# X-ray Dissectography Enables Stereotography to Improve Diagnostic Performance

arXiv: https://arxiv.org/abs/2111.15040 · HTML: https://mlsanity.com/paper/2111.15040/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 24 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset](https://mlsanity.com/paper/2005.06465/) (2020)
- [X2CT-GAN: Reconstructing CT from Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/1905.06902/) (2019)
- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)
- [Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction](https://mlsanity.com/paper/2007.03882/) (2020)
- [GAN-based disentanglement learning for chest X-ray rib suppression](https://mlsanity.com/paper/2110.09134/) (2021)
- [XraySyn: Realistic View Synthesis From a Single Radiograph Through CT Priors](https://mlsanity.com/paper/2012.02407/) (2020)
- [Encoding CT Anatomy Knowledge for Unpaired Chest X-ray Image Decomposition](https://mlsanity.com/paper/1909.12922/) (2019)
- [Lung Structures Enhancement in Chest Radiographs via CT based FCNN Training](https://mlsanity.com/paper/1810.05989/) (2018)
- [Noise Entangled GAN For Low-Dose CT Simulation](https://mlsanity.com/paper/2102.09615/) (2021)

## Abstract (arXiv)

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.