Deep Learning-Based Estimation of Radiographic Position to Automatically Set Up the X-Ray Prime Factors.

C F Del Cerro, R C Giménez, J García-Blas, K Sosenko, J M Ortega, M Desco, M Abella
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Abstract

Radiation dose and image quality in radiology are influenced by the X-ray prime factors: KVp, mAs, and source-detector distance. These parameters are set by the X-ray technician prior to the acquisition considering the radiographic position. A wrong setting of these parameters may result in exposure errors, forcing the test to be repeated with the increase of the radiation dose delivered to the patient. This work presents a novel approach based on deep learning that automatically estimates the radiographic position from a photograph captured prior to X-ray exposure, which can then be used to select the optimal prime factors. We created a database using 66 radiographic positions commonly used in clinical settings, prospectively obtained during 2022 from 75 volunteers in two different X-ray facilities. The architecture for radiographic position classification was a lightweight version of ConvNeXt trained with fine-tuning, discriminative learning rates, and a one-cycle policy scheduler. Our resulting model achieved an accuracy of 93.17% for radiographic position classification and increased to 95.58% when considering the correct selection of prime factors, since half of the errors involved positions with the same KVp and mAs values. Most errors occurred for radiographic positions with similar patient pose in the photograph. Results suggest the feasibility of the method to facilitate the acquisition workflow reducing the occurrence of exposure errors while preventing unnecessary radiation dose delivered to patients.

基于深度学习的 X 射线位置估计,自动设置 X 射线主因。
放射科的辐射剂量和图像质量受 X 射线主要因素的影响:KVp、mAs 和源-探测器距离。这些参数由 X 射线技术员在采集之前根据射线位置进行设置。这些参数的错误设置可能会导致曝光错误,从而被迫重复测试,增加对患者的辐射剂量。这项工作提出了一种基于深度学习的新方法,它能根据 X 射线曝光前拍摄的照片自动估计射线位置,然后用于选择最佳质因子。我们创建了一个数据库,其中使用了 66 个临床常用的放射位置,这些位置是 2022 年期间在两个不同的 X 射线设施中从 75 名志愿者那里获取的。放射位置分类的架构是经过微调、判别学习率和单周期策略调度器训练的轻量级 ConvNeXt 版本。由于一半的错误涉及具有相同 KVp 和 mAs 值的位置,因此我们的模型在放射位置分类方面达到了 93.17% 的准确率,在考虑正确选择质因数后,准确率提高到 95.58%。大多数错误发生在照片中患者姿势相似的放射位置上。结果表明,该方法是可行的,既能简化采集工作流程,减少曝光错误的发生,又能防止患者受到不必要的辐射剂量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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