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Dual-energy (DE) chest radiographs provide greater diagnostic informationthan standard radiographs by separating the image into bone and soft tissue,revealing suspicious lesions which may otherwise be obstructed from view.However, acquisition of DE images requires two physical scans, necessitatingspecialized hardware and processing, and images are prone to motion artifact.Generation of virtual DE images from standard, single-shot chest radiographswould expand the diagnostic value of standard radiographs without changing theacquisition procedure. We present a Multi-scale Conditional Adversarial Network(MCA-Net) which produces high-resolution virtual DE bone images from standard,single-shot chest radiographs. Our proposed MCA-Net is trained using theadversarial network so that it learns sharp details for the production ofhigh-quality bone images. Then, the virtual DE soft tissue image is generatedby processing the standard radiograph with the virtual bone image using a crossprojection transformation. Experimental results from 210 patient DE chestradiographs demonstrated that the algorithm can produce high-quality virtual DEchest radiographs. Important structures were preserved, such as coronarycalcium in bone images and lung lesions in soft tissue images. The averagestructure similarity index and the peak signal to noise ratio of the producedbone images in testing data were 96.4 and 41.5, which are significantly betterthan results from previous methods. Furthermore, our clinical evaluationresults performed on the publicly available dataset indicates the clinicalvalues of our algorithms. Thus, our algorithm can produce high-quality DEimages that are potentially useful for radiologists, computer-aideddiagnostics, and other diagnostic tasks.

Generation of Virtual Dual Energy Images from Standard Single-Shot Radiographs using Multi-scale and Conditional Adversarial Network