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Simulating Developmental Cardiac Morphology in Virtual Reality Using a Deformable Image Registration Approach

Overview of attention for article published in Annals of Biomedical Engineering, August 2018
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Title
Simulating Developmental Cardiac Morphology in Virtual Reality Using a Deformable Image Registration Approach
Published in
Annals of Biomedical Engineering, August 2018
DOI 10.1007/s10439-018-02113-z
Pubmed ID
Authors

Arash Abiri, Yichen Ding, Parinaz Abiri, René R. Sevag Packard, Vijay Vedula, Alison Marsden, C.-C. Jay Kuo, Tzung K. Hsiai

Abstract

While virtual reality (VR) has potential in enhancing cardiovascular diagnosis and treatment, prerequisite labor-intensive image segmentation remains an obstacle for seamlessly simulating 4-dimensional (4-D, 3-D + time) imaging data in an immersive, physiological VR environment. We applied deformable image registration (DIR) in conjunction with 3-D reconstruction and VR implementation to recapitulate developmental cardiac contractile function from light-sheet fluorescence microscopy (LSFM). This method addressed inconsistencies that would arise from independent segmentations of time-dependent data, thereby enabling the creation of a VR environment that fluently simulates cardiac morphological changes. By analyzing myocardial deformation at high spatiotemporal resolution, we interfaced quantitative computations with 4-D VR. We demonstrated that our LSFM-captured images, followed by DIR, yielded average dice similarity coefficients of 0.92 ± 0.05 (n = 510) and 0.93 ± 0.06 (n = 240) when compared to ground truth images obtained from Otsu thresholding and manual segmentation, respectively. The resulting VR environment simulates a wide-angle zoomed-in view of motion in live embryonic zebrafish hearts, in which the cardiac chambers are undergoing structural deformation throughout the cardiac cycle. Thus, this technique allows for an interactive micro-scale VR visualization of developmental cardiac morphology to enable high resolution simulation for both basic and clinical science.

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Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 16%
Student > Master 5 14%
Student > Bachelor 4 11%
Professor > Associate Professor 3 8%
Student > Ph. D. Student 2 5%
Other 4 11%
Unknown 13 35%
Readers by discipline Count As %
Engineering 4 11%
Medicine and Dentistry 3 8%
Psychology 3 8%
Nursing and Health Professions 2 5%
Business, Management and Accounting 2 5%
Other 6 16%
Unknown 17 46%