Virtual Dual Energy Chest Imaging by Convolutional Neural Networks Donghoon Lee, Yonsei University; Hee-Joung Kim Introduction Conventional chest radiography has been known as the most effective tool for lung cancer detection and diagnosis, although a high percentage of lung cancer tumors are missing because of the overlap of lung nodule image contrast with bone image contrast in chest radiography. To address this problem, two different energy subtraction strategies with dual exposures were studied for decomposing a radiograph into bone-free and soft tissue free images. Dual energy chest radiography has been widely accepted in clinical practice because of its clinical value that can improve diagnostic efficiency. However, in dual energy chest radiography, there are problems such as high radiation dose and motion artifacts due to double exposure with different energies. In this study, we tried to solve these problems using deep learning. Hypothesis We assumed that there is a nonlinear relationship between single energy image and dual energy images. If this nonlinear relationship can be deduced through deep learning, a dual energy image can be generated from single energy chest radiography without double exposures. Methods We used chest radiograms in lung image database consortium image collection (LIDC-IDRI) database to develop a deep learning model. The training data used in this study were a pair of single energy and dual energy chest radiograms. Single energy chest radiogram was used as input data of deep learning model and dual energy soft tissue free image was used as output data. The deep learning model used in the development is a U-net based model and we added a shortcut connection, which is used in residual learning method, between the convolution layers. To optimize the learning model, we used the adaptive moment estimation (ADAM) optimization method. The virtual dual energy bone free chest radiogram was obtained by subtracting the predicted dual energy soft tissue free chest radiograms from the conventional single energy chest radiogram. Results Our deep learning model was able to extract the bone signal from the chest radiograms. Predicted virtual dual energy soft tissue free image was similar to the real dual energy images and the structure similarity index measure (SSIM) also reached more than 0.9. The predicted images better visualized the bone structure than conventional chest radiograms. In addition, the developed deep learning model accurately predicted the rib bone fracture site. The virtual dual energy bone free chest radiogram obtained by subtracting the predicted images from conventional chest radiogram improved the visibility of the lung field by eliminating the ribs present in the lung field. Conclusion We have developed a method to generate a dual energy image without double exposures. This method is considered to be able to compensate for the high radiation dose and motion artifact that have been raised as problems in conventional dual energy chest radiography. Statement of Impact The proposed method is a technology that can provide dual energy image economically without hardware help or modification of medical device. In addition, proposed method is considered to be a technique that can increase the usefulness and utilization of medical image data because existing chest radiograms can be transformed into dual energy chest radiograms. Keywords chest radiography, dual energy chest radiography, deep learning

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