A Deep Learning Approach to Visualize Aortic Aneurysm Morphology Without the Use of Intravenous Contrast Agents. Issue 2 (10th February 2023)

• Record Type: Journal Article
• Authors: Chandrashekar, Anirudh; Handa, Ashok; Lapolla, Pierfrancesco; Shivakumar, Natesh; Uberoi, Raman; Grau, Vicente; Lee, Regent
• Is Part Of: Annals of surgery. Volume 277:Issue 2(2023)
• Page Start: e449
• Page End: e459
• Publication Date: 2023-02-10
• Subjects: aortic aneurysms -- computer vision -- computerized tomography -- contrast-enhanced computerized tomography -- CTangiography -- deep learning -- generative adversarial network; Surgery -- Periodicals; 617.005
• DOI: 10.1097/SLA.0000000000004835
• Languages: English
• ISSNs: 0003-4932

Abstract

Abstract : Background: Intravenous contrast agents are routinely used in CT imaging to enable the visualization of intravascular pathology, such as with abdominal aortic aneurysms. 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 noncontrast 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 noncontrast CTs. Methods and Results: Two generative models (cycle- and conditional) are trained with paired noncontrast and contrast enhanced CTs from seventy-five patients (total of 11, 243 pairs of images) with abdominal aortic aneurysms 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-generative adversarial network (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 noncontrast 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.