A Deep Learning Approach to Visualise Aortic Aneurysm Morphology without the Use of Intravenous Contrast Agents

Background Intravenous contrast agents are routinely used in computerized tomography (CT) imaging to enable the visualisation of intravascular pathology, such as with abdominal aortic aneurysms (AAA). However, the injection is contraindicated in patients with iodine allergy and is associated with renal complications. Objectives In this study, we investigate if the raw data acquired from a non-contrast CT image contains sufficient information to differentiate blood and other soft tissue components. A deep learning pipeline underpinned by Generative Adversarial Networks was developed to simulate contrast enhanced CTA images using non-contrast CTs. Methods and results Two generative models (Cycle- and Conditional) are trained with paired non-contrast and contrast enhanced CTs from seventy-five patients (total of 11,243 pairs of images) with abdominal aortic aneurysms (AAA) in a 3-fold cross-validation approach with a training/testing split of 50:25 patients. Subsequently, models were evaluated on an independent validation cohort of 200 patients (total of 29,468 pairs of images). Both deep learning generative models are able to perform this image transformation task with the Cycle-GAN model outperforming the Conditional-GAN model as measured by aneurysm lumen segmentation accuracy (Cycle-GAN: 86.1 ± 12.2% vs. Con-GAN: 85.7 ± 10.4%) and thrombus spatial morphology classification accuracy (Cycle-GAN: 93.5% vs. Con-GAN: 85.7%). Conclusion This pipeline implements deep learning methods to generate CTAs from non-contrast images, without the need of contrast injection, that bear strong concordance to the ground truth and enable the assessment of important clinical metrics. Our pipeline is poised to disrupt clinical pathways requiring intravenous contrast.