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Proceedings Paper

Challenges and limitations of patient specific mitral valve 3D-printing
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Paper Abstract

Purpose: 3D-printing of patient-specific phantoms such as the mitral valve (MV) is challenging due to inability of current imaging systems to reconstruct fine moving features and 3D printing constraints. We investigated methods to 3D-print MV structures using ex-vivo micro-CT. Materials and Methods: A dissected porcine MV was imaged using micro-CT in diastole, using a special fixation holder. The holder design was based on a patient ECG gated cardiac CT scan using as reference points the papillary muscles and annulus. Next the micro-CT volume was segmented and 3D-printed in various elastic materials. We tested different postprocessing techniques for support material removal and surface coatings to preserve the MV integrity. To test the error a Cloud Comparison of the porcine valve-mesh file and the valve-mesh file from the patient ECG gated cardiac CT scan was performed. Results: Best results for the 3D-printed models were achieved using TangoPlus poly-jet material with a Objet Eden printer. The error computation yielded a 2.6mm deviation-distance between the two aligned valves indicating adequate alignment. The post-processing methods for support removal were challenging and required 24+ hours sample-emersion in slow agitating sodium hydroxide baths. Conclusions: The most challenging part for MV manufacturing is 3D volume acquisition and the post-printing methods during support cleaning. We developed methods to circumvent both, the imaging and the 3D-printing challenges and to ensure that the final phantom includes the fine chordae and valve geometry. Using these solutions, we were able to create complete MV structures which could benefit medical research and device testing.

Paper Details

Date Published: 2 March 2020
PDF: 12 pages
Proc. SPIE 11318, Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications, 1131805 (2 March 2020); doi: 10.1117/12.2549140
Show Author Affiliations
Jillian L. Senko, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Alexander R. Podgorsak, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Ariana B. Allman, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Ryan A. Rava, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Mohammad Mahdi Shiraz Bhurwani, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Vijay Iyer M.D., Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)
Stephen Rudin, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)
Ciprian N. Ionita, Univ. at Buffalo (United States)
Canon Stroke and Vascular Research Ctr. (United States)
Univ. at Buffalo Jacobs School of Medicine (United States)


Published in SPIE Proceedings Vol. 11318:
Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications
Po-Hao Chen; Thomas M. Deserno, Editor(s)

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