Lee Jae Choon, Park Hyeong Wook, Kang Young Nam
Department of Medical Physics, Kyonggi University, Suwon, South Korea.
Department of Radiation Oncology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seocho-gu, Seoul, South Korea.
J Appl Clin Med Phys. 2025 Mar;26(3):e14591. doi: 10.1002/acm2.14591. Epub 2024 Dec 3.
The traditional gamma evaluation method combines dose difference (DD) and distance-to-agreement (DTA) to assess the agreement between two dose distributions. However, while gamma evaluation can identify the location of errors, it does not provide information about the type of errors.
The purpose of this study is to optimize and apply the structural similarity (SSIM) index algorithm as a supplementary metric for the quality evaluation of radiation therapy plans alongside gamma evaluation. By addressing the limitations of gamma evaluation, this study aims to establish clinically meaningful SSIM criteria to enhance the accuracy of patient-specific quality assurance (PSQA).
We analyzed the relationship between the gamma passing rate (GPR) and the SSIM index with respect to distance and dose errors. For SSIM analysis corresponding to gamma evaluation criteria of 3%/2 mm, we introduce the concept of SSIM passing rate (SPR). We determined a valid SSIM index that met the gamma evaluation criteria and applied it. Evaluations performed for 40 fields measured with an electronic portal imaging device (EPID) were analyzed using the GPR and the applied SPR.
The study results showed that distance errors significantly affected both the GPR and the SSIM index, whereas dose errors had some influence on the GPR but little impact on the SSIM index. The SPR was 100% for distance error of 2 mm but began to decrease for distance errors of 3 mm or more. An optimal SSIM index threshold of 0.65 was established, indicating that SPR fell below 100% when distance errors exceeded 2 mm.
This study demonstrates that the SSIM algorithm can be effectively applied for the quality evaluation of radiation therapy plans. The SPR can serve as a supplementary metric to gamma evaluation, offering a more precise identification of distance errors. Future research should further validate the efficacy of SSIM algorithm across a broader range of clinical cases.
传统的伽马评估方法结合剂量差异(DD)和距离一致性(DTA)来评估两种剂量分布之间的一致性。然而,虽然伽马评估可以识别误差位置,但它并不能提供有关误差类型的信息。
本研究的目的是优化并应用结构相似性(SSIM)指数算法,作为伽马评估之外的放射治疗计划质量评估的补充指标。通过解决伽马评估的局限性,本研究旨在建立具有临床意义的SSIM标准,以提高患者特异性质量保证(PSQA)的准确性。
我们分析了伽马通过率(GPR)与SSIM指数在距离和剂量误差方面的关系。对于对应于3%/2毫米伽马评估标准的SSIM分析,我们引入了SSIM通过率(SPR)的概念。我们确定了符合伽马评估标准的有效SSIM指数并应用它。使用GPR和应用的SPR对用电子门静脉成像设备(EPID)测量的40个射野进行的评估进行分析。
研究结果表明,距离误差对GPR和SSIM指数均有显著影响,而剂量误差对GPR有一定影响,但对SSIM指数影响较小。距离误差为2毫米时SPR为100%,但距离误差为3毫米或更大时开始下降。建立了0.65的最佳SSIM指数阈值,表明当距离误差超过2毫米时SPR低于100%。
本研究表明SSIM算法可有效应用于放射治疗计划的质量评估。SPR可作为伽马评估的补充指标,能更精确地识别距离误差。未来的研究应在更广泛的临床病例中进一步验证SSIM算法的有效性。