Introduction: Surgery is needed when the thoracic aorta, the largest artery of the human body, is dilated or the inner wall is ruptured. In preoperative CT imaging, the anatomy can be assessed, and diameters can be measured to detect indications for surgery. A downside of this 3D imaging technique is that its conventional visualization is 2D. By annotating the thoracic aorta in a CT scan and adding together all labeled pixels, a 3D object can be created. The use of 3D visualization for preoperative planning of thoracic aortic repair already showed promising results.

Objective: The objective of this research is to train and test a deep learning model to automatically annotate the thoracic aorta on CT imaging. Based on an automatically reconstructed centerline, the diameter of the aorta can be determined perpendicular to the centerline. All this together will be implemented in a 3D visualization, which will be tested by aortic surgeons as a preoperative aortic repair planning tool.
Methods: A deep learning model was trained by CT scans and corresponding manual annotations of the thoracic aorta. To test the performance, the overlap of manual and automatic segmentations of a test dataset was determined. The created 3D visualization was used by aortic surgeons in the preoperative aortic repair planning of five patient cases. Manual diameter measurements were taken to compare to the automatic diameter measurements. The aortic surgeons who tested the 3D visualization were questioned about the ease of use, usefulness, and their attitude toward working with this software in the future.
Results: The average overlap of the manually and automatically created 3D models was 97.4%. The automatic diameter measurements showed high correspondence when compared to manual measurements done by four aortic surgeons. All surgeons responded positively to the questions concerning usefulness. According to the respondents, the software should be used as a supplement to conventional CT visualization, and not instead of.

Conclusion: The automatically created 3D visualization of the thoracic aorta based on CT imaging was successfully demonstrated. It showed a high ease of use and was of added value in the preoperative assessment of thoracic aortic anatomy. Further validation of the automatic diameter measurements is needed to implement this visualization in daily practice.

Introduction Aortic valve regurgitation (AR) is a common type of aortic valve disease that affects the surrounding tissues of the heart. Surgery may be required in patients with acute or chronic AR, aortic root disease or ascending aortic dissection. A viable alternative to traditional aortic valve replacement that offers encouraging clinical outcomes is aortic valve-sparing root replacement (VSRR). However, reoperation due to recurrent or residual AR has a high incidence. Transesophageal echocardiography (TEE) is the gold standard for intraoperatively evaluating residual AR. Evaluation is performed after aortic declamping; therefore, a longer bypass time is inevitable when additional repair is required. Furthermore, quantification of AR with TEE can be challenging. Therefore, the need for an early and quantitative assessment method rises. The main objective of this study was to identify the relationship between the intraoperative and quantitative assessment of the repaired aortic valve and the post-declamping echocardiographic assessment after VSRR. Echocardiographic parameters were studied to find the optimal parameter to identify this relationship. Furthermore, variables that influence the amount of leakage in the quantitative and intraoperative assessment of the repaired valve were identified. Two in vitro experiments were performed to investigate the contribution of these variables to the leakage of sufficient and insufficient aortic valves.

Experiment 1 The effective regurgitant orifice area was the optimal parameter to obtain the relationship between echocardiographic and quantitative assessment. It was implemented in an experimental setup as the orifice area. In addition to this parameter, the contribution of the orifice shape, pressure and fluid viscosity to the leakage was examined using a custom representation of the ascending aorta and the aortic valve. The orifice area was shown to have the most significant contribution to the amount of leakage, followed by the pressure and shape of the orifice. These three variables will be assessed in a second experiment. The viscosity of the fluid did not influence the leakage. 

Experiment 2 In a second experiment, the contribution of different variables to sufficient and insufficient aortic valves was evaluated at different pressures. The experiment was carried out in three cardiac tissues, in which first the contributions to the baseline leakage of a sufficient valve were evaluated. Two methods were used to create insufficiency. The first involved a punch and the second applied a more physiological approach of leaflet manipulation. The graft water permeability and the anastomosis leakage determined the leakage in a sufficient aortic valve state. The leakage of an insufficient aortic valve was almost completely determined by the size of the insufficiency. Graft permeability and anastomosis leakage could be neglected in the insufficient valve state. The leakage of the punched valve was comparable to the expected regurgitant volume based on the echocardiographic guidelines for that area. Leaflet manipulation resulted in less leakage than expected.

Conclusion The contribution of several variables to the leakage of sufficient and insufficient aortic valves was investigated in two in vitro experiments. The results of this study show that the contributions of each of these variables could be quantified. As a result, the leakage of an insufficient aortic valve could be quantified in a static, pressurised in vitro setting. This is the first step in obtaining the relationship between the quantitative amount of leakage and echocardiography, to improve intraoperative assessment of the repaired aortic valve after VSRR.