Detection and Tracking Volumes of Interest in 3D Printed Tissue Engineering Scaffolds using 4D Imaging Modalities

Area of Science:

• Biomaterials Science
• Medical Imaging
• Additive Manufacturing

Background:

• Additive manufacturing (AM) enables complex scaffold architectures for tissue engineering.
• Post-processing techniques like sintering can induce structural deformations in AM scaffolds.
• Understanding these deformations is crucial for developing patient-specific scaffolds with desired properties.

Purpose of the Study:

• To develop an automated image processing technique for segmenting, detecting, and tracking volumes of interest (VOIs) in 3D printed scaffolds.
• To analyze scaffold structural changes during in-situ post-processing using 4D imaging data.
• To overcome the current limitations in automated analysis of scaffold deformations.

Main Methods:

• A novel image processing technique was developed for automated VOI segmentation and tracking.
• The method was validated using 4D synchrotron-sourced microCT data.
• In-situ imaging was performed during the sintering of bioactive glass scaffolds.

Main Results:

• The proposed method successfully segmented, detected, and tracked VOIs in 3D printed scaffolds undergoing sintering.
• The technique provides essential data for understanding the mechanisms of scaffold deformation during post-processing.
• This contributes to the development of advanced image processing tools for AM scaffold analysis.

Conclusions:

• The developed image processing technique offers a solution for automated analysis of scaffold deformations during post-processing.
• This advancement supports the creation of tissue engineering scaffolds with precise architectural control.
• The findings pave the way for improved patient-specific scaffold design and fabrication.