X-ray Dissectography Enables Stereotography to Improve Diagnostic Performance

TL;DR

This paper introduces x-ray dissectography, a novel digital technique that extracts specific organs from limited radiographic projections, enabling stereographic and tomographic analysis with lower radiation and cost.

Contribution

It presents a new deep learning-based x-ray dissectography method for isolating organs, enhancing diagnostic capabilities beyond traditional radiography and CT.

Findings

Successful extraction of lungs from radiographs

Feasibility of stereographic examination of isolated organs

Potential for CT-grade diagnosis with lower radiation

Abstract

X-ray imaging is the most popular medical imaging technology. While x-ray radiography is rather cost-effective, tissue structures are superimposed along the x-ray paths. On the other hand, computed tomography (CT) reconstructs internal structures but CT increases radiation dose, is complicated and expensive. Here we propose "x-ray dissectography" to extract a target organ/tissue digitally from few radiographic projections for stereographic and tomographic analysis in the deep learning framework. As an exemplary embodiment, we propose a general X-ray dissectography network, a dedicated X-ray stereotography network, and the X-ray imaging systems to implement these functionalities. Our experiments show that x-ray stereography can be achieved of an isolated organ such as the lungs in this case, suggesting the feasibility of transforming conventional radiographic reading to the stereographic examination of the isolated organ, which potentially allows higher sensitivity and specificity, and even tomographic visualization of the target. With further improvements, x-ray dissectography promises to be a new x-ray imaging modality for CT-grade diagnosis at radiation dose and system cost comparable to that of radiographic or tomosynthetic imaging.