# Novel-view X-ray Projection Synthesis through Geometry-Integrated Deep Learning

arXiv: https://arxiv.org/abs/2504.11953 · HTML: https://mlsanity.com/paper/2504.11953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 9 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations](https://mlsanity.com/paper/1906.01618/) (2019)
- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)

## Abstract (arXiv)

X-ray imaging plays a crucial role in the medical field, providing essential insights into the internal anatomy of patients for diagnostics, image-guided procedures, and clinical decision-making. Traditional techniques often require multiple X-ray projections from various angles to obtain a comprehensive view, leading to increased radiation exposure and more complex clinical processes. This paper explores an innovative approach using the DL-GIPS model, which synthesizes X-ray projections from new viewpoints by leveraging a single existing projection. The model strategically manipulates geometry and texture features extracted from an initial projection to match new viewing angles. It then synthesizes the final projection by merging these modified geometry features with consistent texture information through an advanced image generation process. We demonstrate the effectiveness and broad applicability of the DL-GIPS framework through lung imaging examples, highlighting its potential to revolutionize stereoscopic and volumetric imaging by minimizing the need for extensive data acquisition.