Although the geometry of the aortic valve is highly patient-specific, state-of-the-art prostheses are not capable of reproducing this individual shape. Promising results in the field of tissue engineering move the goal of the fabrication of personalized aortic valve prostheses within reach. However, there is no study on the degree of personalization that is needed, aiming at finding a trade-off between the patient's outcome and economical or logistical issues. One problem in performing such a study is the lack of a compact, unified description of the individual aortic valve shape, which is needed to perform automatic pattern analysis. In this work, we present such a description which is derived model-free and directly from experimental data. For this purpose, we set up a suitable data base of porcine aortic valve shapes. We used principal component analysis for dimensionality reduction and analyzed the minimal number of values in the representation preserving all relevant information. We could show that an accurate representation of the shape of the aortic valve leaflets is possible with no more than 39 values. This representation makes geometrical pattern analysis possible and presents an important step towards personalized cardiovascular prostheses.
|Title of host publication||2018 Computing in Cardiology Conference (CinC)|
|Publication status||Published - 09.2018|
|Event||45th Computing in Cardiology Conference - Maastricht, Netherlands|
Duration: 23.09.2018 → 26.09.2018
Conference number: 149035