  Chest X-rays or chest radiography (CXR), commonly used for medical
diagnostics, typically enables limited imaging compared to computed tomography
(CT) scans, which offer more detailed and accurate three-dimensional data,
particularly contrast-enhanced scans like CT Pulmonary Angiography (CTPA).
However, CT scans entail higher costs, greater radiation exposure, and are less
accessible than CXRs. In this work we explore cross-modal translation from a 2D
low contrast-resolution X-ray input to a 3D high contrast and
spatial-resolution CTPA scan. Driven by recent advances in generative AI, we
introduce a novel diffusion-based approach to this task. We evaluate the models
performance using both quantitative metrics and qualitative feedback from
radiologists, ensuring diagnostic relevance of the generated images.
Furthermore, we employ the synthesized 3D images in a classification framework
and show improved AUC in a PE categorization task, using the initial CXR input.
The proposed method is generalizable and capable of performing additional
cross-modality translations in medical imaging. It may pave the way for more
accessible and cost-effective advanced diagnostic tools. The code for this
project is available: https://github.com/NoaCahan/X-ray2CTPA .
