X-ray Dissectography Enables Stereotography to Improve Diagnostic Performance
This addresses a critical limitation in medical imaging by improving diagnostic performance for patients, though it appears incremental as it builds on existing x-ray and deep learning methods.
The authors tackled the problem of tissue superposition in x-ray radiography by proposing x-ray dissectography to digitally extract target organs from few projections, enabling stereographic and tomographic analysis with deep learning. Their experiments on lungs showed feasibility for higher sensitivity and specificity, potentially allowing CT-grade diagnosis at lower radiation dose and cost.
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.