Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.
Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.
Biomed Phys Eng Express. 2022 Apr 5;8(3). doi: 10.1088/2057-1976/ac605b.
. The radiology department faces a large number of reconstruction algorithms and kernels during their computed tomography (CT) optimization process. These reconstruction methods are proprietary and ensuring consistent image quality between scanners is becoming increasingly difficult. This study contributes to solving this challenge in CT image quality harmonization by modifying and evaluating a reconstruction algorithm and kernel matching scheme.. The Catphan 600 phantom was scanned with six different CT scanners from four vendors. The phantom was scanned with volumetric CT dose indices (CTDIvols) of 10 mGy and 40 mGy, and the data were reconstructed using 1 mm and 5 mm slices with each combination of reconstruction algorithm, body region kernel, and iterative and deep learning reconstruction strength. A matching scheme developed in previous research, which utilizes the noise power spectrum (NPS) and modulation transfer function (MTF), was modified based on our organization's needs and used to identify the matching reconstruction algorithms and kernels between different scanners.. The matching paradigm produced good matching results, and the mean ± standard deviation (median) matching function values for the different acquisition settings were (a value of 1 indicates a perfect match): CTDIvol 10 mGy, 1 mm slice: 0.78 ± 0.31 (0.94); CTDIvol 10 mGy, 5 mm slice: 0.75 ± 0.33 (0.93); CTDIvol 40 mGy, 1 mm slice: 0.81 ± 0.28 (0.95); CTDIvol 40 mGy, 5 mm slice: 0.75 ± 0.33 (0.93). In general, soft reconstruction kernels, i.e., noise-reducing kernels that reduce sharpness, of one vendor were matched with the soft kernels of another vendor, and vice versa for sharper kernels. Conclusions. Combined quantitative assessment of NPS and MTF allows effective strategy for harmonization of technical image quality between different CT scanners. A software was also shared to support CT image quality harmonization in other institutions.
. 放射科在进行计算机断层扫描（CT）优化过程中，面临大量的重建算法和内核。这些重建方法是专有的，确保扫描仪之间的图像质量一致变得越来越困难。本研究通过修改和评估重建算法和内核匹配方案，为解决 CT 图像质量协调中的这一挑战做出了贡献。. 使用来自四个供应商的六台不同 CT 扫描仪对 Catphan 600 体模进行了扫描。体模以 10 mGy 和 40 mGy 的容积 CT 剂量指数（CTDIvol）进行扫描，数据使用 1mm 和 5mm 切片进行重建，每种重建算法、体部区域内核、迭代和深度学习重建强度的组合。基于我们组织的需求，对以前研究中开发的匹配方案进行了修改，并利用噪声功率谱（NPS）和调制传递函数（MTF）对其进行了修改，用于识别不同扫描仪之间的匹配重建算法和内核。. 匹配范例产生了良好的匹配结果，对于不同采集设置的平均标准偏差（中位数）匹配函数值为（值为 1 表示完美匹配）：CTDIvol 10 mGy，1mm 切片：0.78 ± 0.31（0.94）；CTDIvol 10 mGy，5mm 切片：0.75 ± 0.33（0.93）；CTDIvol 40 mGy，1mm 切片：0.81 ± 0.28（0.95）；CTDIvol 40 mGy，5mm 切片：0.75 ± 0.33（0.93）。总体而言，一个供应商的软重建内核（即降低锐度的降噪内核）与另一个供应商的软内核匹配，反之亦然，对于更锐利的内核也是如此。结论。NPS 和 MTF 的综合定量评估允许在不同 CT 扫描仪之间协调技术图像质量的有效策略。还共享了一个软件，以支持其他机构的 CT 图像质量协调。
