Evaluation of aorta morphology and function in presence of aneurysms or dissection is crucial for a correct treatment choice between surgical resection and percutaneous stent-graft deployment. We developed and tested a new method for automated dynamic aorta segmentation from computed tomography (CT) images IntroductionAneurysm and dissection are the most dangerous diseases that affect the aorta. In presence of these pathologies the evaluation of aorta morphology and function is crucial for a correct treatment choice between surgical resection and percutaneous stent-graft deployment. In addition, in case of stent-graft deployment, imaging techniques such as computed tomography (CT) have the fundamental role in the search for anatomic details necessary to evaluate the most suitable anatomy for stent-graft and in the postoperative follow-up after stent graft placement. It is well known that this knowledge is crucial to improve its durability and results [1,2].Since no analysis software is available on the CT imaging system to extract quantitative parameters that could be very useful to evaluate aorta morphology and function, in clinical practice, medical doctors usually perform manual measurements of aorta diameters in specific anatomical sites. This procedure is subjective and time-consuming. Accordingly, our aims were to: (1) develop an automated technique for aortic surface detection throughout the cardiac cycle and (2) measure static and dynamic parameters characterizing aorta morphology and function.As a first step, quantitative analysis of CT images requires segmentation of the aorta. Previous studies targeting aortic aneurysm segmentation employed a level set framework using either edge strength or region intensity information [3][4][5]. To extract the aortic surface in the 3D domain we applied a level set segmentation scheme [6-8] that incorporates gradient-based, weighted expansion and mean curvature dependent regularizers. The final results are the surfaces corresponding to the aortic vessel throughout the cardiac cycle. Once the vessel is segmented it is possible to calculate various descriptive indexes about it, including shape, size and its dynamic behaviour. Our segmentation method was implemented in the 3D domain and requires a simple definition of few reference points within the data as initial condition for the dynamic detection of the aorta boundaries throughout the cardiac cycle. MethodsThree subjects were imaged using a multi-detector CT scanner (Siemens, SOMATOM Sensation Cardiac): one normal (NL) and two patients (PTS) affected by an aneurysm in the ascending (asc) and descending (desc) aorta respectively. Cine-loops were acquired during breath-hold and ECG-gating (1 mm slice thickness) with a temporal resolution of 10 frames per cardiac cycle, after beta-blocker and intra-vascular contrast injection. Image analysisThe CT datasets were analyzed using custom software,

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