Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Bustamante, Mariana; Petersson, Sven; Eriksson, Jonatan; Alehagen, Urban; Dyverfeldt, Petter; Carlhäll, Carl-Johan; Ebbers, Tino
• Is Part Of: Journal of cardiovascular magnetic resonance. Volume 17:Issue 1(2015)
• Page Start: 1
• Page End: 12
• Publication Date: 2015-12
• Subjects: 4D flow cardiovascular magnetic resonance (4D flow CMR) -- Flow volume -- Image segmentation -- Image registration -- Phase contrast; Cardiovascular system -- Magnetic resonance imaging -- Periodicals; 616.1207548
• DOI: 10.1186/s12968-015-0190-5
• Languages: English
• ISSNs: 1097-6647

Abstract

Abstract Background Flow volume quantification in the great thoracic vessels is used in the assessment of several cardiovascular diseases. Clinically, it is often based on semi-automatic segmentation of a vessel throughout the cardiac cycle in 2D cine phase-contrast Cardiovascular Magnetic Resonance (CMR) images. Three-dimensional (3D), time-resolved phase-contrast CMR with three-directional velocity encoding (4D flow CMR) permits assessment of net flow volumes and flow patterns retrospectively at any location in a time-resolved 3D volume. However, analysis of these datasets can be demanding. The aim of this study is to develop and evaluate a fully automatic method for segmentation and analysis of 4D flow CMR data of the great thoracic vessels. Methods The proposed method utilizes atlas-based segmentation to segment the great thoracic vessels in systole, and registration between different time frames of the cardiac cycle in order to segment these vessels over time. Additionally, net flow volumes are calculated automatically at locations of interest. The method was applied on 4D flow CMR datasets obtained from 11 healthy volunteers and 10 patients with heart failure. Evaluation of the method was performed visually, and by comparison of net flow volumes in the ascending aorta obtained automatically (using the proposed method), and semi-automatically. Further evaluation was done by comparison of net flow volumes obtained automatically at different locations in the aorta, pulmonary artery, and caval veins. Results Visual evaluation of the generated segmentations resulted in good outcomes for all the major vessels in all but one dataset. The comparison between automatically and semi-automatically obtained net flow volumes in the ascending aorta resulted in very high correlation (r 2 =0.926). Moreover, comparison of the net flow volumes obtained automatically in other vessel locations also produced high correlations where expected: pulmonary trunk vs. proximal ascending aorta (r 2 =0.955), pulmonary trunk vs. pulmonary branches (r 2 =0.808), and pulmonary trunk vs. caval veins (r 2 =0.906). Conclusions The proposed method allows for automatic analysis of 4D flow CMR data, including vessel segmentation, assessment of flow volumes at locations of interest, and 4D flow visualization. This constitutes an important step towards facilitating the clinical utility of 4D flow CMR.