X-ray2CTPA: leveraging diffusion models to enhance pulmonary embolism classification.
Authors
Affiliations (6)
Affiliations (6)
- Faculty of Engineering, Tel Aviv University, Tel-Aviv, Israel. [email protected].
- Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Radiology, Tel-Aviv Sourasky Medical Center and Tel Aviv University School of Medicine, Tel Aviv, Israel.
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel affiliated with the Tel Aviv University, Tel Aviv, Israel.
- Faculty of Engineering, Tel Aviv University, Tel-Aviv, Israel.
- Biomedical Engineering and Imaging Institute (BMEII), Dept. of Radiology, Icahn School of Medicine, Mount Sinai, NY, USA.
Abstract
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 employ the synthesized 3D images in a classification framework and show improved AUC in a Pulmonary Embolism (PE) categorization task, using the initial CXR input. Furthermore, we evaluate the model's performance using quantitative metrics, ensuring diagnostic relevance of the generated images. 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 .