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3rd July 2023

DBGAN fuses CT and MRI scans with AI

Researchers at both the Shangdong University of Technology in Zibo, China and the University of London, UK, have developed a method to fuse two types of clinical scans using artificial intelligence (AI).

This fusion, known as “Dual-Branch Generative Adversarial Network” (DBGAN), can offer clinicians a far more comprehensive representation of a patient’s anatomy and reveal hidden details of physical problems.

Separately, clinical X-ray computed tomography (CT) scans have been used to capture detailed images of bone structures using X-ray technology, while magnetic resonance imaging (MRI) scans produce precise images of soft tissues using magnetic radio waves.

Research published in the International Journal of Biomedical Engineering and Technology has demonstrated that through the use of deep-learning algorithms, DBGAN can create a fusion combining both CT and MRI scans.

DBGAN to improve accuracy

By extracting the most salient features and complementary information, radiographers can enhance their diagnostic capabilities and overcome the limitations of both images.

“Fundamentally, the DBGAN fusion retains the bone structure details commonly accessible with a CT image and the soft tissue information provided by an MRI scan”
Zhai et al.

DBGAN successfully fuses CT and MRI scans with AI.

The AI technique minimises artefacts and maximises visual information in these fused images.

Demonstrations in the journals have shown significantly superior performance in terms of image quality and diagnostic accuracy compared to existing techniques.

The method also makes use of multiple discriminators which assess the quality of the generated images, by comparing them to real images and discarding lower-quality returns until a high-quality fusion is achieved.

Once the images are successfully attained, the DBGAN then utilises two multiscale extraction models. Focusing on extracting important features and detailed information from the fused scans and highlighting the most relevant and distinctive features.

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