Bone Suppression on Chest Radiographs With Adversarial Learning
This addresses the need for improved diagnostic tools in thoracic pathology without specialized hardware or higher radiation doses, though it is incremental as it builds on existing GAN methods for medical imaging.
The paper tackled the problem of generating bone-suppressed chest radiographs from conventional ones using GANs, achieving radio-realistic results with suppressed bony structures and few motion artifacts, with paired training yielding slightly better SSIM and PSNR scores but unpaired training showing better generalization to unseen AP radiographs.
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.