Automated multi‐atlas segmentation of cardiac 4D flow MRI

HighlightsWe propose a multi‐atlas based segmentation method for cardiac 4D Flow MRI.The method segments the cardiac chambers and great thoracic vessels in the 4D Flow MR.The segmentations are time‐resolved over the cardiac cycle.The proposed segmentation technique was evaluated on 110 4D Flow MRI datasets. Graphical abstract Figure. No caption available. ABSTRACT Four‐dimensional (4D) flow magnetic resonance imaging (4D Flow MRI) enables acquisition of time‐resolved three‐directional velocity data in the entire heart and all major thoracic vessels. The segmentation of these tissues is typically performed using semi‐automatic methods. Some of which primarily rely on the velocity data and result in a segmentation of the vessels only during the systolic phases. Other methods, mostly applied on the heart, rely on separately acquired balanced Steady State Free Precession (b‐SSFP) MR images, after which the segmentations are superimposed on the 4D Flow MRI. While b‐SSFP images typically cover the whole cardiac cycle and have good contrast, they suffer from a number of problems, such as large slice thickness, limited coverage of the cardiac anatomy, and being prone to displacement errors caused by respiratory motion. To address these limitations we propose a multi‐atlas segmentation method, which relies only on 4D Flow MRI data, to automatically generate four‐dimensional segmentations that include the entire thoracic cardiovascular system present in these datasets. The approach was evaluated on 4D Flow MR datasets from a cohort of 27 healthy volunteers and 83 patients with mildly impaired systolic left‐ventricular function. Comparison of manual and automatic segmentations of the cardiac chambers at end‐systolic and end‐diastolic timeframes showed agreements comparable to those previously reported for automatic segmentation methods of b‐SSFP MR images. Furthermore, automatic segmentation of the entire thoracic cardiovascular system improves visualization of 4D Flow MRI and facilitates computation of hemodynamic parameters.