Automated CTA based measurements for planning support of minimally invasive aortic valve replacement surgery. (January 2017)

• Record Type: Journal Article
• Authors: Elattar, Mustafa A.; van Kesteren, Floortje; Wiegerinck, Esther M.; Vanbavel, Ed; Baan, Jan; Cocchieri, Riccardo; de Mol, Bas; Planken, Nils R.; Marquering, Henk A.
• Is Part Of: Medical engineering & physics. Volume 39(2017)
• Page Start: 123
• Page End: 128
• Publication Date: 2017-01
• Subjects: CT angiograph -- Mini-AVR -- Segmentation -- Sinotubular junction -- Intercostal spaces; AVR Aortic Valve Replacement -- Mini-AVR Minimally invasive Aortic Valve Replacement -- MS Ministernotomy -- RT Right Anterior Minithoracotomy -- STJ Sinotubular Junction -- ICS Intercostal Space -- CTA Computed Tomography Angiography -- MPR Multi Planar Reformat -- MIP Maximum Intensity Projection -- HU Hounsfield Units -- ICC Intraclass Correlation Coefficient; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2016.11.002
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: An automated method determining the optimal incision location for mini-AVR is proposed. The method showed good accuracy compared with expert delineation. For 60% of patients, the fourth ICS was the closest to the STJ's center. This is the first automated method for detecting optimal incision locations. The solution is potentially valuable to support clinical decision making by surgeons. Abstract: Minimally invasive aortic valve replacement (mini-AVR) procedures are a valuable alternative to conventional open heart surgery. Currently, planning of mini-AVR consists of selection of the intercostal space closest to the sinotubular junction on preoperative computer tomography images. We developed an automated algorithm detecting the sinotubular junction (STJ) and intercostal spaces for finding the optimal incision location. The accuracy of the STJ detection was assessed by comparison with manual delineation by measuring the Euclidean distance between the manually and automatically detected points. In all 20 patients, the intercostal spaces were accurately detected. The median distance between automated and manually detected STJ locations was 1.4 [IQR = 0.91–4.7] mm compared to the interobserver variation of 1.0 [IQR = 0.54–1.3] mm. For 60% of patients, the fourth intercostal space was the closest to the STJ. The proposed algorithm is the first automated approach for detecting optimal incision location and has the potential to be implemented in clinical practice for planning of various mini-AVR procedures.