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评估一种新的锥形束 CT 转换方法在治疗计划系统中的应用。

Evaluation of a novel CBCT conversion method implemented in a treatment planning system.

机构信息

Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University of Vienna/AKH Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.

出版信息

Radiat Oncol. 2023 Nov 16;18(1):191. doi: 10.1186/s13014-023-02378-2.

DOI:10.1186/s13014-023-02378-2
PMID:37974264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10655347/
Abstract

BACKGROUND

To evaluate a novel CBCT conversion algorithm for dose calculation implemented in a research version of a treatment planning system (TPS).

METHODS

The algorithm was implemented in a research version of RayStation (v. 11B-DTK, RaySearch, Stockholm, Sweden). CBCTs acquired for each ten head and neck (HN), gynecology (GYN) and lung cancer (LNG) patients were collected and converted using the new algorithm (CBCT). A bulk density overriding technique implemented in the same version of the TPS was used for comparison (CBCT). A deformed CT (dCT) was created by using deformable image registration of the planning CT (pCT) to the CBCT to reduce anatomical changes. All treatment plans were recalculated on the pCT, dCT, CBCT and the CBCT. The resulting dose distributions were analyzed using the MICE toolkit (NONPIMedical AB Sweden, Umeå) with local gamma analysis, with 1% dose difference and 1 mm distance to agreement criteria. A Wilcoxon paired rank sum test was applied to test the differences in gamma pass rates (GPRs). A p value smaller than 0.05 considered statistically significant.

RESULTS

The GPRs for the CBCT method were systematically lower compared to the CBCT method. Using the 10% dose threshold and the dCT as reference the median GPRs were for the CBCT method were 100% and 99.8% for the HN and GYN cases, respectively. Compared to that the GPRs of the CBCT method were lower with values of 99.8% and 98.0%, for the HN and GYN cases, respectively. The GPRs of the LNG cases were 99.9% and 97.5% for the CBCT and CBCT method, respectively. These differences were statistically significant. The main differences between the dose calculated on the CBCTs and the pCTs were found in regions near air/tissue interfaces, which are also subject to anatomical variations.

CONCLUSION

The dose distribution calculated using the new CBCT method showed excellent agreement with the dose calculated using dCT and pCT and was superior to the CBCT method. The main reasons for deviations of the calculated dose distribution were caused by anatomical variations between the pCT and the corrected CBCT.

摘要

背景

评估一种新的用于剂量计算的锥形束 CT(CBCT)转换算法,该算法在治疗计划系统(TPS)的研究版本中实现。

方法

该算法在 RayStation 研究版本(v.11B-DTK,RaySearch,斯德哥尔摩,瑞典)中实现。收集了每个 10 例头颈部(HN)、妇科(GYN)和肺癌(LNG)患者的 CBCT,并使用新算法(CBCT)进行转换。TPS 的同一版本中实现的体密度覆盖技术用于比较(CBCT)。通过对计划 CT(pCT)与 CBCT 进行变形图像配准来创建变形 CT(dCT),以减少解剖结构的变化。所有治疗计划都在 pCT、dCT、CBCT 和 CBCT 上重新计算。使用 MICE 工具包(NONPIMedical AB Sweden,Umeå)进行局部伽马分析,使用 1%剂量差异和 1mm 距离一致性标准来分析所得剂量分布。应用 Wilcoxon 配对秩和检验测试伽马通过率(GPR)的差异。p 值小于 0.05 被认为具有统计学意义。

结果

与 CBCT 方法相比,CBCT 方法的 GPR 系统较低。使用 10%剂量阈值和 dCT 作为参考,HN 和 GYN 病例的 CBCT 方法的中位数 GPR 分别为 100%和 99.8%。与 CBCT 方法相比,HN 和 GYN 病例的 GPR 分别为 99.8%和 98.0%。LNG 病例的 CBCT 和 CBCT 方法的 GPR 分别为 99.9%和 97.5%。这些差异具有统计学意义。在 CBCT 和 pCT 上计算的剂量分布之间的主要差异出现在空气/组织界面附近的区域,这些区域也受到解剖结构变化的影响。

结论

使用新的 CBCT 方法计算的剂量分布与使用 dCT 和 pCT 计算的剂量分布具有极好的一致性,优于 CBCT 方法。计算剂量分布的偏差主要是由于 pCT 和校正后的 CBCT 之间的解剖结构变化引起的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/94f5d988c5aa/13014_2023_2378_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/557fec009c00/13014_2023_2378_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/6f1bf5bbf7ee/13014_2023_2378_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/5a7a8099e2ff/13014_2023_2378_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/34fbbdb7b964/13014_2023_2378_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/5c0145804e82/13014_2023_2378_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/94f5d988c5aa/13014_2023_2378_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/557fec009c00/13014_2023_2378_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/6f1bf5bbf7ee/13014_2023_2378_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/5a7a8099e2ff/13014_2023_2378_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/34fbbdb7b964/13014_2023_2378_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/5c0145804e82/13014_2023_2378_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2306/10655347/94f5d988c5aa/13014_2023_2378_Fig6_HTML.jpg

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