Prediction of individual prosthesis size for valve-sparing aortic root reconstruction based on geometric features

A Systematic Review of Individualized Heart Surgery with a Personalized Prosthesis

Personalized surgery is not just a new trend but rather a patient-specific approach to therapy that makes it possible to adopt a targeted approach for a specific patient and closely mirrors the approach of personalized medicine. However, the application of tailored surgery in the context of cardiovascular replacement surgery has not been systematically reviewed. The ability to customize a device is highly dependent on the collection of radiological image data for precise prosthesis modeling. These facts are essential to "tailor-made" device design for precise prosthesis implantation. According to this study, computed tomography (CT) was the most prominent imaging modality; however, transesophageal echocardiography and echocardiography were also found to be helpful. Additionally, a dynamic finite element simulation was also found to be an attractive alternative to the finite element analysis for an in-silico experiment. Nonetheless, there is a paucity of relevant publications and only sporadic evidence. More clinical studies have been warranted, notwithstanding that the derived data and results from this insight into the use of therapeutic interventions may be evidence of multiple directives in clinical practices and beyond. This study may help the integration of personalized devices for better comprehension of predicted clinical outcomes, thus leading towards enhanced performance gains.

Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue

Even though the field of medical imaging advances, there are structures in the human body that are barely assessible with classical image acquisition modalities. One example are the three leaflets of the aortic valve due to their thin structure and high movement. However, with an increasing accuracy of biomechanical simulation, for example of the heart function, and extense computing capabilities available, concise knowledge of the individual morphology of these structures could have a high impact on personalized therapy and intervention planning as well as on clinical research. Thus, there is a high demand to estimate the individual shape of inassessible structures given only information on the geometry of the surrounding tissue. This leads to a domain adaptation problem, where the domain gap could be very large while typically only small datasets are available. Hence, classical approaches for domain adaptation are not capable of providing sufficient predictions. In this work, we present a new framework for bridging this domain gap in the scope of estimating anatomical shapes based on the surrounding tissue's morphology. Thus, we propose deep representation learning to not map from one image to another but to predict a latent shape representation. We formalize this framework and present two different approaches to solve the given problem. Furthermore, we perform a proof-of-concept study for estimating the individual shape of the aortic valve leaflets based on a volumetric ultrasound image of the aortic root. Therefore, we collect an ex-vivo porcine data set consisting of both, ultrasound volume images as well as high-resolution leaflet images, evaluate both approaches on it and perform an analysis of the model's hyperparameters. Our results show that using deep representation learning and domain mapping between the identified latent spaces, a robust prediction of the unknown leaflet shape only based on surrounding tissue information is possible, even in limited data scenarios. The concept can be applied to a wide range of modeling tasks, not only in the scope of heart modeling but also for all kinds of inassessible structures within the human body.