Learning Bone Suppression from Dual Energy Chest X-rays using Adversarial Networks
This addresses the need for improved diagnostic accuracy in pulmonary diseases by reducing radiation exposure and artifacts, though it is incremental as it builds on existing dual-energy imaging techniques.
The paper tackled the problem of suppressing bones in chest X-rays to improve pathology classification by introducing a deep generative model that predicts bone-suppressed images from single-energy X-rays, achieving state-of-the-art performance compared to existing dual-energy methods.
Suppressing bones on chest X-rays such as ribs and clavicle is often expected to improve pathologies classification. These bones can interfere with a broad range of diagnostic tasks on pulmonary disease except for musculoskeletal system. Current conventional method for acquisition of bone suppressed X-rays is dual energy imaging, which captures two radiographs at a very short interval with different energy levels; however, the patient is exposed to radiation twice and the artifacts arise due to heartbeats between two shots. In this paper, we introduce a deep generative model trained to predict bone suppressed images on single energy chest X-rays, analyzing a finite set of previously acquired dual energy chest X-rays. Since the relatively small amount of data is available, such approach relies on the methodology maximizing the data utilization. Here we integrate the following two approaches. First, we use a conditional generative adversarial network that complements the traditional regression method minimizing the pairwise image difference. Second, we use Haar 2D wavelet decomposition to offer a perceptual guideline in frequency details to allow the model to converge quickly and efficiently. As a result, we achieve state-of-the-art performance on bone suppression as compared to the existing approaches with dual energy chest X-rays.