Flexible and Comprehensive Patient-Specific Mitral Valve Silicone Models with Chordae Tendinae Made From 3D-Printable Molds
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Abstract (arXiv)
Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally-invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients. In order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator. To our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e. the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that EcoflextextsuperscriptTM 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36h per valve. Twelve surgeons performed various surgical techniques, e.g. annuloplasty, neo-chordae implantation, triangular leaflet resection and assessed the realism of the valves very positively. The standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data...
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Apr 2019.
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Citing papers per year: 2021: 4; 2022: 1; 2023: 0; 2024: 1.
6 papers.
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- 2024Analyzing Tumors by Synthesis9 Sep 2024
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- 2021Point detection through multi-instance deep heatmap regression for sutures in endoscopy16 Nov 2021
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- 2021Heatmap-based 2D Landmark Detection with a Varying Number of Landmarks7 Jan 2021