  Dual-energy (DE) chest radiography provides the capability of selectively
imaging two clinically relevant materials, namely soft tissues, and osseous
structures, to better characterize a wide variety of thoracic pathology and
potentially improve diagnosis in posteroanterior (PA) chest radiographs.
However, DE imaging requires specialized hardware and a higher radiation dose
than conventional radiography, and motion artifacts sometimes happen due to
involuntary patient motion. In this work, we learn the mapping between
conventional radiographs and bone suppressed radiographs. Specifically, we
propose to utilize two variations of generative adversarial networks (GANs) for
image-to-image translation between conventional and bone suppressed radiographs
obtained by DE imaging technique. We compare the effectiveness of training with
patient-wisely paired and unpaired radiographs. Experiments show both training
strategies yield "radio-realistic'' radiographs with suppressed bony structures
and few motion artifacts on a hold-out test set. While training with paired
images yields slightly better performance than that of unpaired images when
measuring with two objective image quality metrics, namely Structural
Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), training with
unpaired images demonstrates better generalization ability on unseen
anteroposterior (AP) radiographs than paired training.
