Automating the planning of transcatheter mitral valve interventions : multimodal and multiphasic patient assessment
Mitral valve regurgitation (MR) is one of the most commonly occurring valvular abnormalities in the western world. Even though surgical repair remains the gold standard for patients with MR, up to 50% of symptomatic patients are denied surgery, due to the presence of comorbidities. In an attempt to address this problem, percutaneous techniques, including transcatheter mitral valve implantation (TMVI), have been introduced. However, the mitral valve presents several challenges to the development of such devices, such as a highly complex and dynamic anatomy and patient heterogeneity. In this research work, automation for image segmentation and landmark prediction were investigated to improve the pre-interventional planning of these interventions. First, a 3D statistical shape model (SSM) of the left heart chambers was generated from 50 subjects to represent the anatomical variability present in a population. This 3D SSM was then expanded to the temporal dimension, to ensure that the dynamic behavior of the heart was accurately represented, resulting in three dynamic SSMs. In a second phase, the statistical shape models were used to predict anatomical landmarks necessary to select the most suitable device for the patient, as well as the optimal position and orientation of the device in the patient’s heart. The best performing SSM was selected based on a validation study comparing the automatic measurements with manual measurements by three observers. Finally, a semi-automated method was developed to segment the mitral leaflets from ultrasound images, resulting in a medial surface and a thickness model. These results represent a step forward in advancing transcatheter therapies, representing an alternative for patients currently being denied surgical treatment.
Leuven : KU Leuven, Faculty of Engineering Science , 2024
xxv, 172 p.
Supervisor: Sloten, VandeR, J. [Supervisor]
Supervisor: Bosmans, J. [Supervisor]
Supervisor: Sijbers, J. [Supervisor]
Supervisor: van Herck, P. [Supervisor]
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The publisher created published version Available from 06.03.2025
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Creation 07.03.2024
Last edited 08.03.2024
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