Alawaji Zeyad, Taba Seyedamir Tavakoli, Cartwright Lucy, Rae William
Discipline of Medical Imaging Science, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
Department of Radiologic Technology, College of Applied Medical Sciences, Qassim University, Buraydah, Saudi Arabia.
J Appl Clin Med Phys. 2024 Dec;25(12):e14548. doi: 10.1002/acm2.14548. Epub 2024 Oct 9.
To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images.
Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses).
The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61 mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25 mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15 mm.
Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.
开发并验证一种用于美国放射学会（ACR）数字乳腺摄影（DM）体模图像质量评估的自动化软件分析方法。
使用富士乳腺摄影系统采集了27幅DICOM图像。所有图像均由三位医学物理专家按照澳大利亚和新西兰皇家放射学会（RANZCR）乳腺摄影质量控制指南进行评估。为了增强我们算法的稳健性和敏感性评估，还纳入了一组来自Hologic乳腺摄影系统的12幅图像，以测试各种体模位置调整。该软件自动选择多个感兴趣区域（ROI）进行分析。主要使用模板匹配方法进行图像分析，随后采用另一种方法对每个目标对象（斑点组、纤维和肿块）进行定位和评分。
软件性能与观察者的平均评分显示出良好到优秀的一致性（斑点组、纤维和肿块的组内相关系数[ICC]分别为0.75、0.79、0.82）。在目标对象评分方面，人类观察者和软件之间未发现显著差异。两种方法对斑点组和肿块的评分均达到了通过标准。与软件相比，专家观察者对直径小于0.61毫米的纤维对象评分较低。当体模位置横向变化高达25毫米、旋转高达5度以及超出胸壁边缘高达15毫米时，该软件仍能够准确地对对象进行评分。
自动化软件分析是一种可行的方法，有助于提高乳腺摄影图像质量评估的一致性和可重复性，同时减少对人工交互和处理时间的依赖。
