Aorta-aware GAN for non-contrast to artery contrasted CT translation and its application to abdominal aortic aneurysm detection.

PURPOSE

Artery contrasted computed tomography (CT) enables accurate observations of the arteries and surrounding structures, thus being widely used for the diagnosis of diseases such as aneurysm. To avoid the complications caused by contrast agent, this paper proposes an aorta-aware deep learning method to synthesize artery contrasted CT volume form non-contrast CT volume.

METHODS

By introducing auxiliary multi-resolution segmentation tasks in the generator, we force the proposed network to focus on the regions of aorta and the other vascular structures. Then, the segmentation results produced by the auxiliary tasks were used to extract aorta. The detection of abnormal CT images containing aneurysm was implemented by estimating the maximum axial radius of aorta.

RESULTS

In comparison with the baseline models, the proposed network with auxiliary tasks achieved better performances with higher peak signal-noise ratio value. In aorta regions which are supposed to be the main region of interest in many clinic scenarios, the average improvement can be up to 0.33dB. Using the synthesized artery contrasted CT, the F score of aneurysm detection achieved 0.58 at slice level and 0.85 at case level.

CONCLUSION

This study tries to address the problem of non-contrast to artery contrasted CT modality translation by employing a deep learning model with aorta awareness. The auxiliary tasks help the proposed model focus on aorta regions and synthesize results with clearer boundaries. Additionally, the synthesized artery contrasted CT shows potential in identifying slices with abdominal aortic aneurysm, and may provide an option for patients with contrast agent allergy.