An AI-based universal phantom analysis method based on XML-SVG wireframes with novel functional object identifiers.
An AI-based universal phantom analysis method based on XML-SVG wireframes with novel functional object identifiers.
复制标题
一种基于 AI 的通用模型分析方法，基于 XML-SVG 线框，具有新颖的功能对象标识符。
DOI：
10.1088/1361-6560/acdb44
复制
发表时间：
2023
影响因子：
3.5
通讯作者：
Wiersma,RodneyD
中科院分区：
文献类型：
--
作者：
Sakaamini,Ahmad;VanSlyke,Alexander;Partouche,Julien;Wu,Tianming;Wiersma,RodneyD
质量保证（QA）测试必须定期进行，以确保医疗器械在设计规范内运行。已经开发了许多QA模型和软件包，以便于测量机器性能。然而，由于分析软件中几何体模定义的硬编码性质，用户通常仅限于使用兼容QA体模的一小部分。在这项工作中，我们提出了一种新的基于AI的通用幻影（UniPhan）算法，不是幻影特定的，可以很容易地适应任何预先存在的基于图像的QA phantom.ApproachExtensible标记语言可扩展矢量图形（XML-SVG）进行了修改，包括几个新的标签描述的功能嵌入式幻影对象用于QA分析。功能标签包括对比度和密度塞、空间线性标记、分辨率条和边缘、均匀性区域和光辐射场重合区域。使用机器学习开发用于自动体模类型检测的图像分类模型。AI体模识别后，UniPhan导入相应的XML-SVG线框，将其配准到QA过程中拍摄的图像，对功能标签进行分析，并导出结果以与预期器械规范进行比较。分析结果与手工图像分析生成的结果进行了比较。主要结果XML-SVG线框生成了几个商业体模，包括特定的CT，CBCT，kV平面成像，MV成像。开发了几个功能对象，并将其分配给幻影的图形元素。测试了AI分类模型的训练和验证准确性和损失，沿着体模类型预测准确性和速度。结果显示，训练和验证准确率为99%，体模类型预测置信度约为100%，预测速度约为0.1 s。与手动图像分析相比，UniPhan的结果在所有指标上都是一致的，包括对比度噪声比、调制传递函数、HU精度和均匀性。显著性UniPhan方法可以识别体模类型，并使用其相应的线框进行QA分析。由于这些线框可以通过多种方式生成，因此这代表了一种分析基于图像的QA体模的易于使用的自动化方法，该方法在范围和实施方面都很灵活。
ObjectiveQuality assurance (QA) testing must be performed at regular intervals to ensure that medical devices are operating within designed specifications. Numerous QA phantoms and software packages have been developed to facilitate measurements of machine performance. However, due to the hard-coded nature of geometric phantom definition in analysis software, users are typically limited to the use of a small subset of compatible QA phantoms. In this work, we present a novel AI-based universal Phantom (UniPhan) algorithm that is not phantom specific and can be easily adapted to any pre-existing image-based QA phantom.ApproachExtensible Markup Language Scalable Vector Graphics (XML-SVG) was modified to include several new tags describing the function of embedded phantom objects for use in QA analysis. Functional tags include contrast and density plugs, spatial linearity markers, resolution bars and edges, uniformity regions, and light-radiation field coincidence areas. Machine learning was used to develop an image classification model for automatic phantom type detection. After AI phantom identification, UniPhan imported the corresponding XML-SVG wireframe, registered it to the image taken during the QA process, performed analysis on the functional tags, and exported results for comparison to expected device specifications. Analysis results were compared to those generated by manual image analysis.Main resultsXML-SVG wireframes were generated for several commercial phantoms including ones specific to CT, CBCT, kV planar imaging, and MV imaging. Several functional objects were developed and assigned to the graphical elements of the phantoms. The AI classification model was tested for training and validation accuracy and loss, along with phantom type prediction accuracy and speed. The results reported training and validation accuracies of 99%, phantom type prediction confidence scores of around 100%, and prediction speeds of around 0.1 s. Compared to manual image analysis, Uniphan results were consistent across all metrics including contrast-to-noise ratio, modulation-transfer function, HU accuracy, and uniformity.SignificanceThe UniPhan method can identify phantom type and use its corresponding wireframe to perform QA analysis. As these wireframes can be generated in a variety of ways this represents an accessible automated method of analyzing image-based QA phantoms that is flexible in scope and implementation.