Accuracy of implementing principles of fusion imaging in the follow up and surveillance of complex aneurysm repair. (October 2018)

• Record Type: Journal Article
• Authors: Martin-Gonzalez, Teresa; Penney, Graeme; Chong, Debra; Davis, Meryl; Mastracci, Tara M
• Is Part Of: Vascular medicine. Volume 23:Number 5(2018)
• Page Start: 461
• Page End: 466
• Publication Date: 2018-10
• Subjects: abdominal aortic aneurysm (AAA) -- endovascular therapy -- vascular imaging; Blood-vessels -- Diseases -- Periodicals; Peripheral vascular diseases -- Periodicals; Vascular Diseases -- Periodicals; Vaisseaux sanguins -- Maladies -- Périodiques; Maladies vasculaires périphériques -- Périodiques; 616.13
• DOI: 10.1177/1358863X18768885
• Languages: English
• ISSNs: 1358-863X

Abstract

Fusion imaging is standard for the endovascular treatment of complex aortic aneurysms, but its role in follow up has not been explored. A critical issue is renal function deterioration over time. Renal volume has been used as a marker of renal impairment; however, it is not reproducible and remains a complex and resource-intensive procedure. The aim of this study is to determine the accuracy of a fusion-based software to automatically calculate the renal volume changes during follow up. In this study, computerized tomography (CT) scans of 16 patients who underwent complex aortic endovascular repair were analysed. Preoperative, 1-month and 1-year follow-up CT scans have been analysed using a conventional approach of semi-automatic segmentation, and a second approach with automatic segmentation. For each kidney and at each time point the percentage of change in renal volume was calculated using both techniques. After review, volume assessment was feasible for all CT scans. For the left kidney, the intraclass correlation coefficient (ICC) was 0.794 and 0.877 at 1 month and 1 year, respectively. For the right side, the ICC was 0.817 at 1 month and 0.966 at 1 year. The automated technique reliably detected a decrease in renal volume for the eight patients with occluded renal arteries during follow up. This is the first report of a fusion-based algorithm to detect changes in renal volume during postoperative surveillance using an automated process. Using this technique, the standardized assessment of renal volume could be implemented with greater ease and reproducibility and serve as a warning of potential renal impairment.