Papaver

Progression in Image Analysis for Percutaneous Aortic Valve Replacement

Summary

Transcatheter Aortic Valve Implantation (TAVI) is a valuable alternative therapy for patients with severe aortic valve stenosis and high operative risk: It provides sustained clinical and hemodynamic benefits in selected high-risk patients declined for conventional aortic valve replacement1,2. However, the TAVI procedure is associated with potential adverse effects, such as paravalvular leakage, coronary obstruction, and conduction disorders. Yet, with the recent clinical procedural advances in catheter systems and prosthetic valves, imaging and image analysis support for TAVI is lagging behind. Imaging and image analysis are needed to reduce these adverse effects, facilitating optimized patient selection, and efficiently define image based prognostic values. Furthermore, a standardized sizing of the aortic root dimensions is lacking. As a result, only few automated tools supporting TAVI image measurements exist.

We propose to study, develop, and validate novel quantitative image analysis methods providing the clinic with quantitative numbers on risk factors to optimize patient treatment and limit adverse outcome. In specific, we will study new methods for automated sizing, quantify aortic valve calcium providing new prognostic imaging biomarkers, optimize fluoroscopy angulation, and analyzing LV dynamic parameters focusing on local dynamics in particular.

At the end of the project we expect to have (1) software prototypes for a standardized and automated sizing of the aortic root dimensions; (2) methods to determine and predict pre- and postprocedural LV dynamic parameters to that can be used for prognostic analysis; (3) quantitative measurement tooling for the amount and pre- and postprocedural distribution of valve leaflet calcifications. These tools will result in an optimization of various stages of the TAVI procedure, improved patient and treatment selection, eventually leading to improved patient outcome.

Transcatheter aortic valve implantation has proven to be a valuable therapy for high risk patients. In view of the growing cohort of aging patients with degenerative valvular disease, this novel treatment is increasingly applied. To date, over 20,000 patients have been treated worldwide with this novel therapy and promising results have been reported. We aim to come with automated and validated analysis methods to analyze the pre-, peri- and postprocedural imaging providing the clinic with novel approaches to support the TAVI procedure and generate novel prognostic image biomarkers.

We have formed a consortium with multiple industrial partners, multiple clinical departments, and three image research groups from two academic institutes. The strong involvement of the cardiology department and the cardiothoracal surgery department ensures that imaging research subjects are clinically relevant and are continuously evaluated by potential users. Our consortium includes two imaging companies: 3mensio offers the state-of-art solution for CT-based TAVI patient selection and manual sizing. Pie Medical Imaging has imaging solutions for perioperative procedures with extensive experience in bringing high-tech solutions to the interventional catheterization room. Furthermore, with its experience in cardiovascular hemodynamics, HemoLab provides the possibility to perform controlled experiments with the valve placement under high resolution imaging in ex-vivo beating heart experiments as well as validation of software prototypes using phantom models and in-vitro equipment. Within this field new

knowledge and technology will be directly utilized by HemoLab in performing contract R&D. Medtronic is one of the two manufacturers of transcatheter valve prosthesis and has a strong Dutch involvement.

During the project, we will develop multiple prototypes. These prototypes ensure industrial and clinical feedback at an early phase to evaluate the functionality. Furthermore, they allow our clinical partners to conduct research with cutting edge image analysis methods. The results of this applied clinical research will be published allowing the establishment of a new standard in the image analysis TAVI support. These prototypes will also be used to start validation studies during the course of the project. Finally, novel academic algorithms will be presented as libraries that can be integrated in current commercial products facilitating a commercial introduction. Concluding, our proposal has a large utilization potential with a continuous involvement of clinical partners, multiple industry partners, and academic applicants with proven track records in transferring high-technology methods to commercial clinical products.

Project Team