XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors
This work aims to assist physicians in interpreting anatomy more reliably by providing synthesized novel radiographic views, which is a new capability for medical imaging analysis.
This paper addresses the challenge of synthesizing novel radiographic views from a single radiograph, a task that is ill-posed and lacks paired data. The authors developed XraySyn, a method that leverages CT for radiograph simulation and a differentiable projection algorithm to achieve geometrically consistent transformations, enabling the synthesis of novel views on real radiographs.
A radiograph visualizes the internal anatomy of a patient through the use of X-ray, which projects 3D information onto a 2D plane. Hence, radiograph analysis naturally requires physicians to relate the prior about 3D human anatomy to 2D radiographs. Synthesizing novel radiographic views in a small range can assist physicians in interpreting anatomy more reliably; however, radiograph view synthesis is heavily ill-posed, lacking in paired data, and lacking in differentiable operations to leverage learning-based approaches. To address these problems, we use Computed Tomography (CT) for radiograph simulation and design a differentiable projection algorithm, which enables us to achieve geometrically consistent transformations between the radiography and CT domains. Our method, XraySyn, can synthesize novel views on real radiographs through a combination of realistic simulation and finetuning on real radiographs. To the best of our knowledge, this is the first work on radiograph view synthesis. We show that by gaining an understanding of radiography in 3D space, our method can be applied to radiograph bone extraction and suppression without groundtruth bone labels.