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使用定量图像质量技术对不同供应商的 RT CT 模拟器进行扫描协议标准化。

Standardization of scan protocols for RT CT simulator from different vendors using quantitative image quality technique.

机构信息

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.

Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, USA.

出版信息

J Appl Clin Med Phys. 2024 Oct;25(10):e14484. doi: 10.1002/acm2.14484. Epub 2024 Aug 13.

DOI:10.1002/acm2.14484
PMID:39137027
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11466467/
Abstract

OBJECTIVE

To investigate the feasibility of standardizing RT simulation CT scanner protocols between vendors using target-based image quality (IQ) metrics.

METHOD AND MATERIALS

A systematic assessment process in phantom was developed to standardize clinical scan protocols for scanners from different vendors following these steps: (a) images were acquired by varying CTDI and using an iterative reconstruction (IR) method (IR: iDose and model-based iterative reconstruction [IMR] of CT-Philips Big Bore scanner, SAFIRE of CT-Siemens biograph PETCT scanner), (b) CT exams were classified into body and brain protocols, (c) the rescaled noise power spectrum (NPS) was calculated, (d) quantified the IQ change due to varied CTDI and IR, and (e) matched the IR strength level. IQ metrics included noise and texture from NPS, contrast, and contrast-to-noise ratio (CNR), low contrast detectability (d'). Area under curve (AUC) of the receiver operation characteristic curve of d' was calculated and compared.

RESULTS

The level of change in the IQ ratio was significant (>0.6) when using IMR. The IQ ratio change was relatively low to moderate when using either iDose in CTp (0.1-0.5) or SAFIRE in CT (0.1-0.6). SAFIRE-2 in CT showed a closer match to the reference body protocol when compared to iDose-3 in CT. In the brain protocol, iDose-3 in CT could be matched to the low to moderate level of SAFIRE in CT. The AUC of d' was highest when using IMR in CT with lower CTDI, and SAFIRE in CT performed better than iDose in CT CONCLUSION: It is possible to use target-based IQ metrics to evaluate the performance of the system and operations across various scanners in a phantom. This can serve as an initial reference to convert clinical scanned protocols from one CT simulation scanner to another.

摘要

目的

使用基于目标的图像质量(IQ)指标来研究在不同供应商之间标准化 RT 模拟 CT 扫描仪协议的可行性。

方法和材料

在体模中开发了一种系统评估过程,以遵循以下步骤标准化来自不同供应商的扫描仪的临床扫描协议:(a)通过改变 CTDI 和使用迭代重建(IR)方法(IR:CT-Philips Big Bore 扫描仪的 iDose 和基于模型的迭代重建 [IMR]、CT-Siemens biograph PETCT 扫描仪的 SAFIRE)获取图像,(b)将 CT 检查分为身体和大脑协议,(c)计算重新缩放的噪声功率谱(NPS),(d)量化由于 CTDI 和 IR 变化导致的 IQ 变化,(e)匹配 IR 强度水平。IQ 指标包括 NPS 的噪声和纹理、对比度和对比噪声比(CNR)、低对比度检测能力(d')。计算并比较了 d'的受试者工作特征曲线的曲线下面积(AUC)。

结果

使用 IMR 时,IQ 比的变化水平显着(>0.6)。当在 CTp 中使用 iDose 时(0.1-0.5)或在 CT 中使用 SAFIRE 时(0.1-0.6),IQ 比的变化相对较低至中等。与 CT 中的 iDose-3 相比,CT 中的 SAFIRE-2 显示出与参考身体协议更接近的匹配。在大脑协议中,CT 中的 iDose-3 可以与 CT 中的 SAFIRE 的低至中等水平相匹配。当在 CT 中使用具有较低 CTDI 的 IMR 并且在 CT 中使用 SAFIRE 时,d'的 AUC 最高。结论:使用基于目标的 IQ 指标评估系统在体模中的性能并在各种扫描仪之间进行操作是可行的。这可以作为将临床扫描协议从一个 CT 模拟扫描仪转换为另一个的初始参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/aaaa28a9dc96/ACM2-25-e14484-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/040f0651775a/ACM2-25-e14484-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/089f6f162e31/ACM2-25-e14484-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/f485b4d9c8c5/ACM2-25-e14484-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/9f0ad6de5af1/ACM2-25-e14484-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/aaaa28a9dc96/ACM2-25-e14484-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/040f0651775a/ACM2-25-e14484-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/089f6f162e31/ACM2-25-e14484-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/f485b4d9c8c5/ACM2-25-e14484-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/9f0ad6de5af1/ACM2-25-e14484-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eaa8/11466467/aaaa28a9dc96/ACM2-25-e14484-g001.jpg

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2
"Image quality evaluation of the Precise image CT deep learning reconstruction algorithm compared to Filtered Back-projection and iDose: a phantom study at different dose levels".《与滤波反投影和 iDose 相比,Precise image CT 深度学习重建算法的图像质量评估:不同剂量水平下的体模研究》
Phys Med. 2023 Feb;106:102517. doi: 10.1016/j.ejmp.2022.102517. Epub 2023 Jan 18.
3
iQMetrix-CT: New software for task-based image quality assessment of phantom CT images.
iQMetrix-CT:用于基于任务的 CT 图像质量评估的新软件。
Diagn Interv Imaging. 2022 Nov;103(11):555-562. doi: 10.1016/j.diii.2022.05.007. Epub 2022 Jul 2.
4
Preserving image texture while reducing radiation dose with a deep learning image reconstruction algorithm in chest CT: A phantom study.利用深度学习图像重建算法在胸部 CT 中降低辐射剂量的同时保留图像纹理:一项体模研究。
Phys Med. 2021 Jan;81:86-93. doi: 10.1016/j.ejmp.2020.12.005. Epub 2021 Jan 11.
5
Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.基于任务的深度学习图像重建特征分析及其与腹部CT中滤波反投影和部分基于模型的迭代重建的比较:体模研究
Phys Med. 2020 Aug;76:28-37. doi: 10.1016/j.ejmp.2020.06.004. Epub 2020 Jun 20.
6
Deep learning-based image quality improvement for low-dose computed tomography simulation in radiation therapy.基于深度学习的放射治疗中低剂量计算机断层扫描模拟的图像质量改善
J Med Imaging (Bellingham). 2019 Oct;6(4):043504. doi: 10.1117/1.JMI.6.4.043504. Epub 2019 Oct 24.
7
Performance evaluation of computed tomography systems: Summary of AAPM Task Group 233.CT 系统性能评估:AAPM 工作组 233 报告摘要。
Med Phys. 2019 Nov;46(11):e735-e756. doi: 10.1002/mp.13763. Epub 2019 Sep 11.
8
Ultra-low dose chest computed tomography: Effect of iterative reconstruction levels on image quality.超低剂量胸部 CT:迭代重建水平对图像质量的影响。
Eur J Radiol. 2019 May;114:62-68. doi: 10.1016/j.ejrad.2019.02.021. Epub 2019 Feb 18.
9
Estimating detectability index : development and validation of an automated methodology.估计可检测性指数:一种自动化方法的开发与验证
J Med Imaging (Bellingham). 2018 Jul;5(3):031403. doi: 10.1117/1.JMI.5.3.031403. Epub 2017 Dec 11.
10
Comparison of image quality and visibility of normal and abnormal findings at submillisievert chest CT using filtered back projection, iterative model reconstruction (IMR) and iDose™.使用滤波反投影、迭代模型重建(IMR)和iDose™对亚毫西弗胸部CT的正常和异常表现的图像质量及可视性进行比较。
Eur J Radiol. 2016 Nov;85(11):1971-1979. doi: 10.1016/j.ejrad.2016.09.001. Epub 2016 Sep 7.