文章：

基于深度卷积神经网络的动态胸部X光影像模拟技术：一项概念验证研究

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study

原文发布日期：8 December 2023

DOI: 10.3390/cancers15245768

类型: Article

开放获取: 是

英文摘要：

In this study, we present an innovative approach that harnesses deep neural networks to simulate respiratory lung motion and extract local functional information from single-phase chest X-rays, thus providing valuable auxiliary data for early diagnosis of lung cancer. A novel radiograph motion simulation (RMS) network was developed by combining a U-Net and a long short-term memory (LSTM) network for image generation and sequential prediction. By utilizing a spatial transformer network to deform input images, our proposed network ensures accurate image generation. We conducted both qualitative and quantitative assessments to evaluate the effectiveness and accuracy of our proposed network. The simulated respiratory motion closely aligns with pulmonary biomechanics and reveals enhanced details of pulmonary diseases. The proposed network demonstrates precise prediction of respiratory motion in the test cases, achieving remarkable average Dice scores exceeding 0.96 across all phases. The maximum variation in lung length prediction was observed during the end-exhale phase, with average deviation of 4.76 mm (±6.64) for the left lung and 4.77 mm (±7.00) for the right lung. This research validates the feasibility of generating patient-specific respiratory motion profiles from single-phase chest radiographs.

摘要翻译： 

本研究提出一种创新方法，利用深度神经网络模拟呼吸肺运动并从单期相胸部X光片中提取局部功能信息，从而为肺癌早期诊断提供有价值的辅助数据。通过结合U-Net和长短期记忆（LSTM）网络进行图像生成与序列预测，我们开发了新型放射影像运动模拟（RMS）网络。该网络采用空间变换器对输入图像进行形变处理，确保了图像生成的精确性。我们通过定性与定量评估验证了所提网络的有效性和准确性。模拟的呼吸运动与肺部生物力学特征高度吻合，并能增强肺部疾病的细节显示。实验表明该网络能精准预测测试病例的呼吸运动，所有期相的平均Dice系数均超过0.96。在呼气末阶段观察到肺长度预测的最大变异，左肺平均偏差为4.76毫米（±6.64），右肺为4.77毫米（±7.00）。本研究验证了从单期相胸部X光片生成患者特异性呼吸运动模式的可行性。

原文链接：

Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study