Quantitative Characterization of Transmural Pore Networks Predicts Endothelialization of Cardiovascular Prostheses
Presented During:
Monday, May 4, 2026: 9:00AM - 4:00PM
McCormick Place Lakeside Center
Posted Room Name:
Exhibit Hall, Poster Area
Abstract No:
P0152
Submission Type:
Abstract Submission
Authors:
Andrea Tonelli (1), Timothy Pennel (1), Jaco Theron (1), Francesco Iacoviello (2), Peter Zilla (1)
Institutions:
(1) Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town, Cape Town, South Africa, (2) Correlative X-Ray Laboratory, University College of London, London, United Kingdom
Submitting Author:
Andrea Tonelli
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Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Co-Author(s):
Timothy Pennel
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Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Jaco Theron
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Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Francesco Iacoviello
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Correlative X-Ray Laboratory, University College of London
Correlative X-Ray Laboratory, University College of London
Peter Zilla
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Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Chris Barnard Division of Cardiothoracic Surgery, University of Cape Town
Presenting Author:
Andrea Tonelli
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Groote Schuur Hospital, Observatory, Cape Town
Groote Schuur Hospital, Observatory, Cape Town
Abstract:
Objective: Endothelialization of cardiovascular conduits may be crucial for overcoming thrombogenic, haemodynamic, and degradative complications. Current paradigms emphasize transmural vessel ingrowth via pores as big as 50-80µm, yet biomaterial characterization often lacks the resolution to elucidate and quantify continuous, full thickness 3D pore architecture, leading to variable in vivo outcomes. This study establishes a novel, quantitative approach to non-destructively reconstruct conduit pore space, assess its 3D spatial porosity relevant to blood vessel permissivity, and validate findings in vivo.
Methods: Electrospun cardiovascular conduits were non-destructively scanned using micro-computed tomography (µCT) and processed with deep-learning super-resolution and segmentation to isolate the 3D pore phase. Pore network modelling (PNM) using a custom Python script reconstructed the pore space as an interconnected network, quantifying pore and throat dimensions. Scaffolds were implanted for 1 and 3 weeks in subcutaneous rodent model; explants were assessed for cell ingrowth, extracellular matrix maturation, and blood vessel density.
Results: Deep-learning-enhanced micro-computed tomography (µCT) enabled high-resolution (0.54 µm) imaging across large fields of view (2.8 mm²), achieving reconstructions exceeding 1 x 10⁸ µm³ in ≈70 minutes – a ≈57-fold increase in speed over a brute force approach, and ≈100-fold increase in volume compared to conventional methods like focused-ion beam scanning electron microscopy (FIB-SEM). Validation demonstrated improved accuracy in quantifying material metrics relative to confocal laser scanning microscopy (CLSM) and SEM. Pore network modelling (PNM) revealed median pore inscribed diameters of 5.51 µm (IQR: 5.15) for 16% polymer weight, 5.40 µm (IQR: 6.23) for 18%, and 5.40 µm (IQR: 4.22) for 20%. Thresholding revealed the highest density of interconnected, 3D ingrowth-permissive porosity within the 18% group, which translated at 3 weeks post-implantation, exhibiting collagen density comparable to controls and significantly increased vessel area versus 16% (p < 0.001) and 20% (p < 0.05).
Conclusions: Traditional characterization techniques may inadequately predict transmural blood vessel regeneration in cardiovascular conduits. This study demonstrates that µCT, deep learning, and PNM provide a reliable, efficient, and validated means to predict endothelial regeneration potential of implanted prostheses.
Methods: Electrospun cardiovascular conduits were non-destructively scanned using micro-computed tomography (µCT) and processed with deep-learning super-resolution and segmentation to isolate the 3D pore phase. Pore network modelling (PNM) using a custom Python script reconstructed the pore space as an interconnected network, quantifying pore and throat dimensions. Scaffolds were implanted for 1 and 3 weeks in subcutaneous rodent model; explants were assessed for cell ingrowth, extracellular matrix maturation, and blood vessel density.
Results: Deep-learning-enhanced micro-computed tomography (µCT) enabled high-resolution (0.54 µm) imaging across large fields of view (2.8 mm²), achieving reconstructions exceeding 1 x 10⁸ µm³ in ≈70 minutes – a ≈57-fold increase in speed over a brute force approach, and ≈100-fold increase in volume compared to conventional methods like focused-ion beam scanning electron microscopy (FIB-SEM). Validation demonstrated improved accuracy in quantifying material metrics relative to confocal laser scanning microscopy (CLSM) and SEM. Pore network modelling (PNM) revealed median pore inscribed diameters of 5.51 µm (IQR: 5.15) for 16% polymer weight, 5.40 µm (IQR: 6.23) for 18%, and 5.40 µm (IQR: 4.22) for 20%. Thresholding revealed the highest density of interconnected, 3D ingrowth-permissive porosity within the 18% group, which translated at 3 weeks post-implantation, exhibiting collagen density comparable to controls and significantly increased vessel area versus 16% (p < 0.001) and 20% (p < 0.05).
Conclusions: Traditional characterization techniques may inadequately predict transmural blood vessel regeneration in cardiovascular conduits. This study demonstrates that µCT, deep learning, and PNM provide a reliable, efficient, and validated means to predict endothelial regeneration potential of implanted prostheses.
ADULT CARDIAC:
Basic and Translational Research
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