Automated identification, hemodynamic visualisation and quantification of abdominal aortic aneurysms with 4D flow magnetic resonance imaging

Background: Four dimensional (4D) flow magnetic resonance imaging (MRI) enables both visualisation and quantification of hemodynamics in abdominal aortic aneurysms (AAAs). However, its clinical implementation remains limited due to time-consuming and cumbersome post-processing, as well as a lack of standardisation. While previous work has proposed automated solutions for thoracic and intracranial cases, applications in the abdominal region, particularly for AAAs, remain underexplored. The aim of this study was to develop an automated post-processing pipeline for 4D flow MRI in AAA patients, incorporating aneurysm identification, via automated segmentation, blood flow visualisation and hemodynamic parameter quantification. Methods: 4D flow MRI and three dimensional (3D) cine balanced steady state free precession (bSSFP) scans were acquired from 16 patients with an AAA in the same field of view and with an isotropic resolution of 1 mm. Using 5-fold cross-validation, an nnU-Net was trained to segment the aorta based on 3D cine bSSFP MRI. The automated post-processing pipeline was built using MATLAB. Wall shear stress (WSS) values were assessed within the extracted aneurysm. nnU-Net performance was assessed with the Dice-similarity coefficient (DSC), 95% percentile Hausdorff distance (HD95) and by calculating the Pearson correlation coefficients between WSS values obtained in the aneurysm based on segmentation with both methods. Results: The resulting post-processing pipeline demonstrated robustness to anatomical variability, including tortuosity and intraluminal thrombus (ILT), both characteristic of AAA, and consistently produced output without user-interference. The nnU-Net performance was excellent for lumen segmentation and good for ILT segmentation, with DSCs of 0.93 (0.05) and 0.80 (0.26) respectively. The HD95 was 4.87 (8.59) mm and 6.04 (3.71) mm for respectively lumen and thrombus segmentation. Significant correlations (ρ =0.92 or higher) were found between WSS values derived using the manual and nnU-Net segmentations. Conclusions: An automated post-processing pipeline was developed specifically for 4D flow MRI in patients with AAA. The pipeline is robust for varying anatomies and may facilitate 4D flow MRI implementation in the clinical workflow.