Aortic Valve Leaflet Shape Synthesis With Geometric Prior From Surrounding Tissue.

Jannis Hagenah,Floris Ernst,Michael Scharfschwerdt

doi:10.3389/fcvm.2022.772222

Abstract

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.

Highlights

Despite ongoing advancements of medical imaging techniques, there are structures in the human body that are difficult to visualize using typical medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US)
We present a robust approach for synthesizing aortic valve leaflet shapes with the individual aortic root shape as geometric prior based on shape encoding with autoencoders
Afterwards, the results are discussed in detail and their impact on future research is given in an outlook paragraph

Summary

Introduction

Despite ongoing advancements of medical imaging techniques, there are structures in the human body that are difficult to visualize using typical medical imaging modalities like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US). The knowledge of these structures’ shape is highly relevant for clinical decision making and intervention, e.g., for biomechanical simulations or for personalized prosthesis shaping. One example of such a structure is the aortic valve. The knowledge about the individual geometry of the aortic valve is necessary for many applications, ranging from heart modeling and simulation to the development of personalized prostheses. For both applications, the leaflet shape should be assessed in an unpressurized state to avoid stressrelated deformations. The synthesis of personalized leaflet shapes presents a promising approach for solving this problem

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Discussion

Conclusion