X-ray In-Depth Decomposition: Revealing The Latent Structures

TL;DR

This paper introduces a deep learning method to decompose X-ray images into independent sub-volumes, enhancing interpretability and potentially reducing the need for multiple perspectives or high radiation doses.

Contribution

The work presents a novel deep learning approach for decomposing X-ray images into independent components, addressing the challenge of modeling highly ill-posed problems in medical imaging.

Findings

Encouraging results demonstrate effective decomposition of X-ray images.

The approach advances interpretability of X-ray images in medical diagnosis.

Potential to reduce radiation exposure and imaging from multiple perspectives.

Abstract

X-ray radiography is the most readily available imaging modality and has a broad range of applications that spans from diagnosis to intra-operative guidance in cardiac, orthopedics, and trauma procedures. Proper interpretation of the hidden and obscured anatomy in X-ray images remains a challenge and often requires high radiation dose and imaging from several perspectives. In this work, we aim at decomposing the conventional X-ray image into d X-ray components of independent, non-overlapped, clipped sub-volumes using deep learning approach. Despite the challenging aspects of modeling such a highly ill-posed problem, exciting and encouraging results are obtained paving the path for further contributions in this direction.