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

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

A I Kondarage, B Gayani, G Poologasundarampillai

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference|December 11, 2021

View abstract on PubMed

Summary

This study introduces a novel image processing method to automatically track changes in 3D printed scaffolds during post-processing. This technique aids in understanding and controlling scaffold deformation for improved tissue engineering applications.

Area of Science:

Biomaterials Engineering
Medical Imaging
Computational Biology

Background:

Additive manufacturing (AM) enables patient-specific tissue engineering scaffolds with complex architectures.
Post-processing techniques like sintering and freeze-drying can cause undesirable deformations in AM scaffolds.
Understanding these deformations requires analyzing changes in specific volumes of interest (VOIs) over time using in-situ 4D imaging.

Purpose of the Study:

To develop an automated image processing technique for segmenting, detecting, and tracking VOIs in 3D printed scaffolds during post-processing.
To address the current gap in automated analysis of 4D image data for scaffold deformation studies.

Main Methods:

A new image processing technique was developed to segment, detect, and track VOIs.
The method was validated using 4D synchrotron-sourced microCT data.
The data captured in-situ changes during the sintering of bioactive glass scaffolds.

Main Results:

The proposed method successfully segmented, detected, and tracked VOIs in the 3D printed scaffolds.
This automated tracking provides essential insights into scaffold deformation mechanisms during post-processing.
Validation with in-situ 4D microCT data confirmed the method's efficacy.

Conclusions:

The developed image processing technique offers a solution for automated VOI tracking in 4D scaffold imaging.
This advancement facilitates a deeper understanding of scaffold deformation, crucial for optimizing scaffold design.
The work contributes to developing controllable, patient-specific scaffolds with enhanced properties for tissue engineering.