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

Simple Summary Dynamic chest radiographs offer a distinct advantage over traditional chest radiographs by integrating motion and functional data, elevating their significance in clinical diagnostics. This study introduces a pioneering technique employing deep neural networks to simulate respiratory lung motion and extract local functional details from single-phase chest X-rays, thereby enhancing lung cancer clinical diagnostic capabilities. Our research establishes the viability of generating patient-specific respiratory motion profiles from single-phase chest radiographs. The evaluation of results from the network developed here underscores its substantial accuracy and fidelity, affirming its robustness in providing valuable supplementary insights into pulmonary function.Abstract 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.