当前的分割软件以适应迭代重建的图像特征。此外，校准过程可能有助于减少准确性对重建算法的依赖性，从而可以比较来自不同重建算法扫描的体积定量结果。FBP 和迭代重建之间精密度的微小差异可能是由于结节边缘的迭代重建噪声降低以及高噪声水平下的分辨率损失所致。这些发现并不排除迭代重建可能具有的优势，这可能在使用更多结节或重复扫描的研究中显现出来。
